[CI/Build[ Don't auto-rebase PRs with CI failures (#39443)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/ci_config.yaml

@@ -1,7 +1,8 @@
 name: vllm_ci
 job_dirs:
-  - ".buildkite/test_areas"
   - ".buildkite/image_build"
+  - ".buildkite/test_areas"
+  - ".buildkite/hardware_tests"
 run_all_patterns:
   - "docker/Dockerfile"
   - "CMakeLists.txt"

.buildkite/ci_config_intel.yaml

@@ -0,0 +1,23 @@
+name: vllm_intel_ci
+job_dirs:
+  - ".buildkite/intel_jobs"
+run_all_patterns:
+  - "docker/Dockerfile"
+  - "CMakeLists.txt"
+  - "requirements/common.txt"
+  - "requirements/xpu.txt"
+  - "requirements/build.txt"
+  - "requirements/test.txt"
+  - "setup.py"
+  - "csrc/"
+  - "cmake/"
+run_all_exclude_patterns:
+  - "docker/Dockerfile."
+  - "csrc/cpu/"
+  - "csrc/rocm/"
+  - "cmake/hipify.py"
+  - "cmake/cpu_extension.cmake"
+registries: public.ecr.aws/q9t5s3a7
+repositories:
+  main: "vllm-ci-test-repo"
+  premerge: "vllm-ci-test-repo"

.buildkite/hardware_tests/amd.yaml

@@ -0,0 +1,22 @@
+group: Hardware - AMD Build 
+steps:
+  - label: "AMD: :docker: build image"
+    key: image-build-amd
+    depends_on: []
+    device: amd_cpu
+    no_plugin: true
+    commands:
+    - >
+      docker build
+      --build-arg max_jobs=16
+      --build-arg REMOTE_VLLM=1
+      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942;gfx950'
+      --build-arg VLLM_BRANCH=$BUILDKITE_COMMIT
+      --tag "rocm/vllm-ci:${BUILDKITE_COMMIT}"
+      -f docker/Dockerfile.rocm
+      --target test
+      --no-cache
+      --progress plain .
+    - docker push "rocm/vllm-ci:${BUILDKITE_COMMIT}"
+    env:
+      DOCKER_BUILDKIT: "1"

.buildkite/hardware_tests/ascend_npu.yaml

@@ -0,0 +1,10 @@
+group: Hardware
+depends_on: ~
+steps:
+  - label: "Ascend NPU Test"
+    soft_fail: true
+    timeout_in_minutes: 20
+    no_plugin: true
+    device: ascend_npu
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-npu-test.sh

.buildkite/hardware_tests/cpu.yaml

@@ -0,0 +1,110 @@
+group: CPU
+depends_on: []
+steps:
+- label: CPU-Kernel Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - csrc/cpu/
+  - cmake/cpu_extension.cmake
+  - CMakeLists.txt
+  - vllm/_custom_ops.py
+  - tests/kernels/attention/test_cpu_attn.py
+  - tests/kernels/moe/test_cpu_fused_moe.py
+  - tests/kernels/test_onednn.py
+  - tests/kernels/test_awq_int4_to_int8.py
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
+      pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+      pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
+      pytest -x -v -s tests/kernels/test_onednn.py
+      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py"
+
+- label: CPU-Compatibility Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - cmake/cpu_extension.cmake
+  - setup.py
+  - vllm/platforms/cpu.py
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"
+
+- label: CPU-Language Generation and Pooling Model Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - csrc/cpu/
+  - vllm/
+  - tests/models/language/generation/
+  - tests/models/language/pooling/
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      pytest -x -v -s tests/models/language/generation -m cpu_model
+      pytest -x -v -s tests/models/language/pooling -m cpu_model"
+
+- label: CPU-Quantization Model Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - csrc/cpu/
+  - vllm/model_executor/layers/quantization/cpu_wna16.py
+  - vllm/model_executor/layers/quantization/gptq_marlin.py
+  - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
+  - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
+  - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
+  - tests/quantization/test_compressed_tensors.py
+  - tests/quantization/test_cpu_wna16.py
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
+      pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
+      pytest -x -v -s tests/quantization/test_cpu_wna16.py"
+      
+- label: CPU-Distributed Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - csrc/cpu/shm.cpp
+  - vllm/v1/worker/cpu_worker.py
+  - vllm/v1/worker/gpu_worker.py
+  - vllm/v1/worker/cpu_model_runner.py
+  - vllm/v1/worker/gpu_model_runner.py
+  - vllm/platforms/cpu.py
+  - vllm/distributed/parallel_state.py
+  - vllm/distributed/device_communicators/cpu_communicator.py
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
+
+- label: CPU-Multi-Modal Model Tests %N
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  # - vllm/
+  - vllm/model_executor/layers/rotary_embedding
+  - tests/models/multimodal/generation/
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
+      pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
+  parallelism: 2
+
+- label: "Arm CPU Test"
+  depends_on: []
+  soft_fail: false
+  device: arm_cpu
+  no_plugin: true
+  commands: 
+  - bash .buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

.buildkite/hardware_tests/gh200.yaml

@@ -0,0 +1,10 @@
+group: Hardware
+steps:
+  - label: "GH200 Test"
+    soft_fail: true
+    device: gh200
+    no_plugin: true
+    optional: true
+    commands: 
+    - nvidia-smi 
+    - bash .buildkite/scripts/hardware_ci/run-gh200-test.sh

.buildkite/hardware_tests/intel.yaml

@@ -0,0 +1,17 @@
+group: Hardware
+depends_on: ~
+steps:
+  - label: "Intel HPU Test"
+    soft_fail: true
+    device: intel_hpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-hpu-test.sh
+
+  - label: "Intel GPU Test"
+    depends_on: []
+    soft_fail: true
+    device: intel_gpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-xpu-test.sh

.buildkite/image_build/image_build.sh

@@ -1,56 +1,255 @@
 #!/bin/bash
-set -e
+set -euo pipefail
 
-if [[ $# -lt 8 ]]; then
-  echo "Usage: $0 <registry> <repo> <commit> <branch> <vllm_use_precompiled> <vllm_merge_base_commit> <cache_from> <cache_to>"
-  exit 1
+# replace invalid characters in Docker image tags and truncate to 128 chars
+clean_docker_tag() {
+    local input="$1"
+    echo "$input" | sed 's/[^a-zA-Z0-9._-]/_/g' | cut -c1-128
+}
+
+print_usage_and_exit() {
+    echo "Usage: $0 <registry> <repo> <commit> <branch> <image_tag> [<image_tag_latest>]"
+    exit 1
+}
+
+print_instance_info() {
+    echo ""
+    echo "=== Debug: Instance Information ==="
+    # Get IMDSv2 token
+    if TOKEN=$(curl -s -X PUT "http://169.254.169.254/latest/api/token" \
+            -H "X-aws-ec2-metadata-token-ttl-seconds: 21600" 2>/dev/null); then
+        AMI_ID=$(curl -s -H "X-aws-ec2-metadata-token: $TOKEN" \
+            http://169.254.169.254/latest/meta-data/ami-id 2>/dev/null || echo "unknown")
+        INSTANCE_TYPE=$(curl -s -H "X-aws-ec2-metadata-token: $TOKEN" \
+            http://169.254.169.254/latest/meta-data/instance-type 2>/dev/null || echo "unknown")
+        INSTANCE_ID=$(curl -s -H "X-aws-ec2-metadata-token: $TOKEN" \
+            http://169.254.169.254/latest/meta-data/instance-id 2>/dev/null || echo "unknown")
+        AZ=$(curl -s -H "X-aws-ec2-metadata-token: $TOKEN" \
+            http://169.254.169.254/latest/meta-data/placement/availability-zone 2>/dev/null || echo "unknown")
+        echo "AMI ID:        ${AMI_ID}"
+        echo "Instance Type: ${INSTANCE_TYPE}"
+        echo "Instance ID:   ${INSTANCE_ID}"
+        echo "AZ:            ${AZ}"
+    else
+        echo "Not running on EC2 or IMDS not available"
+    fi
+    # Check for warm cache AMI (marker file baked into custom AMI)
+    if [[ -f /etc/vllm-ami-info ]]; then
+        echo "Cache:         warm (custom vLLM AMI)"
+        cat /etc/vllm-ami-info
+    else
+        echo "Cache:         cold (standard AMI)"
+    fi
+    echo "==================================="
+    echo ""
+}
+
+setup_buildx_builder() {
+    echo "--- :buildkite: Setting up buildx builder"
+    if [[ -S "${BUILDKIT_SOCKET}" ]]; then
+        # Custom AMI with standalone buildkitd - use remote driver for warm cache
+        echo "✅ Found local buildkitd socket at ${BUILDKIT_SOCKET}"
+        echo "Using remote driver to connect to buildkitd (warm cache available)"
+        if docker buildx inspect baked-vllm-builder >/dev/null 2>&1; then
+            echo "Using existing baked-vllm-builder"
+            docker buildx use baked-vllm-builder
+        else
+            echo "Creating baked-vllm-builder with remote driver"
+            docker buildx create \
+                --name baked-vllm-builder \
+                --driver remote \
+                --use \
+                "unix://${BUILDKIT_SOCKET}"
+        fi
+        docker buildx inspect --bootstrap
+    elif docker buildx inspect "${BUILDER_NAME}" >/dev/null 2>&1; then
+        # Existing builder available
+        echo "Using existing builder: ${BUILDER_NAME}"
+        docker buildx use "${BUILDER_NAME}"
+        docker buildx inspect --bootstrap
+    else
+        # No local buildkitd, no existing builder - create new docker-container builder
+        echo "No local buildkitd found, using docker-container driver"
+        docker buildx create --name "${BUILDER_NAME}" --driver docker-container --use
+        docker buildx inspect --bootstrap
+    fi
+
+    # builder info
+    echo "Active builder:"
+    docker buildx ls | grep -E '^\*|^NAME' || docker buildx ls
+}
+
+check_and_skip_if_image_exists() {
+    if [[ -n "${IMAGE_TAG:-}" ]]; then
+        echo "--- :mag: Checking if image exists"
+        if docker manifest inspect "${IMAGE_TAG}" >/dev/null 2>&1; then
+            echo "Image already exists: ${IMAGE_TAG}"
+            echo "Skipping build"
+            exit 0
+        fi
+        echo "Image not found, proceeding with build"
+    fi
+}
+
+ecr_login() {
+    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+}
+
+prepare_cache_tags() {
+    # resolve and set: CACHE_TO, CACHE_FROM, CACHE_FROM_BASE_BRANCH, CACHE_FROM_MAIN
+    TEST_CACHE_ECR="936637512419.dkr.ecr.us-east-1.amazonaws.com/vllm-ci-test-cache"
+    MAIN_CACHE_ECR="936637512419.dkr.ecr.us-east-1.amazonaws.com/vllm-ci-postmerge-cache"
+
+    if [[ "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
+        if [[ "$BUILDKITE_BRANCH" == "main" ]]; then
+            cache="${MAIN_CACHE_ECR}:latest"
+        else
+            clean_branch=$(clean_docker_tag "$BUILDKITE_BRANCH")
+            cache="${TEST_CACHE_ECR}:${clean_branch}"
+        fi
+        CACHE_TO="$cache"
+        CACHE_FROM="$cache"
+        CACHE_FROM_BASE_BRANCH="$cache"
+    else
+        CACHE_TO="${TEST_CACHE_ECR}:pr-${BUILDKITE_PULL_REQUEST}"
+        CACHE_FROM="${TEST_CACHE_ECR}:pr-${BUILDKITE_PULL_REQUEST}"
+        if [[ "$BUILDKITE_PULL_REQUEST_BASE_BRANCH" == "main" ]]; then
+            CACHE_FROM_BASE_BRANCH="${MAIN_CACHE_ECR}:latest"
+        else
+            clean_base=$(clean_docker_tag "$BUILDKITE_PULL_REQUEST_BASE_BRANCH")
+            CACHE_FROM_BASE_BRANCH="${TEST_CACHE_ECR}:${clean_base}"
+        fi
+    fi
+
+    CACHE_FROM_MAIN="${MAIN_CACHE_ECR}:latest"
+    export CACHE_TO CACHE_FROM CACHE_FROM_BASE_BRANCH CACHE_FROM_MAIN
+}
+
+resolve_parent_commit() {
+    if [[ -z "${PARENT_COMMIT:-}" ]]; then
+        PARENT_COMMIT=$(git rev-parse HEAD~1 2>/dev/null || echo "")
+        if [[ -n "${PARENT_COMMIT}" ]]; then
+            echo "Computed parent commit for cache fallback: ${PARENT_COMMIT}"
+            export PARENT_COMMIT
+        else
+            echo "Could not determine parent commit (may be first commit in repo)"
+        fi
+    else
+        echo "Using provided PARENT_COMMIT: ${PARENT_COMMIT}"
+    fi
+}
+
+print_bake_config() {
+    echo "--- :page_facing_up: Resolved bake configuration"
+    # Write to a temp directory to avoid polluting the repo root (which is the
+    # Docker build context). Files left in the repo root get COPY'd into the
+    # image and can cause duplicate artifact uploads from downstream steps.
+    local bake_tmp
+    bake_tmp="$(mktemp -d)"
+    BAKE_CONFIG_FILE="${bake_tmp}/bake-config-build-${BUILDKITE_BUILD_NUMBER:-local}.json"
+    docker buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --print "${TARGET}" | tee "${BAKE_CONFIG_FILE}" || true
+    echo "Saved bake config to ${BAKE_CONFIG_FILE}"
+    echo "--- :arrow_down: Uploading bake config to Buildkite"
+    (cd "$(dirname "${BAKE_CONFIG_FILE}")" && buildkite-agent artifact upload "$(basename "${BAKE_CONFIG_FILE}")")
+}
+
+#################################
+#         Main Script           #
+#################################
+print_instance_info
+
+if [[ $# -lt 5 ]]; then
+    print_usage_and_exit
 fi
 
+# input args
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
 BRANCH=$4
-VLLM_USE_PRECOMPILED=$5
-VLLM_MERGE_BASE_COMMIT=$6
-CACHE_FROM=$7
-CACHE_TO=$8
+IMAGE_TAG=$5
+IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional
 
-# authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+# build config
+TARGET="test-ci"
+VLLM_BAKE_FILE_PATH="${VLLM_BAKE_FILE_PATH:-docker/docker-bake.hcl}"
+BUILDER_NAME="${BUILDER_NAME:-vllm-builder}"
+CI_HCL_URL="${CI_HCL_URL:-https://raw.githubusercontent.com/vllm-project/ci-infra/main/docker/ci.hcl}"
+CI_HCL_PATH="/tmp/ci.hcl"
+BUILDKIT_SOCKET="/run/buildkit/buildkitd.sock"
 
-# docker buildx 
-docker buildx create --name vllm-builder --driver docker-container --use
-docker buildx inspect --bootstrap
-docker buildx ls
+prepare_cache_tags
+ecr_login
 
-# skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
+# Environment info (for docs and human readers)
+#   VLLM_CI_BRANCH      - ci-infra branch to use (default: main)
+#   VLLM_BAKE_FILE_PATH      - Path to vLLM's bake file (default: docker/docker-bake.hcl)
+#   BUILDER_NAME        - Name for buildx builder (default: vllm-builder)
+#
+# Build configuration (exported as environment variables for bake):
+export BUILDKITE_COMMIT
+export PARENT_COMMIT
+export IMAGE_TAG
+export IMAGE_TAG_LATEST
+export CACHE_FROM
+export CACHE_FROM_BASE_BRANCH
+export CACHE_FROM_MAIN
+export CACHE_TO
+
+# print args
+echo "--- :mag: Arguments"
+echo "REGISTRY: ${REGISTRY}"
+echo "REPO: ${REPO}"
+echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
+echo "BRANCH: ${BRANCH}"
+echo "IMAGE_TAG: ${IMAGE_TAG}"
+echo "IMAGE_TAG_LATEST: ${IMAGE_TAG_LATEST}"
+
+# print build configuration
+echo "--- :mag: Build configuration"
+echo "TARGET: ${TARGET}"
+echo "vLLM bake file: ${VLLM_BAKE_FILE_PATH}"
+echo "BUILDER_NAME: ${BUILDER_NAME}"
+echo "CI_HCL_URL: ${CI_HCL_URL}"
+echo "BUILDKIT_SOCKET: ${BUILDKIT_SOCKET}"
+
+echo "--- :mag: Cache tags"
+echo "CACHE_TO: ${CACHE_TO}"
+echo "CACHE_FROM: ${CACHE_FROM}"
+echo "CACHE_FROM_BASE_BRANCH: ${CACHE_FROM_BASE_BRANCH}"
+echo "CACHE_FROM_MAIN: ${CACHE_FROM_MAIN}"
+
+check_and_skip_if_image_exists
+
+echo "--- :docker: Setting up Docker buildx bake"
+echo "Target: ${TARGET}"
+echo "vLLM bake file: ${VLLM_BAKE_FILE_PATH}"
+echo "CI HCL path: ${CI_HCL_PATH}"
+
+if [[ ! -f "${VLLM_BAKE_FILE_PATH}" ]]; then
+    echo "Error: vLLM bake file not found at ${VLLM_BAKE_FILE_PATH}"
+    echo "Make sure you're running from the vLLM repository root"
+    exit 1
 fi
 
-if [[ "${VLLM_USE_PRECOMPILED:-0}" == "1" ]]; then
-  merge_base_commit_build_args="--build-arg VLLM_MERGE_BASE_COMMIT=${VLLM_MERGE_BASE_COMMIT}"
-else
-  merge_base_commit_build_args=""
+echo "--- :arrow_down: Downloading ci.hcl"
+curl -sSfL -o "${CI_HCL_PATH}" "${CI_HCL_URL}"
+echo "Downloaded to ${CI_HCL_PATH}"
+
+if [[ ! -f "${CI_HCL_PATH}" ]]; then
+    echo "Error: ci.hcl not found at ${CI_HCL_PATH}"
+    exit 1
 fi
 
-# build
-docker buildx build --file docker/Dockerfile \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --build-arg USE_SCCACHE=1 \
-  --build-arg TORCH_CUDA_ARCH_LIST="8.0 8.9 9.0 10.0" \
-  --build-arg FI_TORCH_CUDA_ARCH_LIST="8.0 8.9 9.0a 10.0a" \
-  --build-arg VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED:-0}" \
-  ${merge_base_commit_build_args} \
-  --cache-from type=registry,ref=${CACHE_FROM},mode=max \
-  --cache-to type=registry,ref=${CACHE_TO},mode=max \
-  --tag ${REGISTRY}/${REPO}:${BUILDKITE_COMMIT} \
-  $( [[ "${BRANCH}" == "main" ]] && echo "--tag ${REGISTRY}/${REPO}:latest" ) \
-  --push \
-  --target test \
-  --progress plain .
+setup_buildx_builder
+
+resolve_parent_commit
+export PARENT_COMMIT
+
+print_bake_config
+
+echo "--- :docker: Building ${TARGET}"
+docker --debug buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"
+
+echo "--- :white_check_mark: Build complete"

.buildkite/image_build/image_build.yaml

@@ -3,8 +3,9 @@ steps:
   - label: ":docker: Build image"
     key: image-build
     depends_on: []
+    timeout_in_minutes: 600
     commands:
-    - .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $VLLM_USE_PRECOMPILED $VLLM_MERGE_BASE_COMMIT $CACHE_FROM $CACHE_TO
+    - if [[ "$BUILDKITE_BRANCH" == "main" ]]; then .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG $IMAGE_TAG_LATEST; else .buildkite/image_build/image_build.sh $REGISTRY $REPO $BUILDKITE_COMMIT $BRANCH $IMAGE_TAG; fi
     retry:
       automatic:
         - exit_status: -1  # Agent was lost
@@ -40,7 +41,7 @@ steps:
           limit: 2
         - exit_status: -10  # Agent was lost
           limit: 2
-  
+
   - label: ":docker: Build CPU arm64 image"
     key: cpu-arm64-image-build
     depends_on: []

.buildkite/image_build/image_build_cpu.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -24,13 +24,11 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --build-arg VLLM_CPU_AVX512BF16=true \
-  --build-arg VLLM_CPU_AVX512VNNI=true \
-  --build-arg VLLM_CPU_AMXBF16=true \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --build-arg VLLM_CPU_X86=true \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu) ]]; then
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -24,10 +24,10 @@ fi
 # build
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu

.buildkite/image_build/image_build_hpu.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin $REGISTRY
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu) ]]; then
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -25,10 +25,10 @@ fi
 docker build \
   --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
   --build-arg max_jobs=16 \
-  --build-arg buildkite_commit=$BUILDKITE_COMMIT \
-  --tag $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
   --progress plain \
   https://github.com/vllm-project/vllm-gaudi.git
 
 # push
-docker push $REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu

.buildkite/image_build/image_build_xpu.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+set -e
+
+if [[ $# -lt 3 ]]; then
+  echo "Usage: $0 <registry> <repo> <commit>"
+  exit 1
+fi
+
+REGISTRY=$1
+REPO=$2
+BUILDKITE_COMMIT=$3
+
+# authenticate with AWS ECR
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+
+# skip build if image already exists
+if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
+  echo "Image not found, proceeding with build..."
+else
+  echo "Image found"
+  exit 0
+fi
+
+# build
+docker build \
+  --file docker/Dockerfile.xpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu

.buildkite/intel_jobs/test-intel.yaml

@@ -0,0 +1,64 @@
+group: Intel
+steps:
+  - label: ":docker: Build XPU image"
+    soft_fail: true
+    depends_on: []
+    key: image-build-xpu
+    commands:
+      - bash -lc '.buildkite/image_build/image_build_xpu.sh "public.ecr.aws/q9t5s3a7" "vllm-ci-test-repo" "$BUILDKITE_COMMIT"'
+    env:
+      DOCKER_BUILDKIT: "1"
+    retry:
+      automatic:
+        - exit_status: -1  # Agent was lost
+          limit: 2
+        - exit_status: -10  # Agent was lost
+          limit: 2
+  - label: "XPU example Test"
+    depends_on:
+      - image-build-xpu
+    timeout_in_minutes: 30
+    device: intel_gpu
+    no_plugin: true
+    env:
+      REGISTRY: "public.ecr.aws/q9t5s3a7"
+      REPO: "vllm-ci-test-repo"
+    source_file_dependencies:
+      - vllm/
+      - .buildkite/intel_jobs/test-intel.yaml 
+    commands:
+      - >-
+        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+        'pip install tblib==3.1.0 &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8 &&
+        python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192 &&
+        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 &&
+        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel'
+  - label: "XPU V1 test"
+    depends_on:
+      - image-build-xpu
+    timeout_in_minutes: 30
+    device: intel_gpu
+    no_plugin: true
+    env:
+      REGISTRY: "public.ecr.aws/q9t5s3a7"
+      REPO: "vllm-ci-test-repo"
+    source_file_dependencies:
+      - vllm/
+      - .buildkite/intel_jobs/test-intel.yaml 
+    commands:
+      - >-
+        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+        'cd tests &&
+        pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py &&
+        pytest -v -s v1/engine --ignore=v1/engine/test_output_processor.py &&
+        pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py -k "not test_topk_only and not test_topp_only and not test_topk_and_topp" &&
+        pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
+        pytest -v -s v1/structured_output &&
+        pytest -v -s v1/test_serial_utils.py &&
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py'

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -1,6 +1,9 @@
 # For hf script, without -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
 - name: "mmlu_pro"
   metrics:

.buildkite/lm-eval-harness/configs/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.yaml

@@ -0,0 +1,15 @@
+model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.695
+  - name: "exact_match,flexible-extract"
+    value: 0.447
+limit: 1319
+num_fewshot: 5
+max_model_len: 262144
+enforce_eager: false
+apply_chat_template: true
+fewshot_as_multiturn: true
+trust_remote_code: true

.buildkite/lm-eval-harness/configs/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

@@ -0,0 +1,19 @@
+model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.7142
+  - name: "exact_match,flexible-extract"
+    value: 0.4579
+env_vars:
+  VLLM_USE_FLASHINFER_MOE_FP8: "1"
+  VLLM_FLASHINFER_MOE_BACKEND: "throughput"
+limit: 1319
+num_fewshot: 5
+max_model_len: 262144
+kv_cache_dtype: fp8
+enforce_eager: false
+apply_chat_template: true
+fewshot_as_multiturn: true
+trust_remote_code: true

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -1,6 +1,9 @@
 # For vllm script, with -t option (tensor parallel size)
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
 model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
 - name: "gsm8k"
   metrics:

.buildkite/lm-eval-harness/configs/Qwen3-235B-A22B-Instruct-2507-FP8.yaml

@@ -1,4 +1,7 @@
 model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
   - name: "mmlu_pro"
     metrics:

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -1,12 +0,0 @@
-# For vllm script, with -t option (tensor parallel size).
-# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
-model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.6353
-  - name: "exact_match,flexible-extract"
-    value: 0.637
-limit: null
-num_fewshot: null 

.buildkite/lm-eval-harness/configs/models-large-hopper.txt

@@ -1 +1,2 @@
 Qwen3-235B-A22B-Instruct-2507-FP8.yaml
+NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large-rocm-fp8.txt

@@ -0,0 +1 @@
+Qwen3-235B-A22B-Instruct-2507-FP8.yaml

.buildkite/lm-eval-harness/configs/models-large.txt

@@ -3,3 +3,4 @@ Meta-Llama-3-70B-Instruct.yaml
 Mixtral-8x7B-Instruct-v0.1.yaml
 Qwen2-57B-A14-Instruct.yaml
 DeepSeek-V2-Lite-Chat.yaml
+NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.yaml

.buildkite/lm-eval-harness/configs/models-small-rocm.txt

@@ -1,5 +1,6 @@
 Qwen2.5-1.5B-Instruct.yaml
 Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
 Qwen1.5-MoE-W4A16-compressed-tensors.yaml

.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh

@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on chartqa for vllm.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install "lm-eval[api]>=0.4.11"
 
 usage() {
     echo``
@@ -41,4 +41,4 @@ lm_eval --model vllm-vlm \
   --tasks chartqa \
   --batch_size auto \
   --apply_chat_template \
-  --limit $LIMIT
+  --limit "$LIMIT"

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install "lm-eval[api]>=0.4.11"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install "lm-eval[api]>=0.4.11"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh

@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.9.2"
+#   pip install "lm-eval[api]>=0.4.11"
 
 usage() {
     echo``
@@ -20,14 +20,11 @@ usage() {
     echo
 }
 
-while getopts "m:b:l:f:t:" OPT; do
+while getopts "m:l:f:t:" OPT; do
   case ${OPT} in
     m )
         MODEL="$OPTARG"
         ;;
-    b )
-        BATCH_SIZE="$OPTARG"
-        ;;
     l )
         LIMIT="$OPTARG"
         ;;

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -13,9 +13,11 @@ import os
 from contextlib import contextmanager
 
 import lm_eval
-import numpy as np
+import pytest
 import yaml
 
+from vllm.platforms import current_platform
+
 DEFAULT_RTOL = 0.08
 
 
@@ -63,6 +65,9 @@ def launch_lm_eval(eval_config, tp_size):
         "allow_deprecated_quantization=True,"
     )
 
+    if current_platform.is_rocm() and "Nemotron-3" in eval_config["model_name"]:
+        model_args += "attention_backend=TRITON_ATTN"
+
     env_vars = eval_config.get("env_vars", None)
     with scoped_env_vars(env_vars):
         results = lm_eval.simple_evaluate(
@@ -85,9 +90,40 @@ def launch_lm_eval(eval_config, tp_size):
     return results
 
 
+def _check_rocm_gpu_arch_requirement(eval_config):
+    """Skip the test if the model requires a ROCm GPU arch not present.
+
+    Model YAML configs can specify::
+
+        required_gpu_arch:
+          - gfx942
+          - gfx950
+
+    The check only applies on ROCm.  On other platforms (e.g. CUDA) the
+    field is ignored so that shared config files work for both NVIDIA and
+    AMD CI pipelines.
+    """
+    required_archs = eval_config.get("required_gpu_arch")
+    if not required_archs:
+        return
+
+    if not current_platform.is_rocm():
+        return
+
+    from vllm.platforms.rocm import _GCN_ARCH  # noqa: E402
+
+    if not any(arch in _GCN_ARCH for arch in required_archs):
+        pytest.skip(
+            f"Model requires GPU arch {required_archs}, "
+            f"but detected arch is '{_GCN_ARCH}'"
+        )
+
+
 def test_lm_eval_correctness_param(config_filename, tp_size):
     eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
 
+    _check_rocm_gpu_arch_requirement(eval_config)
+
     results = launch_lm_eval(eval_config, tp_size)
 
     rtol = eval_config.get("rtol", DEFAULT_RTOL)
@@ -102,6 +138,8 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
                 f"ground_truth={ground_truth:.3f} | "
                 f"measured={measured_value:.3f} | rtol={rtol}"
             )
-            success = success and np.isclose(ground_truth, measured_value, rtol=rtol)
+
+            min_acceptable = ground_truth * (1 - rtol)
+            success = success and measured_value >= min_acceptable
 
     assert success

.buildkite/performance-benchmarks/README.md

@@ -83,7 +83,6 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3-8B",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },

.buildkite/performance-benchmarks/scripts/compare-json-results.py

@@ -7,8 +7,10 @@ import argparse
 import html as _html
 import json
 import os
+from contextlib import nullcontext
 from dataclasses import dataclass
 from importlib import util
+from pathlib import Path
 
 import pandas as pd
 
@@ -31,6 +33,45 @@ pd.set_option("display.precision", 2)
 pd.set_option("display.float_format", lambda x: f"{x:.2f}")
 
 
+# -----------------------------
+# Concurrency normalization (NEW, small)
+# -----------------------------
+def _find_concurrency_col(df: pd.DataFrame) -> str:
+    for c in [
+        "# of max concurrency.",
+        "# of max concurrency",
+        "Max Concurrency",
+        "max_concurrency",
+        "Concurrency",
+    ]:
+        if c in df.columns:
+            return c
+
+    for c in df.columns:
+        if "concurr" in str(c).lower():
+            s = df[c]
+            if s.dtype.kind in "iu" and s.nunique() > 1 and s.min() >= 1:
+                return c
+
+    raise ValueError(
+        "Cannot infer concurrency column. "
+        "Please rename the column to one of the known names "
+        "or add an explicit override (e.g., --concurrency-col)."
+    )
+
+
+def _normalize_concurrency_in_df(
+    df: pd.DataFrame, canonical: str = "# of max concurrency."
+) -> pd.DataFrame:
+    if canonical in df.columns:
+        return df
+    detected = _find_concurrency_col(df)
+    if detected in df.columns and detected != canonical:
+        return df.rename(columns={detected: canonical})
+    df[canonical] = pd.NA
+    return df
+
+
 # -----------------------------
 # Core data compare
 # -----------------------------
@@ -50,19 +91,25 @@ def compare_data_columns(
     - Concat along axis=1 (indexes align), then reset_index so callers can
       group by columns.
     - If --debug, add a <file_label>_name column per file.
+
+    Minimal fix to support different max_concurrency lists across files:
+      - normalize concurrency column naming to "# of max concurrency."
+      - align on UNION of keys (missing points become NaN)
+      - BUGFIX: don't drop throughput rows based on P99/Median presence
     """
     print("\ncompare_data_column:", data_column)
 
     frames = []
     raw_data_cols: list[str] = []
-    compare_frames = []
 
+    # Determine key cols after normalizing concurrency
     cols_per_file: list[set] = []
     for f in files:
         try:
             df_tmp = pd.read_json(f, orient="records")
         except Exception as err:
             raise ValueError(f"Failed to read {f}") from err
+        df_tmp = _normalize_concurrency_in_df(df_tmp, canonical="# of max concurrency.")
         cols_per_file.append(set(df_tmp.columns))
 
     key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
@@ -73,12 +120,25 @@ def compare_data_columns(
             "No common key columns found from info_cols across the input files."
         )
 
-    meta_added = False
+    union_index = None
+    metas: list[pd.DataFrame] = []
+    staged: list[tuple[str, pd.Series, pd.Series | None]] = []
 
     for file in files:
         df = pd.read_json(file, orient="records")
+        df = _normalize_concurrency_in_df(df, canonical="# of max concurrency.")
 
-        if drop_column in df.columns:
+        # BUGFIX: only drop rows for latency-like metrics; throughput rows may have
+        # NaN in P99/Median columns even if the column exists in the JSON.
+        metric_lc = str(data_column).lower()
+        is_latency_metric = (
+            "ttft" in metric_lc
+            or "tpot" in metric_lc
+            or "p99" in metric_lc
+            or "median" in metric_lc
+            or metric_lc.strip() in {"p99", "median"}
+        )
+        if is_latency_metric and drop_column in df.columns:
             df = df.dropna(subset=[drop_column], ignore_index=True)
 
         for c in (
@@ -103,35 +163,61 @@ def compare_data_columns(
             meta = meta.groupby(level=key_cols, dropna=False).first()
 
         file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
-        s = df_idx[data_column]
-        if not s.index.is_unique:
-            s = s.groupby(level=key_cols, dropna=False).mean()
+
+        if data_column in df_idx.columns:
+            s = df_idx[data_column]
+            if not s.index.is_unique:
+                s = s.groupby(level=key_cols, dropna=False).mean()
+        else:
+            # keep NA series to preserve meta keys for union_index
+            s = pd.Series(pd.NA, index=meta.index)
         s.name = file_label
 
-        if not meta_added:
-            frames.append(meta)
-            meta_added = True
-
+        name_s = None
         if debug and name_column in df_idx.columns:
             name_s = df_idx[name_column]
             if not name_s.index.is_unique:
                 name_s = name_s.groupby(level=key_cols, dropna=False).first()
             name_s.name = f"{file_label}_name"
-            frames.append(name_s)
 
-        frames.append(s)
+        if union_index is None:
+            union_index = meta.index
+        else:
+            union_index = union_index.union(meta.index)
+        metas.append(meta)
+
+        staged.append((file_label, s, name_s))
+
+    if union_index is None:
+        raise ValueError("No data found after loading inputs.")
+
+    # meta first (union-aligned): build UNION meta across all files
+    if metas:
+        meta_union = pd.concat(metas, axis=0)
+        # Collapse duplicates on the MultiIndex; keep first non-null per column
+        meta_union = meta_union.groupby(level=key_cols, dropna=False).first()
+        frames.append(meta_union.reindex(union_index))
+
+    # values + ratios (union-aligned)
+    metric_series_aligned: list[pd.Series] = []
+    for file_label, s, name_s in staged:
+        s_aligned = s.reindex(union_index)
+        frames.append(s_aligned)
         raw_data_cols.append(file_label)
-        compare_frames.append(s)
+        metric_series_aligned.append(s_aligned)
 
-        if len(compare_frames) >= 2:
-            base = compare_frames[0]
-            current = compare_frames[-1]
-            if "P99" in data_column or "Median" in data_column:
+        if debug and name_s is not None:
+            frames.append(name_s.reindex(union_index))
+
+        if len(metric_series_aligned) >= 2:
+            base = metric_series_aligned[0]
+            current = metric_series_aligned[-1]
+            if "P99" in str(data_column) or "Median" in str(data_column):
                 ratio = base / current
             else:
                 ratio = current / base
             ratio = ratio.mask(base == 0)
-            ratio.name = f"Ratio 1 vs {len(compare_frames)}"
+            ratio.name = f"Ratio 1 vs {len(metric_series_aligned)}"
             frames.append(ratio)
 
     concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
@@ -202,24 +288,10 @@ def split_json_by_tp_pp(
 # -----------------------------
 # Styling helpers
 # -----------------------------
-def _find_concurrency_col(df: pd.DataFrame) -> str:
-    for c in [
-        "# of max concurrency.",
-        "# of max concurrency",
-        "Max Concurrency",
-        "max_concurrency",
-        "Concurrency",
-    ]:
-        if c in df.columns:
-            return c
-    for c in df.columns:
-        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
-            return c
-    return "# of max concurrency."
-
-
 def _highlight_threshold(
-    df: pd.DataFrame, threshold: float
+    df: pd.DataFrame,
+    threshold: float,
+    slack_pct: float = 0.0,
 ) -> pd.io.formats.style.Styler:
     conc_col = _find_concurrency_col(df)
     key_cols = [
@@ -232,12 +304,24 @@ def _highlight_threshold(
     ]
     conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
 
-    return df.style.map(
-        lambda v: "background-color:#e6ffe6;font-weight:bold;"
-        if pd.notna(v) and v <= threshold
-        else "",
-        subset=conf_cols,
-    )
+    try:
+        slack_pct = float(slack_pct or 0.0)
+    except Exception:
+        slack_pct = 0.0
+    slack_limit = threshold * (1.0 + slack_pct / 100.0)
+
+    def _cell(v):
+        if pd.isna(v):
+            return ""
+        if v <= threshold:
+            # Strict SLA
+            return "background-color:#e6ffe6;font-weight:bold;"
+        if v <= slack_limit:
+            # Within slack range
+            return "background-color:#ffe5cc;font-weight:bold;"
+        return ""
+
+    return df.style.map(_cell, subset=conf_cols)
 
 
 def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
@@ -275,6 +359,177 @@ def _apply_two_decimals(
     return styler.format({c: "{:.2f}" for c in num_cols}, na_rep="")
 
 
+# -----------------------------
+# Export helpers (Excel + CSV)
+# -----------------------------
+def _sanitize_sheet_name(name: str) -> str:
+    """
+    Excel sheet constraints:
+      - max 31 chars
+      - cannot contain: : \ / ? * [ ]
+      - cannot be empty
+
+    NOTE: Use fast, non-regex operations here to avoid the third-party `regex`
+    module's compile overhead/edge-cases on some systems.
+    """
+    name = "sheet" if name is None else str(name)
+
+    # Replace illegal characters with underscore.
+    trans = str.maketrans(
+        {
+            ":": "_",
+            "\\": "_",
+            "/": "_",
+            "?": "_",
+            "*": "_",
+            "[": "_",
+            "]": "_",
+        }
+    )
+    name = name.translate(trans)
+
+    # Strip quotes/spaces and collapse whitespace.
+    name = name.strip().strip("'")
+    name = " ".join(name.split())
+
+    if not name:
+        name = "sheet"
+    return name[:31]
+
+
+def _group_to_sheet_base(group_cols: list[str], gkey_tuple) -> str:
+    d = dict(zip(group_cols, gkey_tuple))
+
+    # Always keep input/output lengths (these are important).
+    ilen = d.get("Input Len", "")
+    olen = d.get("Output Len", "")
+    lens = f"_{ilen}x{olen}" if ilen != "" and olen != "" else ""
+
+    # Shorten model name aggressively to make room for lens.
+    model = d.get("Model", "model")
+    leaf = str(model).split("/")[-1]
+
+    max_model_len = max(1, 31 - len(lens))
+    model_short = leaf[:max_model_len]
+
+    return _sanitize_sheet_name(f"{model_short}{lens}")
+
+
+def _write_tables_to_excel_sheet(
+    writer: pd.ExcelWriter, sheet: str, blocks: list[tuple[str, pd.DataFrame]]
+):
+    """Write all blocks to a sheet with a single to_excel() call.
+
+    Pandas+openpyxl can be extremely slow when called many times per sheet.
+    We flatten blocks into one table with a 'Section' column to keep structure
+    while making Excel generation fast and deterministic.
+    """
+    if not blocks:
+        pd.DataFrame().to_excel(writer, sheet_name=sheet, index=False)
+        return
+
+    combined_parts: list[pd.DataFrame] = []
+    for title, df in blocks:
+        df2 = df.copy()
+        # Put the section label as the first column for readability.
+        df2.insert(0, "Section", title)
+        combined_parts.append(df2)
+
+    combined = pd.concat(combined_parts, axis=0, ignore_index=True, sort=False)
+    combined.to_excel(writer, sheet_name=sheet, index=False)
+
+
+def _safe_filename(s: str) -> str:
+    # Fast path without the third-party `regex` module.
+    s = " ".join(str(s).strip().split())
+    allowed = []
+    for ch in s:
+        if ch.isalnum() or ch in "._-":
+            allowed.append(ch)
+        else:
+            allowed.append("_")
+    out = "".join(allowed)
+    return out[:180] if len(out) > 180 else out
+
+
+# -----------------------------
+# vLLM environment export helper
+# -----------------------------
+def _parse_vllm_env_txt(env_path: Path) -> pd.DataFrame:
+    """Parse vllm_env.txt into a flat table (Section, Key, Value).
+
+    Supports:
+      - section headers as standalone lines (no ':' or '=')
+      - key-value lines like 'OS: Ubuntu ...'
+      - env var lines like 'HF_HOME=/data/hf'
+    """
+    lines = env_path.read_text(encoding="utf-8", errors="replace").splitlines()
+    section = "General"
+    rows: list[dict] = []
+
+    def set_section(s: str):
+        nonlocal section
+        s = (s or "").strip()
+        if s:
+            section = s
+
+    for raw in lines:
+        stripped = raw.strip()
+        if not stripped:
+            continue
+        # divider lines like =====
+        if set(stripped) <= {"="}:
+            continue
+
+        # section header heuristic: short standalone line
+        if ":" not in stripped and "=" not in stripped and len(stripped) <= 64:
+            if stripped.lower().startswith("collecting environment information"):
+                continue
+            set_section(stripped)
+            continue
+
+        # env var style: KEY=VALUE (and not a URL with :)
+        if "=" in stripped and ":" not in stripped:
+            k, v = stripped.split("=", 1)
+            k = k.strip()
+            v = v.strip()
+            if k:
+                rows.append({"Section": section, "Key": k, "Value": v})
+            continue
+
+        # key: value
+        if ":" in stripped:
+            k, v = stripped.split(":", 1)
+            k = k.strip()
+            v = v.strip()
+            if k:
+                rows.append({"Section": section, "Key": k, "Value": v})
+            continue
+
+    return pd.DataFrame(rows, columns=["Section", "Key", "Value"])
+
+
+def _load_env_df_for_inputs(args, files: list[str]) -> pd.DataFrame | None:
+    """Load vllm_env.txt next to the *original* input JSON file.
+
+    Note: when only one -f is provided, the script may split JSON into ./splits/...,
+    but vllm_env.txt typically lives next to the original benchmark_results.json.
+    """
+    base_dir: Path | None = None
+    if getattr(args, "file", None):
+        base_dir = Path(args.file[0]).resolve().parent
+    elif files:
+        base_dir = Path(files[0]).resolve().parent
+    if base_dir is None:
+        return None
+
+    env_path = base_dir / "vllm_env.txt"
+    if not env_path.exists():
+        return None
+    df = _parse_vllm_env_txt(env_path)
+    return df
+
+
 # -----------------------------
 # Valid max concurrency summary helpers
 # -----------------------------
@@ -301,7 +556,11 @@ def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
 
 
 def _max_concurrency_ok(
-    df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
+    df: pd.DataFrame,
+    conc_col: str,
+    cfg_col: str,
+    threshold: float,
+    slack_pct: float = 0.0,
 ):
     if df is None or conc_col not in df.columns or cfg_col not in df.columns:
         return pd.NA
@@ -314,7 +573,14 @@ def _max_concurrency_ok(
     if d.empty:
         return pd.NA
 
-    ok = d[d[cfg_col] <= threshold]
+    # Accept values up to (1 + slack_pct%) above the SLA.
+    try:
+        slack_pct = float(slack_pct or 0.0)
+    except Exception:
+        slack_pct = 0.0
+    effective_limit = float(threshold) * (1.0 + slack_pct / 100.0)
+
+    ok = d[d[cfg_col] <= effective_limit]
     if ok.empty:
         return pd.NA
 
@@ -380,15 +646,25 @@ def build_valid_max_concurrency_summary_html(
     if not cfg_cols:
         cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
 
+    # Display SLA ranges in the table header (SLA .. SLA*(1+slack))
+    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
+    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
+    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
+    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
+
     rows = []
     for cfg in cfg_cols:
         ttft_max = (
-            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
+            _max_concurrency_ok(
+                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
+            )
             if ttft_group_df is not None
             else pd.NA
         )
         tpot_max = (
-            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
+            _max_concurrency_ok(
+                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
+            )
             if tpot_group_df is not None
             else pd.NA
         )
@@ -417,8 +693,8 @@ def build_valid_max_concurrency_summary_html(
         rows.append(
             {
                 "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
+                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
                 f"Max {conc_col} (Both)": both,
                 "Output Tput @ Both (tok/s)": tput_at_both,
                 "TTFT @ Both (ms)": ttft_at_both,
@@ -428,7 +704,6 @@ def build_valid_max_concurrency_summary_html(
 
     summary_df = pd.DataFrame(rows)
 
-    # --- Coerce numeric columns so Styler doesn't miss them due to object dtype ---
     for c in summary_df.columns:
         if c == "Configuration":
             continue
@@ -436,12 +711,10 @@ def build_valid_max_concurrency_summary_html(
 
     both_col = f"Max {conc_col} (Both)"
 
-    # --- Strict 2-decimal formatting for ALL non-Configuration columns ---
     formatters = {}
     for c in summary_df.columns:
         if c == "Configuration":
             continue
-        # default argument binds per-column formatter correctly
         formatters[c] = lambda v: "" if pd.isna(v) else f"{float(v):.2f}"
 
     styler = summary_df.style.format(formatters)
@@ -460,6 +733,104 @@ def build_valid_max_concurrency_summary_html(
     return title + styler.to_html(table_attributes='border="1" class="dataframe"')
 
 
+def build_valid_max_concurrency_summary_df(
+    tput_group_df: pd.DataFrame | None,
+    ttft_group_df: pd.DataFrame | None,
+    tpot_group_df: pd.DataFrame | None,
+    conc_col: str,
+    args,
+) -> pd.DataFrame | None:
+    if ttft_group_df is None and tpot_group_df is None:
+        return None
+
+    ttft_cols = (
+        _config_value_columns(ttft_group_df, conc_col)
+        if ttft_group_df is not None
+        else []
+    )
+    tpot_cols = (
+        _config_value_columns(tpot_group_df, conc_col)
+        if tpot_group_df is not None
+        else []
+    )
+    tput_cols = (
+        _config_value_columns(tput_group_df, conc_col)
+        if tput_group_df is not None
+        else []
+    )
+
+    if ttft_group_df is not None and tpot_group_df is not None:
+        cfg_cols = [c for c in ttft_cols if c in tpot_cols]
+        if tput_group_df is not None:
+            cfg_cols = [c for c in cfg_cols if c in tput_cols] or cfg_cols
+    else:
+        cfg_cols = ttft_cols or tpot_cols
+
+    if not cfg_cols:
+        cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
+
+    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
+    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
+    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
+    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
+
+    rows = []
+    for cfg in cfg_cols:
+        ttft_max = (
+            _max_concurrency_ok(
+                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
+            )
+            if ttft_group_df is not None
+            else pd.NA
+        )
+        tpot_max = (
+            _max_concurrency_ok(
+                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
+            )
+            if tpot_group_df is not None
+            else pd.NA
+        )
+        both = (
+            pd.NA
+            if (pd.isna(ttft_max) or pd.isna(tpot_max))
+            else min(ttft_max, tpot_max)
+        )
+
+        tput_at_both = (
+            _value_at_concurrency(tput_group_df, conc_col, cfg, both)
+            if tput_group_df is not None
+            else pd.NA
+        )
+        ttft_at_both = (
+            _value_at_concurrency(ttft_group_df, conc_col, cfg, both)
+            if ttft_group_df is not None
+            else pd.NA
+        )
+        tpot_at_both = (
+            _value_at_concurrency(tpot_group_df, conc_col, cfg, both)
+            if tpot_group_df is not None
+            else pd.NA
+        )
+
+        rows.append(
+            {
+                "Configuration": cfg,
+                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
+                f"Max {conc_col} (Both)": both,
+                "Output Tput @ Both (tok/s)": tput_at_both,
+                "TTFT @ Both (ms)": ttft_at_both,
+                "TPOT @ Both (ms)": tpot_at_both,
+            }
+        )
+
+    df = pd.DataFrame(rows)
+    for c in df.columns:
+        if c != "Configuration":
+            df[c] = pd.to_numeric(df[c], errors="coerce")
+    return df
+
+
 # -----------------------------
 # Plot helper
 # -----------------------------
@@ -537,6 +908,35 @@ def build_parser() -> argparse.ArgumentParser:
         default=100.0,
         help="Reference limit for TPOT plots (ms)",
     )
+
+    # ---- SLA tolerance (slack) options ----
+    parser.add_argument(
+        "--ttft-slack-pct",
+        type=float,
+        default=5.0,
+        help="Allowed percentage above TTFT SLA (default: 5).",
+    )
+    parser.add_argument(
+        "--tpot-slack-pct",
+        type=float,
+        default=5.0,
+        help="Allowed percentage above TPOT SLA (default: 5).",
+    )
+
+    # ---- export options ----
+    parser.add_argument(
+        "--excel-out",
+        type=str,
+        default="perf_comparison.xlsx",
+        help="Write one sheet per (Model, Dataset, Input Len, Output Len).",
+    )
+    parser.add_argument(
+        "--csv-out-dir",
+        type=str,
+        default="",
+        help="If set, write per-group per-metric CSVs into this directory.",
+    )
+
     return parser
 
 
@@ -615,9 +1015,13 @@ def render_metric_table_html(
 
     metric_name = metric_label.lower()
     if "ttft" in metric_name:
-        styler = _highlight_threshold(display_group, args.ttft_max_ms)
+        styler = _highlight_threshold(
+            display_group, args.ttft_max_ms, args.ttft_slack_pct
+        )
     elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
-        styler = _highlight_threshold(display_group, args.tpot_max_ms)
+        styler = _highlight_threshold(
+            display_group, args.tpot_max_ms, args.tpot_slack_pct
+        )
     else:
         styler = display_group.style
 
@@ -657,7 +1061,6 @@ def maybe_write_plot(
         markers=True,
     )
 
-    # Ensure plot hover + y tick labels are also 2 decimals.
     fig.update_traces(hovertemplate="%{y:.2f}<extra></extra>")
     fig.update_yaxes(tickformat=".2f")
 
@@ -730,87 +1133,186 @@ def write_report_group_first(
         for metric_label, (df, _) in metric_cache.items()
     }
 
-    with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
-        main_fh.write('<meta charset="utf-8">\n')
-        for gkey in group_keys:
-            gkey_tuple = normalize_group_key(gkey)
-            suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
-            sub_path = group_filename(gkey_tuple)
-            group_header = (
-                '<div style="font-size: 1.4em; font-weight: 700; '
-                'margin: 18px 0 10px 0;">'
-                f"{_html.escape(suffix)}"
-                "</div>\n"
-            )
+    csv_dir = Path(args.csv_out_dir) if args.csv_out_dir else None
+    if csv_dir:
+        csv_dir.mkdir(parents=True, exist_ok=True)
 
-            main_fh.write(group_header)
-            with open(sub_path, "w", encoding="utf-8") as sub_fh:
-                sub_fh.write('<meta charset="utf-8">\n')
-                sub_fh.write(group_header)
-                tput_group_df = None
-                ttft_group_df = None
-                tpot_group_df = None
-                conc_col = args.xaxis
+    excel_path = args.excel_out or "perf_comparison.xlsx"
+    disable_excel = os.getenv("VLLM_COMPARE_DISABLE_EXCEL", "0") == "1"
 
-                for metric_label in plan.data_cols:
-                    gb = metric_groupbys[metric_label]
-                    df_sorted, raw_data_cols = metric_cache[metric_label]
+    # Prefer xlsxwriter for speed; fallback to openpyxl if unavailable.
+    excel_engine = (
+        os.getenv("VLLM_COMPARE_EXCEL_ENGINE", "xlsxwriter").strip() or "xlsxwriter"
+    )
+    if excel_engine == "xlsxwriter" and util.find_spec("xlsxwriter") is None:
+        excel_engine = "openpyxl"
 
-                    try:
-                        group_df = gb.get_group(gkey)
-                    except KeyError:
-                        missing = (
-                            '<div style="font-size: 1.1em; font-weight: 600; '
-                            'margin: 10px 0;">'
-                            f"{_html.escape(metric_label)} — missing for this group"
-                            "</div>\n"
+    excel_engine_kwargs = {}
+    if excel_engine == "xlsxwriter":
+        # Reduce memory pressure & usually faster writes.
+        excel_engine_kwargs = {"options": {"constant_memory": True}}
+
+    xw_ctx = (
+        nullcontext(None)
+        if disable_excel
+        else pd.ExcelWriter(
+            excel_path, engine=excel_engine, engine_kwargs=excel_engine_kwargs
+        )
+    )
+    with xw_ctx as xw:
+        used_sheets: set[str] = set()
+        # ---- Environment sheet (first) ----
+        env_sheet = _sanitize_sheet_name("Environment")
+        env_df = _load_env_df_for_inputs(args, files)
+        if xw is not None:
+            if env_df is None or env_df.empty:
+                pd.DataFrame(
+                    [
+                        {
+                            "Section": "Environment",
+                            "Key": "vllm_env.txt",
+                            "Value": "NOT FOUND (or empty)",
+                        }
+                    ]
+                ).to_excel(xw, sheet_name=env_sheet, index=False)
+            else:
+                env_df.to_excel(xw, sheet_name=env_sheet, index=False)
+            used_sheets.add(env_sheet)
+        with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
+            main_fh.write('<meta charset="utf-8">\n')
+            for gkey in group_keys:
+                gkey_tuple = normalize_group_key(gkey)
+                suffix = build_group_suffix(group_cols_canonical, gkey_tuple)
+                sub_path = group_filename(gkey_tuple)
+                group_header = (
+                    '<div style="font-size: 1.4em; font-weight: 700; '
+                    'margin: 18px 0 10px 0;">'
+                    f"{_html.escape(suffix)}"
+                    "</div>\n"
+                )
+
+                main_fh.write(group_header)
+
+                do_excel = xw is not None
+                sheet = _group_to_sheet_base(group_cols_canonical, gkey_tuple)
+                sheet_base = sheet
+                if do_excel:
+                    dedup_i = 1
+                    while sheet in used_sheets:
+                        dedup_i += 1
+                        suffix = f"_{dedup_i}"
+                        # Ensure uniqueness even when sheet names are truncated.
+                        base = str(sheet_base)
+                        keep = max(1, 31 - len(suffix))
+                        sheet = _sanitize_sheet_name(base[:keep] + suffix)
+                    used_sheets.add(sheet)
+
+                excel_blocks: list[tuple[str, pd.DataFrame]] = []
+
+                with open(sub_path, "w", encoding="utf-8") as sub_fh:
+                    sub_fh.write('<meta charset="utf-8">\n')
+                    sub_fh.write(group_header)
+                    tput_group_df = None
+                    ttft_group_df = None
+                    tpot_group_df = None
+                    conc_col = args.xaxis
+
+                    for metric_label in plan.data_cols:
+                        gb = metric_groupbys[metric_label]
+                        df_sorted, raw_data_cols = metric_cache[metric_label]
+
+                        try:
+                            group_df = gb.get_group(gkey)
+                        except KeyError:
+                            missing = (
+                                '<div style="font-size: 1.1em; font-weight: 600; '
+                                'margin: 10px 0;">'
+                                f"{_html.escape(metric_label)} — missing for this group"
+                                "</div>\n"
+                            )
+                            main_fh.write(missing)
+                            sub_fh.write(missing)
+                            continue
+
+                        if conc_col not in group_df.columns:
+                            conc_col = _find_concurrency_col(group_df)
+
+                        mn = metric_label.lower().strip()
+                        if "tok/s" in mn:
+                            tput_group_df = group_df
+                        elif "ttft" in mn:
+                            ttft_group_df = group_df
+                        elif mn in ("p99", "median") or "tpot" in mn:
+                            tpot_group_df = group_df
+
+                        display_group = group_df.drop(
+                            columns=group_cols_canonical, errors="ignore"
                         )
 
-                        main_fh.write(missing)
-                        sub_fh.write(missing)
-                        continue
+                        html = render_metric_table_html(
+                            display_group, metric_label, suffix, args
+                        )
+                        main_fh.write(html)
+                        sub_fh.write(html)
 
-                    if conc_col not in group_df.columns:
-                        conc_col = _find_concurrency_col(group_df)
+                        maybe_write_plot(
+                            main_fh,
+                            sub_fh,
+                            group_df=group_df,
+                            raw_data_cols=raw_data_cols,
+                            metric_label=metric_label,
+                            y_axis_col=y_axis_col,
+                            args=args,
+                        )
 
-                    mn = metric_label.lower().strip()
-                    if "tok/s" in mn:
-                        tput_group_df = group_df
-                    elif "ttft" in mn:
-                        ttft_group_df = group_df
-                    elif mn in ("p99", "median") or "tpot" in mn:
-                        tpot_group_df = group_df
+                        excel_blocks.append(
+                            (metric_label, group_df.reset_index(drop=True))
+                        )
+                        if csv_dir:
+                            fn = _safe_filename(
+                                f"{sheet}__{metric_label}".replace(" ", "_").replace(
+                                    "/", "_"
+                                )
+                            )
+                            group_df.to_csv(csv_dir / f"{fn}.csv", index=False)
 
-                    display_group = group_df.drop(
-                        columns=group_cols_canonical, errors="ignore"
-                    )
-
-                    html = render_metric_table_html(
-                        display_group, metric_label, suffix, args
-                    )
-                    main_fh.write(html)
-                    sub_fh.write(html)
-
-                    maybe_write_plot(
-                        main_fh,
-                        sub_fh,
-                        group_df=group_df,
-                        raw_data_cols=raw_data_cols,
-                        metric_label=metric_label,
-                        y_axis_col=y_axis_col,
+                    summary_html = build_valid_max_concurrency_summary_html(
+                        tput_group_df=tput_group_df,
+                        ttft_group_df=ttft_group_df,
+                        tpot_group_df=tpot_group_df,
+                        conc_col=conc_col,
                         args=args,
                     )
+                    if summary_html:
+                        main_fh.write(summary_html)
+                        sub_fh.write(summary_html)
 
-                summary_html = build_valid_max_concurrency_summary_html(
-                    tput_group_df=tput_group_df,
-                    ttft_group_df=ttft_group_df,
-                    tpot_group_df=tpot_group_df,
-                    conc_col=conc_col,
-                    args=args,
-                )
-                if summary_html:
-                    main_fh.write(summary_html)
-                    sub_fh.write(summary_html)
+                    summary_df = build_valid_max_concurrency_summary_df(
+                        tput_group_df=tput_group_df,
+                        ttft_group_df=ttft_group_df,
+                        tpot_group_df=tpot_group_df,
+                        conc_col=conc_col,
+                        args=args,
+                    )
+                    if summary_df is not None:
+                        excel_blocks.append(
+                            ("Valid Max Concurrency Summary", summary_df)
+                        )
+                        if csv_dir:
+                            fn = _safe_filename(
+                                f"{sheet}__Valid_Max_Concurrency_Summary"
+                            )
+                            summary_df.to_csv(csv_dir / f"{fn}.csv", index=False)
+
+                if do_excel:
+                    _write_tables_to_excel_sheet(xw, sheet, excel_blocks)
+
+    if disable_excel:
+        print("Skipped Excel generation (VLLM_COMPARE_DISABLE_EXCEL=1).")
+    else:
+        print(f"Wrote Excel: {excel_path}")
+    if csv_dir:
+        print(f"Wrote CSVs under: {csv_dir}")
 
 
 def main():

.buildkite/performance-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -393,7 +393,7 @@ if __name__ == "__main__":
     with open(results_folder / md_file, "w") as f:
         results = read_markdown(
             "../.buildkite/performance-benchmarks/"
-            + "performance-benchmarks-descriptions.md"
+            "performance-benchmarks-descriptions.md"
         )
         results = results.format(
             latency_tests_markdown_table=latency_md_table,

.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh

@@ -1,6 +1,4 @@
 #!/bin/bash
-
-# This script should be run inside the CI process
 # This script assumes that we are already inside the vllm/ directory
 # Benchmarking results will be available inside vllm/benchmarks/results/
 
@@ -9,14 +7,26 @@
 set -x
 set -o pipefail
 
+# Environment-driven debug controls (like ON_CPU=1)
+DRY_RUN="${DRY_RUN:-0}"
+MODEL_FILTER="${MODEL_FILTER:-}"
+DTYPE_FILTER="${DTYPE_FILTER:-}"
+
+# Adaptive search controls
+ENABLE_ADAPTIVE_CONCURRENCY="${ENABLE_ADAPTIVE_CONCURRENCY:-0}"
+SLA_TTFT_MS="${SLA_TTFT_MS:-3000}"
+SLA_TPOT_MS="${SLA_TPOT_MS:-100}"
+ADAPTIVE_MAX_PROBES="${ADAPTIVE_MAX_PROBES:-8}"
+ADAPTIVE_MAX_CONCURRENCY="${ADAPTIVE_MAX_CONCURRENCY:-1024}"
+
 check_gpus() {
   if command -v nvidia-smi; then
     # check the number of GPUs and GPU type.
-    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+    declare -g gpu_count=$(nvidia-smi --list-gpus | grep -c . || true)
   elif command -v amd-smi; then
-    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
+    declare -g gpu_count=$(amd-smi list | grep -c 'GPU' || true)
   elif command -v hl-smi; then
-    declare -g gpu_count=$(hl-smi --list | grep -i "Module ID" | wc -l)
+    declare -g gpu_count=$(hl-smi --list | grep -ci "Module ID" || true)
   fi
 
   if [[ $gpu_count -gt 0 ]]; then
@@ -25,9 +35,9 @@ check_gpus() {
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  
+
   declare -g arch_suffix=''
-  
+
   if command -v nvidia-smi; then
     declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
   elif command -v amd-smi; then
@@ -44,7 +54,7 @@ check_cpus() {
   declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
   if [[ $numa_count -gt 0 ]]; then
     echo "NUMA found."
-    echo $numa_count
+    echo "$numa_count"
   else
     echo "Need at least 1 NUMA to run benchmarking."
     exit 1
@@ -112,13 +122,12 @@ json2envs() {
 }
 
 wait_for_server() {
-  # wait for vllm server to start
-  # return 1 if vllm server crashes
   local timeout_val="1200"
   timeout "$timeout_val" bash -c '
-    until curl -X POST localhost:8000/v1/completions; do
+    until curl -sf http://localhost:8000/v1/models >/dev/null; do
       sleep 1
-    done' && return 0 || return 1
+    done
+  '
 }
 
 kill_processes_launched_by_current_bash() {
@@ -181,19 +190,318 @@ upload_to_buildkite() {
   $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
 }
 
-run_latency_tests() {
-  # run latency tests using `vllm bench latency` command
-  # $1: a json file specifying latency test cases
+# -------------------------------
+# Adaptive concurrency helpers
+# -------------------------------
+result_json_path_for_serving() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency=$3
+  echo "$RESULTS_FOLDER/${test_name}_qps_${qps}_concurrency_${max_concurrency}.json"
+}
 
-  local latency_test_file
-  latency_test_file=$1
+extract_metric_ms() {
+  local metric_name=$1
+  local json_file=$2
 
-  # Iterate over latency tests
-  jq -c '.[]' "$latency_test_file" | while read -r params; do
+  [[ -f "$json_file" ]] || return 0
+
+  if [[ "$metric_name" == "ttft" ]]; then
+    jq -r '
+      [
+        .ttft_ms.p99?,
+        .metrics.ttft_ms.p99?,
+        .ttft.p99?,
+        .metrics.ttft.p99?,
+        .p99_ttft_ms?,
+        .ttft_ms.mean?,
+        .metrics.ttft_ms.mean?,
+        .ttft.mean?,
+        .metrics.ttft.mean?,
+        .mean_ttft_ms?
+      ] | map(select(. != null)) | .[0] // empty
+    ' "$json_file"
+  else
+    jq -r '
+      [
+        .tpot_ms.p99?,
+        .metrics.tpot_ms.p99?,
+        .tpot.p99?,
+        .metrics.tpot.p99?,
+        .p99_tpot_ms?,
+        .itl_ms.p99?,
+        .metrics.itl_ms.p99?,
+        .inter_token_latency_ms.p99?,
+        .tpot_ms.mean?,
+        .metrics.tpot_ms.mean?,
+        .tpot.mean?,
+        .metrics.tpot.mean?,
+        .itl_ms.mean?,
+        .metrics.itl_ms.mean?,
+        .mean_tpot_ms?,
+        .mean_itl_ms?
+      ] | map(select(. != null)) | .[0] // empty
+    ' "$json_file"
+  fi
+}
+
+evaluate_sla_from_json() {
+  local json_file=$1
+  local ttft
+  local tpot
+  local pass
+
+  [[ -f "$json_file" ]] || return 2
+
+  ttft=$(extract_metric_ms ttft "$json_file")
+  tpot=$(extract_metric_ms tpot "$json_file")
+
+  [[ -n "$ttft" && -n "$tpot" ]] || return 2
+
+  pass=$(jq -n \
+    --argjson ttft "$ttft" \
+    --argjson tpot "$tpot" \
+    --argjson sla_ttft "$SLA_TTFT_MS" \
+    --argjson sla_tpot "$SLA_TPOT_MS" \
+    '($ttft <= $sla_ttft) and ($tpot <= $sla_tpot)')
+
+  [[ "$pass" == "true" ]]
+}
+
+write_adaptive_summary_json() {
+  local summary_file=$1
+  local test_name=$2
+  local qps=$3
+  local static_last_pass=$4
+  local static_first_fail=$5
+  local final_last_pass=$6
+  local final_first_fail=$7
+
+  jq -n \
+    --arg test_name "$test_name" \
+    --arg qps "$qps" \
+    --argjson sla_ttft "$SLA_TTFT_MS" \
+    --argjson sla_tpot "$SLA_TPOT_MS" \
+    --arg static_last_pass "${static_last_pass:-}" \
+    --arg static_first_fail "${static_first_fail:-}" \
+    --arg final_last_pass "${final_last_pass:-}" \
+    --arg final_first_fail "${final_first_fail:-}" \
+    '{
+      test_name: $test_name,
+      qps: $qps,
+      sla_ttft_ms: $sla_ttft,
+      sla_tpot_ms: $sla_tpot,
+      static_last_pass: (if $static_last_pass == "" then null else ($static_last_pass | tonumber) end),
+      static_first_fail: (if $static_first_fail == "" then null else ($static_first_fail | tonumber) end),
+      final_last_pass: (if $final_last_pass == "" then null else ($final_last_pass | tonumber) end),
+      final_first_fail: (if $final_first_fail == "" then null else ($final_first_fail | tonumber) end)
+    }' > "$summary_file"
+}
+
+run_single_serving_probe() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency=$3
+  local tp=$4
+  local compilation_config_mode=$5
+  local optimization_level=$6
+  local client_args_effective=$7
+  local client_remote_args=$8
+  local server_command=$9
+
+  local new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
+  local result_json
+  local num_prompts_arg=""
+  local client_command
+
+  result_json=$(result_json_path_for_serving "$test_name" "$qps" "$max_concurrency")
+
+  if [[ -f "$result_json" ]]; then
+    evaluate_sla_from_json "$result_json"
+    return $?
+  fi
+
+  if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+    num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
+    if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
+    if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
+    num_prompts_arg="--num-prompts $num_prompts"
+  fi
+
+  client_command="vllm bench serve \
+    --save-result \
+    --result-dir $RESULTS_FOLDER \
+    --result-filename ${new_test_name}.json \
+    --request-rate $qps \
+    --max-concurrency $max_concurrency \
+    $num_prompts_arg \
+    --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level adaptive_search=1 \
+    $client_args_effective $client_remote_args "
+
+  echo "Adaptive probe: $client_command"
+
+  if [[ "${DRY_RUN:-0}" != "1" ]]; then
+    bash -c "$client_command"
+  fi
+
+  jq_output=$(jq -n \
+    --arg server "$server_command" \
+    --arg client "$client_command" \
+    --arg gpu "$gpu_type" \
+    '{
+      server_command: $server,
+      client_command: $client,
+      gpu_type: $gpu,
+      adaptive_search: true
+    }')
+  echo "$jq_output" > "$RESULTS_FOLDER/${new_test_name}.commands"
+
+  evaluate_sla_from_json "$result_json"
+}
+
+adaptive_refine_from_static_results() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency_list_raw=$3
+  local tp=$4
+  local compilation_config_mode=$5
+  local optimization_level=$6
+  local client_args_effective=$7
+  local client_remote_args=$8
+  local server_command=$9
+
+  local sorted_points
+  local point
+  local rc
+  local static_last_pass=""
+  local static_first_fail=""
+  local largest_static=""
+  local step_hint=1
+  local previous_point=""
+  local low
+  local high
+  local mid
+  local probes=0
+  local summary_file="$RESULTS_FOLDER/${test_name}_qps_${qps}_sla_summary.json"
+
+  [[ "${ENABLE_ADAPTIVE_CONCURRENCY}" == "1" ]] || return 0
+  [[ "${DRY_RUN:-0}" != "1" ]] || return 0
+
+  sorted_points=$(for point in $max_concurrency_list_raw; do printf '%s\n' "$point"; done | tr -d "'" | awk '/^[0-9]+$/' | sort -n | uniq)
+  [[ -n "$sorted_points" ]] || return 0
+
+  while read -r point; do
+    [[ -z "$point" ]] && continue
+    largest_static="$point"
+    evaluate_sla_from_json "$(result_json_path_for_serving "$test_name" "$qps" "$point")"
+    rc=$?
+    if (( rc == 0 )); then
+      static_last_pass="$point"
+    elif (( rc == 1 )); then
+      if [[ -n "$static_last_pass" ]]; then
+        static_first_fail="$point"
+        break
+      fi
+    fi
+
+    if [[ -n "$previous_point" ]]; then
+      step_hint=$(( point - previous_point ))
+      if (( step_hint < 1 )); then step_hint=1; fi
+    fi
+    previous_point="$point"
+  done <<< "$sorted_points"
+
+  if [[ -z "$static_last_pass" ]]; then
+    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "" "$static_first_fail" "" "$static_first_fail"
+    return 0
+  fi
+
+  if [[ -n "$static_first_fail" ]]; then
+    low=$static_last_pass
+    high=$static_first_fail
+    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
+      mid=$(( (low + high) / 2 ))
+      probes=$(( probes + 1 ))
+      run_single_serving_probe \
+        "$test_name" "$qps" "$mid" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
+      rc=$?
+      if (( rc == 0 )); then
+        low=$mid
+      elif (( rc == 1 )); then
+        high=$mid
+      else
+        break
+      fi
+    done
+    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "$static_first_fail" "$low" "$high"
+    return 0
+  fi
+
+  low=$largest_static
+  high=""
+  while (( probes < ADAPTIVE_MAX_PROBES )); do
+    point=$(( low + step_hint ))
+    if (( point > ADAPTIVE_MAX_CONCURRENCY )); then
+      point=$ADAPTIVE_MAX_CONCURRENCY
+    fi
+    (( point > low )) || break
+    probes=$(( probes + 1 ))
+    run_single_serving_probe \
+      "$test_name" "$qps" "$point" "$tp" \
+      "$compilation_config_mode" "$optimization_level" \
+      "$client_args_effective" "$client_remote_args" "$server_command"
+    rc=$?
+    if (( rc == 0 )); then
+      low=$point
+      (( point == ADAPTIVE_MAX_CONCURRENCY )) && break
+      step_hint=$(( step_hint * 2 ))
+      if (( step_hint < 1 )); then step_hint=1; fi
+    elif (( rc == 1 )); then
+      high=$point
+      break
+    else
+      break
+    fi
+  done
+
+  if [[ -n "$high" ]]; then
+    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
+      mid=$(( (low + high) / 2 ))
+      probes=$(( probes + 1 ))
+      run_single_serving_probe \
+        "$test_name" "$qps" "$mid" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
+      rc=$?
+      if (( rc == 0 )); then
+        low=$mid
+      elif (( rc == 1 )); then
+        high=$mid
+      else
+        break
+      fi
+    done
+  fi
+
+  write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "" "$low" "$high"
+}
+
+run_benchmark_tests() {
+  # run benchmark tests using `vllm bench <test_type>` command
+  # $1: test type (latency or throughput)
+  # $2: a json file specifying test cases
+
+  local test_type=$1
+  local test_file=$2
+
+  # Iterate over tests
+  jq -c '.[]' "$test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
     test_name=$(echo "$params" | jq -r '.test_name')
-    if [[ ! "$test_name" =~ ^latency_ ]]; then
-      echo "In latency-test.json, test_name must start with \"latency_\"."
+    if [[ ! "$test_name" =~ ^${test_type}_ ]]; then
+      echo "In ${test_type}-test.json, test_name must start with \"${test_type}_\"."
       exit 1
     fi
 
@@ -204,15 +512,15 @@ run_latency_tests() {
     fi
 
     # get arguments
-    latency_params=$(echo "$params" | jq -r '.parameters')
-    latency_args=$(json2args "$latency_params")
-    latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    latency_envs=$(json2envs "$latency_environment_variables")
+    bench_params=$(echo "$params" | jq -r '.parameters')
+    bench_args=$(json2args "$bench_params")
+    bench_environment_variables=$(echo "$params" | jq -r '.environment_variables')
+    bench_envs=$(json2envs "$bench_environment_variables")
 
     # check if there is enough GPU to run the test
-    tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
+    tp=$(echo "$bench_params" | jq -r '.tensor_parallel_size')
     if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$latency_params" | jq -r '.pipeline_parallel_size // 1')
+      pp=$(echo "$bench_params" | jq -r '.pipeline_parallel_size // 1')
       world_size=$(($tp*$pp))
       if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
         echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
@@ -225,118 +533,42 @@ run_latency_tests() {
       fi
     fi
 
-    latency_command=" $latency_envs vllm bench latency \
+    bench_command=" $bench_envs vllm bench $test_type \
       --output-json $RESULTS_FOLDER/${test_name}.json \
-      $latency_args"
+      $bench_args"
 
     echo "Running test case $test_name"
-    echo "Latency command: $latency_command"
+    echo "${test_type^} command: $bench_command"
 
-    # recoding benchmarking command ang GPU command
+    # recording benchmarking command and GPU command
     jq_output=$(jq -n \
-      --arg latency "$latency_command" \
+      --arg command "$bench_command" \
       --arg gpu "$gpu_type" \
+      --arg test_type "$test_type" \
       '{
-        latency_command: $latency,
+        ($test_type + "_command"): $command,
         gpu_type: $gpu
       }')
     echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
 
     # run the benchmark
-    eval "$latency_command"
+    eval "$bench_command"
 
     kill_gpu_processes
 
   done
 }
 
-run_throughput_tests() {
-  # run throughput tests using `vllm bench throughput`
-  # $1: a json file specifying throughput test cases
+run_latency_tests() { run_benchmark_tests "latency" "$1"; }
+run_startup_tests() { run_benchmark_tests "startup" "$1"; }
+run_throughput_tests() { run_benchmark_tests "throughput" "$1"; }
 
-  local throughput_test_file
-  throughput_test_file=$1
-
-  # Iterate over throughput tests
-  jq -c '.[]' "$throughput_test_file" | while read -r params; do
-    # get the test name, and append the GPU type back to it.
-    test_name=$(echo "$params" | jq -r '.test_name')
-    if [[ ! "$test_name" =~ ^throughput_ ]]; then
-      echo "In throughput-test.json, test_name must start with \"throughput_\"."
-      exit 1
-    fi
-
-    # if TEST_SELECTOR is set, only run the test cases that match the selector
-    if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
-      echo "Skip test case $test_name."
-      continue
-    fi
-
-    # get arguments
-    throughput_params=$(echo "$params" | jq -r '.parameters')
-    throughput_args=$(json2args "$throughput_params")
-    throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    throughput_envs=$(json2envs "$throughput_environment_variables")
-
-    # check if there is enough GPU to run the test
-    tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [[ "$ON_CPU" == "1" ]]; then
-      pp=$(echo "$throughput_params" | jq -r '.pipeline_parallel_size // 1')
-      world_size=$(($tp*$pp))
-      if [[ $numa_count -lt $world_size  && -z "${REMOTE_HOST}" ]]; then
-        echo "Required world-size $world_size but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
-    fi
-
-    throughput_command=" $throughput_envs vllm bench throughput \
-      --output-json $RESULTS_FOLDER/${test_name}.json \
-      $throughput_args"
-
-    echo "Running test case $test_name"
-    echo "Throughput command: $throughput_command"
-    # recoding benchmarking command ang GPU command
-    jq_output=$(jq -n \
-      --arg command "$throughput_command" \
-      --arg gpu "$gpu_type" \
-      '{
-        throughput_command: $command,
-        gpu_type: $gpu
-      }')
-    echo "$jq_output" >"$RESULTS_FOLDER/$test_name.commands"
-
-    # run the benchmark
-    eval "$throughput_command"
-
-    kill_gpu_processes
-
-  done
-}
-
-run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
-  # $1: a json file specifying serving test cases
-  #
-  # Supported JSON formats:
-  # 1) Plain format: top-level array
-  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #
-  # 2) Default parameters field + plain format tests
-  #    {
-  #      "defaults": { ... },
-  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
-  #    }
-
-  local serving_test_file
-  serving_test_file=$1
-
-  # Iterate over serving tests
-  jq -c '
+merge_serving_tests_stream() {
+  # Emit merged serving test objects, optionally filtered by MODEL_FILTER/DTYPE_FILTER in DRY_RUN mode.
+  # This helper does NOT modify JSON; it only filters the stream in dry-run mode.
+  local serving_test_file="$1"
+  # shellcheck disable=SC2016
+  local merged='
     if type == "array" then
       # Plain format: test cases array
       .[]
@@ -358,7 +590,50 @@ run_serving_tests() {
     else
       error("Unsupported serving test file format: must be array or object with .tests")
     end
-  ' "$serving_test_file" | while read -r params; do
+  '
+
+  jq -c "$merged" "$serving_test_file" | \
+  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
+    jq -c --arg model "$MODEL_FILTER" --arg dtype "$DTYPE_FILTER" '
+      select((($model|length)==0)
+             or ((.server_parameters.model // "") == $model)
+             or ((.client_parameters.model // "") == $model))
+      | select((($dtype|length)==0) or ((.server_parameters.dtype // "") == $dtype))
+    '
+  else
+    cat
+  fi
+}
+
+run_serving_tests() {
+  # run serving tests using `vllm bench serve` command
+  # $1: a json file specifying serving test cases
+  #
+  # Supported JSON formats:
+  # 1) Plain format: top-level array
+  #    [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #
+  # 2) Default parameters field + plain format tests
+  #    {
+  #      "defaults": { ... },
+  #      "tests": [ { "test_name": "...", "server_parameters": {...}, ... }, ... ]
+  #    }
+
+  local serving_test_file
+  serving_test_file=$1
+
+  # In dry-run mode, if filters are provided but no tests match, fail fast.
+  if [[ "${DRY_RUN:-0}" == "1" && ( "${MODEL_FILTER}${DTYPE_FILTER}" != "" ) ]]; then
+    local count
+    count=$(merge_serving_tests_stream "$serving_test_file" | wc -l | tr -d ' ')
+    if [[ "$count" -eq 0 ]]; then
+      echo "No matching serving tests found in $serving_test_file for model='$MODEL_FILTER' dtype='$DTYPE_FILTER'." >&2
+      return 0
+    fi
+  fi
+
+  # Iterate over serving tests (merged + optional filtered stream)
+  merge_serving_tests_stream "$serving_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
     test_name=$(echo "$params" | jq -r '.test_name')
     if [[ ! "$test_name" =~ ^serving_ ]]; then
@@ -377,10 +652,48 @@ run_serving_tests() {
     server_envs=$(echo "$params" | jq -r '.server_environment_variables')
     client_params=$(echo "$params" | jq -r '.client_parameters')
 
-    server_args=$(json2args "$server_params")
+    # vLLM serve CLI: model must be positional (no --model). Convert server_parameters accordingly.
+    server_model=$(echo "$server_params" | jq -r '.model // empty')
+    if [[ -z "$server_model" || "$server_model" == "null" ]]; then
+      echo "Error: serving test '$test_name' is missing server_parameters.model" >&2
+      exit 1
+    fi
+    server_params_no_model=$(echo "$server_params" | jq -c 'del(.model)')
+    server_args=$(json2args "$server_params_no_model")
+
     server_envs=$(json2envs "$server_envs")
     client_args=$(json2args "$client_params")
 
+    # ------------------------------------------------------------
+    # Option 1: Dynamic num-prompts scaling based on max_concurrency
+    #
+    # If PROMPTS_PER_CONCURRENCY is set, override JSON num_prompts with:
+    #   num_prompts = max_concurrency * PROMPTS_PER_CONCURRENCY
+    #
+    # If PROMPTS_PER_CONCURRENCY is NOT set, keep JSON num_prompts behavior
+    # unchanged (i.e., whatever is in serving-tests-*.json).
+    # ------------------------------------------------------------
+    PROMPTS_PER_CONCURRENCY="${PROMPTS_PER_CONCURRENCY-}"  # no default on purpose
+    MIN_NUM_PROMPTS="${MIN_NUM_PROMPTS:-1}"
+    MAX_NUM_PROMPTS="${MAX_NUM_PROMPTS:-1000000}"
+
+    if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
+      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
+      # Handles: --num-prompts 123   and   --num-prompts=123
+      client_args_no_np="$(
+        printf ' %s ' "$client_args" \
+        | sed -E \
+          -e 's/[[:space:]]--num-prompts=([^[:space:]]+)([[:space:]]|$)/ /g' \
+          -e 's/[[:space:]]--num-prompts[[:space:]]+([^[:space:]]+)([[:space:]]|$)/ /g'
+      )"
+      # normalize whitespace
+      client_args_no_np="$(echo "$client_args_no_np" | tr -s ' ' | sed -E 's/^ //; s/ $//')"
+      client_args_no_np="$(echo "$client_args_no_np" | xargs)"
+      client_args_effective="$client_args_no_np"
+    else
+      client_args_effective="$client_args"
+    fi
     # qps_list
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
@@ -412,14 +725,13 @@ run_serving_tests() {
     fi
 
     # check if server model and client model is aligned
-    server_model=$(echo "$server_params" | jq -r '.model')
     client_model=$(echo "$client_params" | jq -r '.model')
     if [[ $server_model != "$client_model" ]]; then
       echo "Server model and client model must be the same. Skip testcase $test_name."
       continue
     fi
 
-    server_command="$server_envs vllm serve \
+    server_command="$server_envs vllm serve $server_model \
       $server_args"
 
     # run the server
@@ -427,7 +739,7 @@ run_serving_tests() {
     echo "Server command: $server_command"
     # support remote vllm server
     client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" ]]; then
+    if [[ -z "${REMOTE_HOST}" && "${DRY_RUN:-0}" != "1" ]]; then
       bash -c "$server_command" &
       server_pid=$!
       # wait until the server is alive
@@ -438,6 +750,9 @@ run_serving_tests() {
         echo ""
         echo "vLLM failed to start within the timeout period."
       fi
+    elif [[ "${DRY_RUN:-0}" == "1" ]]; then
+        # dry-run: don't start server
+        echo "Dry Run."
     else
       server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
       if [[ ${REMOTE_PORT} ]]; then
@@ -447,34 +762,48 @@ run_serving_tests() {
       fi
     fi
 
+    # save the compilation mode and optimization level on the serving results
+    # whenever they are set
+    compilation_config_mode=$(echo "$server_params" | jq -r '."compilation_config.mode" // empty')
+    optimization_level=$(echo "$server_params" | jq -r '.optimization_level // empty')
+
     # iterate over different QPS
     for qps in $qps_list; do
       # remove the surrounding single quote from qps
       if [[ "$qps" == *"inf"* ]]; then
-        echo "qps was $qps"
         qps="inf"
-        echo "now qps is $qps"
       fi
 
       # iterate over different max_concurrency
       for max_concurrency in $max_concurrency_list; do
-        new_test_name=$test_name"_qps_"$qps"_concurrency_"$max_concurrency
+        new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
         echo " new test name $new_test_name"
-        # pass the tensor parallel size to the client so that it can be displayed
-        # on the benchmark dashboard
+        # If PROMPTS_PER_CONCURRENCY is set, compute per-concurrency --num-prompts.
+        num_prompts_arg=""
+        if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+          num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
+          if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
+          if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
+          num_prompts_arg="--num-prompts $num_prompts"
+        fi
+        # pass the tensor parallel size, the compilation mode, and the optimization
+        # level to the client so that they can be used on the benchmark dashboard
         client_command="vllm bench serve \
           --save-result \
           --result-dir $RESULTS_FOLDER \
           --result-filename ${new_test_name}.json \
           --request-rate $qps \
           --max-concurrency $max_concurrency \
-          --metadata "tensor_parallel_size=$tp" \
-          $client_args $client_remote_args "
+          $num_prompts_arg \
+          --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level \
+          $client_args_effective $client_remote_args "
 
         echo "Running test case $test_name with qps $qps"
         echo "Client command: $client_command"
 
-        bash -c "$client_command"
+        if [[ "${DRY_RUN:-0}" != "1" ]]; then
+          bash -c "$client_command"
+        fi
 
         # record the benchmarking commands
         jq_output=$(jq -n \
@@ -489,15 +818,23 @@ run_serving_tests() {
         echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
 
       done
+
+      adaptive_refine_from_static_results \
+        "$test_name" "$qps" "$max_concurrency_list" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
     done
 
     # clean up
-    kill -9 $server_pid
-    kill_gpu_processes
+    if [[ "${DRY_RUN:-0}" != "1" ]]; then
+      kill -9 "$server_pid"
+      kill_gpu_processes
+    fi
   done
 }
 
 main() {
+
   local ARCH
   ARCH=''
   if [[ "$ON_CPU" == "1" ]]; then
@@ -507,7 +844,13 @@ main() {
      check_gpus
      ARCH="$arch_suffix"
   fi
-  check_hf_token
+
+  # DRY_RUN does not execute vLLM; do not require HF_TOKEN.
+  if [[ "${DRY_RUN:-0}" != "1" ]]; then
+    check_hf_token
+  else
+    echo "DRY_RUN=1 -> skip HF_TOKEN validation"
+  fi
 
   # dependencies
   (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
@@ -528,17 +871,24 @@ main() {
 
   # dump vllm info via vllm collect-env
   env_output=$(vllm collect-env)
-
   echo "$env_output" >"$RESULTS_FOLDER/vllm_env.txt"
 
   # benchmarking
-  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}" || exit $?
+
+  if [[ "${DRY_RUN:-0}" == "1" ]]; then
+    echo "DRY_RUN=1 -> skip latency/startup/throughput suites"
+    exit 0
+  fi
+
   run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
+  run_startup_tests $QUICK_BENCHMARK_ROOT/tests/"${STARTUP_JSON:-startup-tests$ARCH.json}"
   run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
 
   # postprocess benchmarking results
   pip install tabulate pandas
   python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
+  python3 $QUICK_BENCHMARK_ROOT/scripts/compare-json-results.py -f $RESULTS_FOLDER/benchmark_results.json
 
   upload_to_buildkite
 }

.buildkite/performance-benchmarks/tests/latency-tests-hpu.json

@@ -51,5 +51,56 @@
             "max-model-len": 256,
             "async-scheduling": ""
         }
+    },
+    {
+        "test_name": "latency_deepseek_r1",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "load_format": "dummy",
+            "max-model-len": 2048,
+            "dtype": "bfloat16"
+        }
+    },
+    {
+        "test_name": "latency_llama4_maverick_17b128e_instruct_fp8",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "max-model-len": 512,
+            "max-num-seqs": 128,
+            "async-scheduling": "",
+            "gpu-memory-utilization": 0.95,
+            "enable_expert_parallel": ""
+        }
+    },
+    {
+        "test_name": "latency_qwen3_8b",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "Qwen/Qwen3-8B",
+            "tensor_parallel_size": 1,
+            "max-model-len": 2048,
+            "max-num-seqs": 128,
+            "dtype": "bfloat16",
+            "async-scheduling": ""
+        }
     }
 ]

.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json

@@ -36,6 +36,7 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
+      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -127,4 +128,4 @@
       }
     }
   ]
-}
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-asr.json

@@ -0,0 +1,38 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120
+    },
+    "server_parameters": {
+      "dtype": "bfloat16",
+      "model": "openai/whisper-large-v3-turbo"
+    },
+    "client_parameters": {
+      "model": "openai/whisper-large-v3-turbo",
+      "backend": "openai-audio",
+      "endpoint": "/v1/audio/transcriptions",
+      "dataset_name": "hf",
+      "dataset_path": "openslr/librispeech_asr",
+      "hf_subset": "clean",
+      "hf_split": "test",
+      "no_stream": "",
+      "no_oversample": "",
+      "temperature": 0,
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_whisper_large_v3_turbo_librispeech_clean_tp1",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {}
+    }
+  ]
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-embed.json

@@ -0,0 +1,41 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [
+      32,
+      64,
+      128
+    ],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+      "VLLM_CPU_SGL_KERNEL": 1,
+      "VLLM_CPU_KVCACHE_SPACE": 40
+    },
+    "server_parameters": {
+      "dtype": "bfloat16",
+      "model": "jinaai/jina-embeddings-v3",
+      "trust_remote_code": ""
+    },
+    "client_parameters": {
+      "model": "jinaai/jina-embeddings-v3",
+      "backend": "openai-embeddings",
+      "endpoint": "/v1/embeddings",
+      "dataset_name": "sharegpt",
+      "dataset_path": "ShareGPT_V3_unfiltered_cleaned_split.json",
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_jina_embed_v3_tp1_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {}
+    }
+  ]
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-text.json

@@ -0,0 +1,356 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
+      "VLLM_CPU_SGL_KERNEL": 1,
+      "VLLM_CPU_KVCACHE_SPACE": 40
+    },
+    "server_parameters": {
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "tensor_parallel_size": 1,
+      "dtype": "bfloat16",
+      "distributed_executor_backend": "mp",
+      "block_size": 128,
+      "trust_remote_code": "",
+      "disable_log_stats": "",
+      "max_num_batched_tokens": 2048,
+      "max_num_seqs": 256
+    },
+    "client_parameters": {
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "backend": "vllm",
+      "ignore-eos": "",
+      "temperature": 0,
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_llama8B_tp1_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp1_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp1_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp2_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp4_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp1_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp2_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp4_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama3B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_granite2B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen1.7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-1.7B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen4B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-4B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_qwen8B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "Qwen/Qwen3-8B",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_glm9B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "zai-org/glm-4-9b-hf",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_gemma7B_tp1_random_128_128",
+      "server_parameters": {
+        "model": "google/gemma-7b",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "google/gemma-7b",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    }
+  ]
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu.json

@@ -26,6 +26,7 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
+      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -72,17 +73,6 @@
         "random-output-len": 128
       }
     },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
     {
       "test_name": "serving_llama8B_tp1_random_128_2048",
       "server_parameters": {
@@ -105,17 +95,6 @@
         "random-output-len": 2048
       }
     },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
     {
       "test_name": "serving_llama8B_tp1_random_2048_128",
       "server_parameters": {
@@ -139,144 +118,25 @@
       }
     },
     {
-      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "test_name": "serving_llama8B_tp1_random_2048_2048",
       "server_parameters": {
-        "tensor_parallel_size": 4
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "dataset_name": "random",
         "random-input-len": 2048,
-        "random-output-len": 128
+        "random-output-len": 2048
       }
     },
     {
-      "test_name": "serving_llama8B_int4_tp1_random_128_128",
+      "test_name": "serving_llama8B_tp2_random_2048_2048",
       "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp2_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
         "tensor_parallel_size": 2
       },
       "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
         "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int4_tp4_random_128_128",
-      "server_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama3B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_granite2B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen1.7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen4B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen8B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_glm9B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "zai-org/glm-4-9b-hf",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma7B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-7b",
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "model": "google/gemma-7b",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
+        "random-input-len": 2048,
+        "random-output-len": 2048
       }
     }
   ]

.buildkite/performance-benchmarks/tests/serving-tests-hpu.json

@@ -10,7 +10,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -22,6 +21,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -37,7 +37,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -49,6 +48,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -64,7 +64,6 @@
         "server_parameters": {
             "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
             "tensor_parallel_size": 2,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -76,6 +75,88 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_deepseek_r1",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "disable_log_stats": "",
+            "load_format": "dummy",
+            "max-model-len": 2048,
+            "max-num-seqs": 200,
+            "async-scheduling": "",
+            "dtype": "bfloat16"
+        },
+        "client_parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama4_maverick_17b128e_instruct_fp8",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "disable_log_stats": "",
+            "max-model-len": 2048,
+            "max-num-seqs": 128,
+            "async-scheduling": "",
+            "enable_expert_parallel": "",
+            "max-num-batched-tokens": 4096
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_qwen3_8b",
+        "qps_list": [1, 4, 10, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "tensor_parallel_size": 1,
+            "dtype": "bfloat16",
+            "disable_log_stats": "",
+            "async-scheduling": ""
+        },
+        "client_parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     }

.buildkite/performance-benchmarks/tests/serving-tests.json

@@ -5,7 +5,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -14,6 +13,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -23,7 +23,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -32,6 +31,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -41,7 +41,6 @@
         "server_parameters": {
             "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
             "tensor_parallel_size": 2,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -50,6 +49,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -59,7 +59,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "speculative_config": {
                 "model": "turboderp/Qwama-0.5B-Instruct",
                 "num_speculative_tokens": 4,
@@ -71,6 +70,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200 
         }
     }

.buildkite/performance-benchmarks/tests/throughput-tests-hpu.json

@@ -57,5 +57,67 @@
             "max-num-seqs": 512,
             "async-scheduling": ""
         }
+    },
+    {
+        "test_name": "throughput_deepseek_r1",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "load_format": "dummy",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "backend": "vllm",
+            "max-model-len": 2048,
+            "max-num-seqs": 384,
+            "async-scheduling": ""
+        }
+    },
+    {
+        "test_name": "throughput_llama4_maverick_17b128e_instruct_fp8",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "backend": "vllm",
+            "max-model-len": 2048,
+            "max-num-seqs": 512,
+            "async-scheduling": "",
+            "enable_expert_parallel": ""
+        }
+    },
+    {
+        "test_name": "throughput_qwen3_8b",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "max-num-seqs": 512,
+            "backend": "vllm",
+            "async-scheduling": ""
+        }
     }
 ]

.buildkite/release-pipeline.yaml

@@ -12,7 +12,7 @@ steps:
         depends_on: ~
         id: build-wheel-arm64-cuda-12-9
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
           # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
@@ -27,7 +27,7 @@ steps:
         depends_on: ~
         id: build-wheel-arm64-cuda-13-0
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           # #NOTE: torch_cuda_arch_list is derived from upstream PyTorch build files here:
           # https://github.com/pytorch/pytorch/blob/main/.ci/aarch64_linux/aarch64_ci_build.sh#L7
@@ -42,7 +42,7 @@ steps:
         depends_on: ~
         id: build-wheel-arm64-cpu
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
@@ -55,7 +55,7 @@ steps:
         depends_on: ~
         id: build-wheel-x86-cuda-12-9
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
@@ -68,7 +68,7 @@ steps:
         depends_on: ~
         id: build-wheel-x86-cuda-13-0
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
@@ -81,15 +81,23 @@ steps:
         depends_on: ~
         id: build-wheel-x86-cpu
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
           - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
+  - label: "Generate and upload wheel indices"
+    depends_on: "build-wheels"
+    allow_dependency_failure: true
+    agents:
+      queue: cpu_queue_release
+    commands:
+      - "bash .buildkite/scripts/generate-and-upload-nightly-index.sh"
+
   - group: "Build release Docker images"
     key: "build-release-images"
     steps:
@@ -97,7 +105,7 @@ steps:
         depends_on: ~
         id: build-release-image-x86
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
@@ -110,7 +118,7 @@ steps:
         depends_on: ~
         id: build-release-image-arm64
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
@@ -120,7 +128,7 @@ steps:
         depends_on: ~
         id: build-release-image-x86-cuda-13-0
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
@@ -133,13 +141,57 @@ steps:
         depends_on: ~
         id: build-release-image-arm64-cuda-13-0
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           # compute capability 12.0 for RTX-50 series / RTX PRO 6000 Blackwell, 12.1 for DGX Spark
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130"
 
+      - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
+        depends_on: ~
+        id: build-release-image-x86-ubuntu2404
+        agents:
+          queue: cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
+
+      - label: "Build release image - aarch64 - CUDA 12.9 - Ubuntu 24.04"
+        depends_on: ~
+        id: build-release-image-arm64-ubuntu2404
+        agents:
+          queue: arm64_cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
+
+      - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
+        depends_on: ~
+        id: build-release-image-x86-cuda-13-0-ubuntu2404
+        agents:
+          queue: cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+
+      - label: "Build release image - aarch64 - CUDA 13.0 - Ubuntu 24.04"
+        depends_on: ~
+        id: build-release-image-arm64-cuda-13-0-ubuntu2404
+        agents:
+          queue: arm64_cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 8.9 9.0 10.0+PTX 12.0 12.1' --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.1-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu130-ubuntu2404"
+
       - block: "Build release image for x86_64 CPU"
         key: block-cpu-release-image-build
         depends_on: ~
@@ -149,10 +201,10 @@ steps:
           - block-cpu-release-image-build
           - input-release-version
         agents:
-          queue: cpu_queue_postmerge
+          queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --build-arg VLLM_CPU_AMXBF16=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
         env:
@@ -167,7 +219,7 @@ steps:
           - block-arm64-cpu-release-image-build
           - input-release-version
         agents:
-          queue: arm64_cpu_queue_postmerge
+          queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
@@ -176,23 +228,6 @@ steps:
         env:
           DOCKER_BUILDKIT: "1"
 
-      - block: "Build release image for x86_64 ROCm"
-        key: block-rocm-release-image-build
-        depends_on: ~
-
-      - label: "Build release image - x86_64 - ROCm"
-        depends_on: block-rocm-release-image-build
-        id: build-release-image-rocm
-        agents:
-          queue: cpu_queue_postmerge
-        commands:
-          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          # Build base image first
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --tag rocm/vllm-dev:base-$BUILDKITE_COMMIT --target final --progress plain -f docker/Dockerfile.rocm_base ."
-          # Build vLLM ROCm image using the base
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg BASE_IMAGE=rocm/vllm-dev:base-$BUILDKITE_COMMIT --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm --target vllm-openai --progress plain -f docker/Dockerfile.rocm ."
-          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-rocm"
-
   - group: "Publish release images"
     key: "publish-release-images"
     steps:
@@ -202,7 +237,7 @@ steps:
           - build-release-image-arm64
         id: create-multi-arch-manifest
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64 --amend"
@@ -213,7 +248,7 @@ steps:
           - create-multi-arch-manifest
         id: annotate-release-workflow
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
           - "bash .buildkite/scripts/annotate-release.sh"
 
@@ -223,18 +258,42 @@ steps:
           - build-release-image-arm64-cuda-13-0
         id: create-multi-arch-manifest-cuda-13-0
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
           - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130 --amend"
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130"
 
+      - label: "Create multi-arch manifest - CUDA 12.9 - Ubuntu 24.04"
+        depends_on:
+          - build-release-image-x86-ubuntu2404
+          - build-release-image-arm64-ubuntu2404
+        id: create-multi-arch-manifest-ubuntu2404
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-ubuntu2404 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
+
+      - label: "Create multi-arch manifest - CUDA 13.0 - Ubuntu 24.04"
+        depends_on:
+          - build-release-image-x86-cuda-13-0-ubuntu2404
+          - build-release-image-arm64-cuda-13-0-ubuntu2404
+        id: create-multi-arch-manifest-cuda-13-0-ubuntu2404
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - "docker manifest create public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-x86_64-cu130-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-aarch64-cu130-ubuntu2404 --amend"
+          - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu130-ubuntu2404"
+
       - label: "Publish nightly multi-arch image to DockerHub"
         depends_on:
           - create-multi-arch-manifest
         if: build.env("NIGHTLY") == "1"
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
           - "bash .buildkite/scripts/push-nightly-builds.sh"
           # Clean up old nightly builds (keep only last 14)
@@ -252,7 +311,7 @@ steps:
           - create-multi-arch-manifest-cuda-13-0
         if: build.env("NIGHTLY") == "1"
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
           - "bash .buildkite/scripts/push-nightly-builds.sh cu130"
           # Clean up old nightly builds (keep only last 14)
@@ -274,14 +333,14 @@ steps:
           - input-release-version
           - build-wheels
 
-      - label: "Upload release wheels to PyPI and GitHub"
+      - label: "Upload release wheels to PyPI"
         depends_on:
           - block-upload-release-wheels
         id: upload-release-wheels
         agents:
-          queue: small_cpu_queue_postmerge
+          queue: small_cpu_queue_release
         commands:
-          - "bash .buildkite/scripts/upload-release-wheels.sh"
+          - "bash .buildkite/scripts/upload-release-wheels-pypi.sh"
 
   # =============================================================================
   # ROCm Release Pipeline (x86_64 only)
@@ -291,184 +350,112 @@ steps:
   # To build a specific version, trigger the build from that branch/tag.
   #
   # Environment variables for ROCm builds (set via Buildkite UI or schedule):
-  #   ROCM_PYTHON_VERSION: Python version (default: 3.12)
-  #   PYTORCH_ROCM_ARCH: GPU architectures (default: gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151)
-  #   ROCM_UPLOAD_WHEELS: Upload to S3 (default: false for nightly, true for releases)
-  #   ROCM_FORCE_REBUILD: Force rebuild base wheels, ignore S3 cache (default: false)
   #
   # Note: ROCm version is determined by BASE_IMAGE in docker/Dockerfile.rocm_base
-  #       (currently rocm/dev-ubuntu-22.04:7.1-complete)
   #
   # =============================================================================
 
-  # ROCm Input Step - Collect build configuration (manual trigger only)
-  - input: "ROCm Wheel Release Build Configuration"
-    key: input-rocm-config
-    depends_on: ~
-    if: build.source == "ui"
-    fields:
-      - text: "Python Version"
-        key: "rocm-python-version"
-        default: "3.12"
-        hint: "Python version (e.g., 3.12)"
-      - text: "GPU Architectures"
-        key: "rocm-pytorch-rocm-arch"
-        default: "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151"
-        hint: "Semicolon-separated GPU architectures"
-      - select: "Upload Wheels to S3"
-        key: "rocm-upload-wheels"
-        default: "true"
-        options:
-          - label: "No - Build only (nightly/dev)"
-            value: "false"
-          - label: "Yes - Upload to S3 (release)"
-            value: "true"
-      - select: "Force Rebuild Base Wheels"
-        key: "rocm-force-rebuild"
-        default: "false"
-        hint: "Ignore S3 cache and rebuild base wheels from scratch"
-        options:
-          - label: "No - Use cached wheels if available"
-            value: "false"
-          - label: "Yes - Rebuild even if cache exists"
-            value: "true"
-
   # ROCm Job 1: Build ROCm Base Wheels (with S3 caching)
-  - label: ":rocm: Build ROCm Base Wheels"
+  - label: ":rocm: Build ROCm Base Image & Wheels"
     id: build-rocm-base-wheels
-    depends_on:
-      - step: input-rocm-config
-        allow_failure: true  # Allow failure so non-UI builds can proceed (input step is skipped)
+    depends_on: ~
     agents:
-      queue: cpu_queue_postmerge
+      queue: cpu_queue_release
     commands:
-      # Set configuration and check cache
       - |
         set -euo pipefail
 
-        # Get values from meta-data (set by input step) or use defaults
-        PYTHON_VERSION="$$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo '')"
-        export PYTHON_VERSION="$${PYTHON_VERSION:-3.12}"
-
-        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
-        export PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
-
-        # Check for force rebuild flag
-        ROCM_FORCE_REBUILD="$${ROCM_FORCE_REBUILD:-}"
-        if [ -z "$${ROCM_FORCE_REBUILD}" ]; then
-          ROCM_FORCE_REBUILD="$$(buildkite-agent meta-data get rocm-force-rebuild 2>/dev/null || echo '')"
-        fi
-
-        echo "========================================"
-        echo "ROCm Base Wheels Build Configuration"
-        echo "========================================"
-        echo "  PYTHON_VERSION: $${PYTHON_VERSION}"
-        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
-        echo "  ROCM_FORCE_REBUILD: $${ROCM_FORCE_REBUILD:-false}"
-        echo "========================================"
-
-        # Save resolved config for later jobs
-        buildkite-agent meta-data set "rocm-python-version" "$${PYTHON_VERSION}"
-        buildkite-agent meta-data set "rocm-pytorch-rocm-arch" "$${PYTORCH_ROCM_ARCH}"
-
-        # Check S3 cache for pre-built wheels
+        # Generate cache key
         CACHE_KEY=$$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-        CACHE_PATH=$$(.buildkite/scripts/cache-rocm-base-wheels.sh path)
-        echo ""
-        echo "Cache key: $${CACHE_KEY}"
-        echo "Cache path: $${CACHE_PATH}"
+        ECR_CACHE_TAG="public.ecr.aws/q9t5s3a7/vllm-release-repo:$${CACHE_KEY}-rocm-base"
 
-        # Save cache key for downstream jobs
-        buildkite-agent meta-data set "rocm-cache-key" "$${CACHE_KEY}"
+        echo "========================================"
+        echo "ROCm Base Build Configuration"
+        echo "========================================"
+        echo "  CACHE_KEY: $${CACHE_KEY}"
+        echo "  ECR_CACHE_TAG: $${ECR_CACHE_TAG}"
+        echo "========================================"
+        
+        # Login to ECR
+        aws ecr-public get-login-password --region us-east-1 | \
+          docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+        
+        IMAGE_EXISTS=false
+        WHEELS_EXIST=false
+        
+        # Check ECR for Docker image
 
-        CACHE_STATUS="miss"
-        if [ "$${ROCM_FORCE_REBUILD}" != "true" ]; then
-          CACHE_STATUS=$$(.buildkite/scripts/cache-rocm-base-wheels.sh check)
-        else
-          echo "Force rebuild requested, skipping cache check"
+        if docker manifest inspect "$${ECR_CACHE_TAG}" > /dev/null 2>&1; then
+          IMAGE_EXISTS=true
+          echo "ECR image cache HIT"
+        fi
+        
+        # Check S3 for wheels
+        WHEEL_CACHE_STATUS=$(.buildkite/scripts/cache-rocm-base-wheels.sh check)
+        if [ "$${WHEEL_CACHE_STATUS}" = "hit" ]; then
+          WHEELS_EXIST=true
+          echo "S3 wheels cache HIT"
         fi
 
-        if [ "$${CACHE_STATUS}" = "hit" ]; then
+        
+        # Scenario 1: Both cached (best case)
+        if [ "$${IMAGE_EXISTS}" = "true" ] && [ "$${WHEELS_EXIST}" = "true" ]; then
           echo ""
-          echo "CACHE HIT! Downloading pre-built wheels..."
+          echo "FULL CACHE HIT - Reusing both image and wheels"
           echo ""
+
+          # Download wheels
           .buildkite/scripts/cache-rocm-base-wheels.sh download
-
-          # Set the S3 path for the cached Docker image (for Job 2 to download)
-          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
-          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Mark that we used cache (for Docker image handling)
-          buildkite-agent meta-data set "rocm-used-cache" "true"
-
-          echo ""
-          echo "Cache download complete. Skipping Docker build."
-          echo "Docker image will be downloaded from: $${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
+          
+          # Save ECR tag for downstream jobs
+          buildkite-agent meta-data set "rocm-base-image-tag" "$${ECR_CACHE_TAG}"
+          
+        # Scenario 2: Full rebuild needed
         else
           echo ""
-          echo "CACHE MISS. Building from scratch..."
+          echo " CACHE MISS - Building from scratch..."
           echo ""
-
-          # Build full base image (for later vLLM build)
+          
+          # Build full base image and push to ECR
           DOCKER_BUILDKIT=1 docker buildx build \
             --file docker/Dockerfile.rocm_base \
-            --tag rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} \
-            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
-            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
+            --tag "$${ECR_CACHE_TAG}" \
             --build-arg USE_SCCACHE=1 \
             --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
             --build-arg SCCACHE_REGION_NAME=us-west-2 \
             --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
-            --load \
+            --push \
             .
-
-          # Build debs_wheel_release stage for wheel extraction
+          
+          # Build wheel extraction stage
           DOCKER_BUILDKIT=1 docker buildx build \
             --file docker/Dockerfile.rocm_base \
             --tag rocm-base-debs:$${BUILDKITE_BUILD_NUMBER} \
             --target debs_wheel_release \
-            --build-arg PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
-            --build-arg PYTHON_VERSION="$${PYTHON_VERSION}" \
             --build-arg USE_SCCACHE=1 \
             --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
             --build-arg SCCACHE_REGION_NAME=us-west-2 \
             --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
             --load \
             .
-
-          # Extract wheels from Docker image
+          
+          # Extract and upload wheels
           mkdir -p artifacts/rocm-base-wheels
-          container_id=$$(docker create rocm-base-debs:$${BUILDKITE_BUILD_NUMBER})
-          docker cp $${container_id}:/app/debs/. artifacts/rocm-base-wheels/
-          docker rm $${container_id}
-          echo "Extracted base wheels:"
-          ls -lh artifacts/rocm-base-wheels/
-
-          # Upload wheels to S3 cache for future builds
-          echo ""
-          echo "Uploading wheels to S3 cache..."
+          cid=$(docker create rocm-base-debs:$${BUILDKITE_BUILD_NUMBER})
+          docker cp $${cid}:/app/debs/. artifacts/rocm-base-wheels/
+          docker rm $${cid}
+          
           .buildkite/scripts/cache-rocm-base-wheels.sh upload
 
-          # Export base Docker image for reuse in vLLM build
-          mkdir -p artifacts/rocm-docker-image
-          docker save rocm/vllm-dev:base-$${BUILDKITE_BUILD_NUMBER} | gzip > artifacts/rocm-docker-image/rocm-base-image.tar.gz
-          echo "Docker image size:"
-          ls -lh artifacts/rocm-docker-image/
-
-          # Upload large Docker image to S3 (also cached by cache key)
-          S3_ARTIFACT_PATH="s3://$${S3_BUCKET}/rocm/cache/$${CACHE_KEY}"
-          echo "Uploading Docker image to $${S3_ARTIFACT_PATH}/"
-          aws s3 cp artifacts/rocm-docker-image/rocm-base-image.tar.gz "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Save the S3 path for downstream jobs
-          buildkite-agent meta-data set "rocm-docker-image-s3-path" "$${S3_ARTIFACT_PATH}/rocm-base-image.tar.gz"
-
-          # Mark that we did NOT use cache
-          buildkite-agent meta-data set "rocm-used-cache" "false"
-
+          # Cache base docker image to ECR
+          docker push "$${ECR_CACHE_TAG}"
+          
+          buildkite-agent meta-data set "rocm-base-image-tag" "$${ECR_CACHE_TAG}"
+          
           echo ""
-          echo "Build complete. Wheels cached for future builds."
+          echo " Build complete - Image and wheels cached"
         fi
+        
     artifact_paths:
       - "artifacts/rocm-base-wheels/*.whl"
     env:
@@ -476,13 +463,13 @@ steps:
       S3_BUCKET: "vllm-wheels"
 
   # ROCm Job 2: Build vLLM ROCm Wheel
-  - label: ":python: Build vLLM ROCm Wheel"
+  - label: ":python: Build vLLM ROCm Wheel - x86_64"
     id: build-rocm-vllm-wheel
     depends_on:
       - step: build-rocm-base-wheels
         allow_failure: false
     agents:
-      queue: cpu_queue_postmerge
+      queue: cpu_queue_release
     timeout_in_minutes: 180
     commands:
       # Download artifacts and prepare Docker image
@@ -512,31 +499,25 @@ steps:
         echo "Downloading wheel artifacts from current build"
         buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
 
-        # Download Docker image from S3 (too large for Buildkite artifacts)
-        DOCKER_IMAGE_S3_PATH="$$(buildkite-agent meta-data get rocm-docker-image-s3-path 2>/dev/null || echo '')"
-        if [ -z "$${DOCKER_IMAGE_S3_PATH}" ]; then
-          echo "ERROR: rocm-docker-image-s3-path metadata not found"
+        # Get ECR image tag from metadata (set by build-rocm-base-wheels)
+        ECR_IMAGE_TAG="$$(buildkite-agent meta-data get rocm-base-image-tag 2>/dev/null || echo '')"
+        if [ -z "$${ECR_IMAGE_TAG}" ]; then
+          echo "ERROR: rocm-base-image-tag metadata not found"
           echo "This should have been set by the build-rocm-base-wheels job"
           exit 1
         fi
-        echo "Downloading Docker image from $${DOCKER_IMAGE_S3_PATH}"
-        mkdir -p artifacts/rocm-docker-image
-        aws s3 cp "$${DOCKER_IMAGE_S3_PATH}" artifacts/rocm-docker-image/rocm-base-image.tar.gz
-
-        # Load base Docker image and capture the tag
-        echo "Loading base Docker image..."
-        LOAD_OUTPUT=$$(gunzip -c artifacts/rocm-docker-image/rocm-base-image.tar.gz | docker load)
-        echo "$${LOAD_OUTPUT}"
-        # Extract the actual loaded image tag from "Loaded image: <tag>" output
-        # This avoids picking up stale images (like rocm/vllm-dev:nightly) already on the agent
-        BASE_IMAGE_TAG=$$(echo "$${LOAD_OUTPUT}" | grep "Loaded image:" | sed 's/Loaded image: //')
-        if [ -z "$${BASE_IMAGE_TAG}" ]; then
-          echo "ERROR: Failed to extract image tag from docker load output"
-          echo "Load output was: $${LOAD_OUTPUT}"
-          exit 1
-        fi
-        echo "Loaded base image: $${BASE_IMAGE_TAG}"
-
+        
+        echo "Pulling base Docker image from ECR: $${ECR_IMAGE_TAG}"
+        
+        # Login to ECR
+        aws ecr-public get-login-password --region us-east-1 | \
+          docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+        
+        # Pull base Docker image from ECR
+        docker pull "$${ECR_IMAGE_TAG}"
+        
+        echo "Loaded base image: $${ECR_IMAGE_TAG}"
+        
         # Prepare base wheels for Docker build context
         mkdir -p docker/context/base-wheels
         touch docker/context/base-wheels/.keep
@@ -544,16 +525,11 @@ steps:
         echo "Base wheels for vLLM build:"
         ls -lh docker/context/base-wheels/
 
-        # Get GPU architectures from meta-data
-        PYTORCH_ROCM_ARCH="$$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo '')"
-        PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH:-gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151}"
-
         echo "========================================"
         echo "Building vLLM wheel with:"
         echo "  BUILDKITE_COMMIT: $${BUILDKITE_COMMIT}"
         echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
-        echo "  PYTORCH_ROCM_ARCH: $${PYTORCH_ROCM_ARCH}"
-        echo "  BASE_IMAGE: $${BASE_IMAGE_TAG}"
+        echo "  BASE_IMAGE: $${ECR_IMAGE_TAG}"
         echo "========================================"
 
         # Build vLLM wheel using local checkout (REMOTE_VLLM=0)
@@ -561,8 +537,7 @@ steps:
           --file docker/Dockerfile.rocm \
           --target export_vllm_wheel_release \
           --output type=local,dest=rocm-dist \
-          --build-arg BASE_IMAGE="$${BASE_IMAGE_TAG}" \
-          --build-arg ARG_PYTORCH_ROCM_ARCH="$${PYTORCH_ROCM_ARCH}" \
+          --build-arg BASE_IMAGE="$${ECR_IMAGE_TAG}" \
           --build-arg REMOTE_VLLM=0 \
           --build-arg GIT_REPO_CHECK=1 \
           --build-arg USE_SCCACHE=1 \
@@ -570,10 +545,8 @@ steps:
           --build-arg SCCACHE_REGION_NAME=us-west-2 \
           --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
           .
-
         echo "Built vLLM wheel:"
         ls -lh rocm-dist/*.whl
-
         # Copy wheel to artifacts directory
         mkdir -p artifacts/rocm-vllm-wheel
         cp rocm-dist/*.whl artifacts/rocm-vllm-wheel/
@@ -592,35 +565,13 @@ steps:
       - step: build-rocm-vllm-wheel
         allow_failure: false
     agents:
-      queue: cpu_queue_postmerge
+      queue: cpu_queue_release
     timeout_in_minutes: 60
     commands:
       # Download all wheel artifacts and run upload
       - |
         set -euo pipefail
 
-        # Check if upload is enabled (from env var, meta-data, or release branch)
-        ROCM_UPLOAD_WHEELS="$${ROCM_UPLOAD_WHEELS:-}"
-        if [ -z "$${ROCM_UPLOAD_WHEELS}" ]; then
-          # Try to get from meta-data (input form)
-          ROCM_UPLOAD_WHEELS="$$(buildkite-agent meta-data get rocm-upload-wheels 2>/dev/null || echo '')"
-        fi
-
-        echo "========================================"
-        echo "Upload check:"
-        echo "  ROCM_UPLOAD_WHEELS: $${ROCM_UPLOAD_WHEELS}"
-        echo "  BUILDKITE_BRANCH: $${BUILDKITE_BRANCH}"
-        echo "========================================"
-
-        # Skip upload if not enabled
-        if [ "$${ROCM_UPLOAD_WHEELS}" != "true" ]; then
-          echo "Skipping S3 upload (ROCM_UPLOAD_WHEELS != true, NIGHTLY != 1, not a release branch)"
-          echo "To enable upload, set 'Upload Wheels to S3' to 'Yes' in the build configuration"
-          exit 0
-        fi
-
-        echo "Upload enabled, proceeding..."
-
         # Download artifacts from current build
         echo "Downloading artifacts from current build"
         buildkite-agent artifact download "artifacts/rocm-base-wheels/*.whl" .
@@ -636,11 +587,112 @@ steps:
   - label: ":memo: Annotate ROCm wheel release"
     id: annotate-rocm-release
     depends_on:
-      - step: upload-rocm-wheels
-        allow_failure: true
+      - upload-rocm-wheels
     agents:
-      queue: cpu_queue_postmerge
+      queue: cpu_queue_release
     commands:
       - "bash .buildkite/scripts/annotate-rocm-release.sh"
     env:
       S3_BUCKET: "vllm-wheels"
+
+  # ROCm Job 5: Generate Root Index for ROCm Wheels (for release only)
+  # This is the job to create https://wheels.vllm.ai/rocm/ index allowing
+  # users to install with `uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/`
+  - block: "Generate Root Index for ROCm Wheels for Release"
+    key: block-generate-root-index-rocm-wheels
+    depends_on: upload-rocm-wheels
+
+  - label: ":package: Generate Root Index for ROCm Wheels for Release"
+    depends_on: block-generate-root-index-rocm-wheels
+    id: generate-root-index-rocm-wheels
+    agents:
+      queue: cpu_queue_release
+    commands:
+      - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
+    env:
+      S3_BUCKET: "vllm-wheels"
+      VARIANT: "rocm721"
+
+  # ROCm Job 6: Build ROCm Release Docker Image
+  - label: ":docker: Build release image - x86_64 - ROCm"
+    id: build-rocm-release-image
+    depends_on:
+      - step: build-rocm-base-wheels
+        allow_failure: false
+    agents:
+      queue: cpu_queue_release
+    timeout_in_minutes: 60
+    commands:
+      - |
+        set -euo pipefail
+        
+        # Login to ECR
+        aws ecr-public get-login-password --region us-east-1 | \
+          docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+        
+        # Get ECR image tag from metadata (set by build-rocm-base-wheels)
+        ECR_IMAGE_TAG="$$(buildkite-agent meta-data get rocm-base-image-tag 2>/dev/null || echo '')"
+        if [ -z "$${ECR_IMAGE_TAG}" ]; then
+          echo "ERROR: rocm-base-image-tag metadata not found"
+          echo "This should have been set by the build-rocm-base-wheels job"
+          exit 1
+        fi
+        
+        echo "Pulling base Docker image from ECR: $${ECR_IMAGE_TAG}"
+        
+        # Pull base Docker image from ECR
+        docker pull "$${ECR_IMAGE_TAG}"
+        
+        echo "Loaded base image: $${ECR_IMAGE_TAG}"
+        
+        # Pass the base image ECR tag to downstream steps (nightly publish)
+        buildkite-agent meta-data set "rocm-base-ecr-tag" "$${ECR_IMAGE_TAG}"
+        
+        echo "========================================"
+        echo "Building vLLM ROCm release image with:"
+        echo "  BASE_IMAGE: $${ECR_IMAGE_TAG}"
+        echo "  BUILDKITE_COMMIT: $${BUILDKITE_COMMIT}"
+        echo "========================================"
+        
+        # Build vLLM ROCm release image using cached base
+        DOCKER_BUILDKIT=1 docker build \
+          --build-arg max_jobs=16 \
+          --build-arg BASE_IMAGE="$${ECR_IMAGE_TAG}" \
+          --build-arg USE_SCCACHE=1 \
+          --build-arg SCCACHE_BUCKET_NAME=vllm-build-sccache \
+          --build-arg SCCACHE_REGION_NAME=us-west-2 \
+          --build-arg SCCACHE_S3_NO_CREDENTIALS=0 \
+          --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm \
+          --target vllm-openai \
+          --progress plain \
+          -f docker/Dockerfile.rocm .
+        
+        # Push to ECR
+        docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm
+        
+        echo ""
+        echo " Successfully built and pushed ROCm release image"
+        echo "   Image: public.ecr.aws/q9t5s3a7/vllm-release-repo:$${BUILDKITE_COMMIT}-rocm"
+        echo ""
+    env:
+      DOCKER_BUILDKIT: "1"
+      S3_BUCKET: "vllm-wheels"
+
+  - label: "Publish nightly ROCm image to DockerHub"
+    depends_on:
+      - build-rocm-release-image
+    if: build.env("NIGHTLY") == "1"
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/scripts/push-nightly-builds-rocm.sh"
+      # Clean up old nightly builds (keep only last 14)
+      - "bash .buildkite/scripts/cleanup-nightly-builds.sh nightly- vllm/vllm-openai-rocm"
+      - "bash .buildkite/scripts/cleanup-nightly-builds.sh base-nightly- vllm/vllm-openai-rocm"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"

.buildkite/scripts/annotate-release.sh

@@ -8,31 +8,41 @@ if [ -z "${RELEASE_VERSION}" ]; then
   RELEASE_VERSION="1.0.0.dev"
 fi
 
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
 
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
+(Optional) For CUDA 13.0:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+
+(Optional) For CPU:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-To download the wheel (by version):
-\`\`\`
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux2014_aarch64.whl .
-
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu129/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu130/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux1_x86_64.whl .
-\`\`\`
 
 To download and upload the image:
 
 \`\`\`
+# Download images:
+
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+# Tag and push images:
+
+## CUDA
 
 docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
 docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
@@ -40,22 +50,70 @@ docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
 docker push vllm/vllm-openai:latest-x86_64
 docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
 
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
+docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+docker push vllm/vllm-openai:latest-x86_64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
+
 docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
 docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
 docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 docker push vllm/vllm-openai:latest-aarch64
 docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai:rocm
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:latest-rocm
-docker tag vllm/vllm-openai:rocm vllm/vllm-openai:v${RELEASE_VERSION}-rocm
-docker push vllm/vllm-openai:latest-rocm
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-rocm
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker push vllm/vllm-openai:latest-aarch64-cu130
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+
+## ROCm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+## CPU
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-cpu:latest-x86_64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker push vllm/vllm-openai-cpu:latest-arm64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+# Create multi-arch manifest:
 
 docker manifest rm vllm/vllm-openai:latest
 docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
 docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
 docker manifest push vllm/vllm-openai:latest
 docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+docker manifest rm vllm/vllm-openai:latest-cu130
+docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
+docker manifest push vllm/vllm-openai:latest-cu130
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
+
+docker manifest rm vllm/vllm-openai-cpu:latest || true
+docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker manifest push vllm/vllm-openai-cpu:latest
+docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
 \`\`\`
-EOF 
+EOF

.buildkite/scripts/annotate-rocm-release.sh

@@ -3,25 +3,33 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 #
 # Generate Buildkite annotation for ROCm wheel release
-
 set -ex
 
-# Get build configuration from meta-data
+# Extract build configuration from Dockerfile.rocm_base (single source of truth)
 # Extract ROCm version dynamically from Dockerfile.rocm_base
-# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.1-complete -> extracts "7.1"
+# BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.0-complete -> extracts "7.0"
 ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
-PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
-PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
+PYTHON_VERSION=$(grep '^ARG PYTHON_VERSION=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTHON_VERSION=//')
+PYTORCH_ROCM_ARCH=$(grep '^ARG PYTORCH_ROCM_ARCH=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTORCH_ROCM_ARCH=//')
+
+# Get release version, default to 1.0.0.dev for nightly/per-commit builds
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null || echo "")
+if [ -z "${RELEASE_VERSION}" ]; then
+  RELEASE_VERSION="1.0.0.dev"
+fi
+
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
 
 # S3 URLs
 S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
 S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
-S3_URL="https://${S3_BUCKET}.s3.${S3_REGION}.amazonaws.com"
-ROCM_PATH="rocm/${BUILDKITE_COMMIT}"
+S3_URL="http://${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com"
 
+# Format ROCm version for path (e.g., "7.1" -> "rocm710")
+ROCM_VERSION_PATH="rocm$(echo "${ROCM_VERSION}" | tr -d '.')"
+ROCM_PATH="rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}"
 buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' << EOF
-## :rocm: ROCm Wheel Release
-
+## ROCm Wheel and Docker Image Releases
 ### Build Configuration
 | Setting | Value |
 |---------|-------|
@@ -34,41 +42,72 @@ buildkite-agent annotate --style 'success' --context 'rocm-release-workflow' <<
 ### :package: Installation
 
 **Install from this build (by commit):**
-\`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/{rocm_variant}/
 
-# Example:
-uv pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/rocm700/
+\`\`\`bash
+pip install vllm --extra-index-url ${S3_URL}/${ROCM_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
+
+# Example for ROCm ${ROCM_VERSION}:
+pip install vllm --extra-index-url ${S3_URL}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
 \`\`\`
 
 **Install from nightly (if published):**
+
 \`\`\`bash
-uv pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/
+pip install vllm --extra-index-url ${S3_URL}/rocm/nightly/ --trusted-host ${S3_BUCKET}.s3-website-${S3_REGION}.amazonaws.com
 \`\`\`
 
 ### :floppy_disk: Download Wheels Directly
 
 \`\`\`bash
 # List all ROCm wheels
-aws s3 ls s3://${S3_BUCKET}/${ROCM_PATH}/
-
+aws s3 ls s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/
 # Download specific wheels
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/vllm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torch-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/triton_rocm-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/torchvision-*.whl .
-aws s3 cp s3://${S3_BUCKET}/${ROCM_PATH}/amdsmi-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/vllm-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torch-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton-kernels-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchvision-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchaudio-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amdsmi-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amd_aiter-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-attn-*.whl .
 \`\`\`
 
 ### :gear: Included Packages
 - **vllm**: vLLM with ROCm support
 - **torch**: PyTorch built for ROCm ${ROCM_VERSION}
-- **triton_rocm**: Triton built for ROCm
+- **triton**: Triton
+- **triton-kernels**: Triton kernels
 - **torchvision**: TorchVision for ROCm PyTorch
+- **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
+- **amd_aiter**: Aiter for ROCm
+- **flash-attn**: Flash Attention for ROCm
 
 ### :warning: Notes
 - These wheels are built for **ROCm ${ROCM_VERSION}** and will NOT work with CUDA GPUs
 - Supported GPU architectures: ${PYTORCH_ROCM_ARCH}
 - Platform: Linux x86_64 only
+
+### :package: Docker Image Release
+
+To download and upload the image:
+
+\`\`\`
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+\`\`\`
+
 EOF

.buildkite/scripts/cache-rocm-base-wheels.sh

@@ -15,8 +15,6 @@
 #
 # Environment variables:
 #   S3_BUCKET          - S3 bucket name (default: vllm-wheels)
-#   PYTHON_VERSION     - Python version (affects cache key)
-#   PYTORCH_ROCM_ARCH  - GPU architectures (affects cache key)
 #
 # Note: ROCm version is determined by BASE_IMAGE in Dockerfile.rocm_base,
 #       so changes to ROCm version are captured by the Dockerfile hash.
@@ -36,13 +34,7 @@ generate_cache_key() {
     fi
     local dockerfile_hash=$(sha256sum "$DOCKERFILE" | cut -c1-16)
 
-    # Include key build args that affect the output
-    # These should match the ARGs in Dockerfile.rocm_base that change the build output
-    # Note: ROCm version is determined by BASE_IMAGE in the Dockerfile, so it's captured by dockerfile_hash
-    local args_string="${PYTHON_VERSION:-}|${PYTORCH_ROCM_ARCH:-}"
-    local args_hash=$(echo "$args_string" | sha256sum | cut -c1-8)
-
-    echo "${dockerfile_hash}-${args_hash}"
+    echo "${dockerfile_hash}"
 }
 
 CACHE_KEY=$(generate_cache_key)
@@ -52,9 +44,6 @@ case "${1:-}" in
     check)
         echo "Checking cache for key: ${CACHE_KEY}" >&2
         echo "Cache path: ${CACHE_PATH}" >&2
-        echo "Variables used in cache key:" >&2
-        echo "  PYTHON_VERSION: ${PYTHON_VERSION:-<not set>}" >&2
-        echo "  PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH:-<not set>}" >&2
 
         # Check if cache exists by listing objects
         # We look for at least one .whl file
@@ -83,7 +72,7 @@ case "${1:-}" in
             exit 1
         fi
 
-        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
+        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
         if [[ "$WHEEL_COUNT" -eq 0 ]]; then
             echo "ERROR: No wheels found in artifacts/rocm-base-wheels/" >&2
             exit 1
@@ -104,15 +93,17 @@ case "${1:-}" in
         echo "Cache key: ${CACHE_KEY}"
         echo "Cache path: ${CACHE_PATH}"
         echo ""
-
         mkdir -p artifacts/rocm-base-wheels
-        aws s3 cp --recursive "${CACHE_PATH}" artifacts/rocm-base-wheels/
-
+        
+        # Use sync with include/exclude to only download .whl files
+        aws s3 sync "${CACHE_PATH}" artifacts/rocm-base-wheels/ \
+            --exclude "*" \
+            --include "*.whl"
+        
         echo ""
         echo "Downloaded wheels:"
-        ls -lh artifacts/rocm-base-wheels/
-
-        WHEEL_COUNT=$(ls artifacts/rocm-base-wheels/*.whl 2>/dev/null | wc -l)
+        find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' -exec ls -lh {} \;
+        WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
         echo ""
         echo "Total: $WHEEL_COUNT wheels"
         echo "========================================"

.buildkite/scripts/check-ray-compatibility.sh

@@ -0,0 +1,235 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Check if Ray LLM can generate lock files that are compatible with this
+# version of vllm. Downloads Ray's requirement files and runs a full
+# dependency resolution with the installed vllm's constraints to see if
+# a valid lock file can be produced.
+#
+# See: https://github.com/vllm-project/vllm/issues/33599
+
+set -eo pipefail
+
+RAY_BASE_URL="https://raw.githubusercontent.com/ray-project/ray/master/python"
+
+WORK_DIR=$(mktemp -d)
+trap 'rm -rf "$WORK_DIR"' EXIT
+
+# ── Detect PyTorch index URL ─────────────────────────────────────────────
+
+if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
+    ROCM_VER=$(python3 -c "import torch; print(torch.version.hip.rsplit('.', 1)[0])")
+    CANDIDATE_URL="https://download.pytorch.org/whl/rocm${ROCM_VER}"
+    if curl -fsSL --head "${CANDIDATE_URL}/" >/dev/null 2>&1; then
+        TORCH_INDEX_URL="${CANDIDATE_URL}"
+    else
+        echo ">>> WARNING: ROCm ${ROCM_VER} wheel index not found at ${CANDIDATE_URL}"
+        echo ">>>          Falling back to default PyPI (resolution may be incomplete)"
+        TORCH_INDEX_URL=""
+    fi
+else
+    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu129"
+fi
+echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
+
+# Fetch all Ray requirement files used in the LLM depset pipeline
+echo ">>> Fetching Ray requirement files"
+RAY_FILES=(
+    "requirements.txt"
+    "requirements/cloud-requirements.txt"
+    "requirements/base-test-requirements.txt"
+    "requirements/llm/llm-requirements.txt"
+    "requirements/llm/llm-test-requirements.txt"
+)
+for FILE in "${RAY_FILES[@]}"; do
+    LOCAL_PATH="${WORK_DIR}/$(basename "$FILE")"
+    echo "    ${FILE}"
+    curl -fsSL -o "$LOCAL_PATH" "${RAY_BASE_URL}/${FILE}"
+done
+
+# Extract installed vllm deps
+echo ">>> Extracting installed vllm dependency constraints"
+python3 - "${WORK_DIR}/vllm-constraints.txt" <<'PYEOF'
+"""Write out the installed vllm's dependencies as pip constraint lines.
+
+Ray uses vllm[audio], so audio-extra deps are included with their extra
+markers stripped. The resolver cannot evaluate extra markers for a
+package that is not itself being resolved from an index, so we activate
+them manually here.
+"""
+import importlib.metadata
+import re
+import sys
+
+out_path = sys.argv[1]
+raw_reqs = importlib.metadata.requires("vllm") or []
+
+# Ray uses vllm[audio] – activate that extra.
+ACTIVE_EXTRAS = {"audio"}
+EXTRA_RE = re.compile(r"""extra\s*==\s*['"]([^'"]+)['"]""")
+
+lines = []
+for r in raw_reqs:
+    if ";" not in r:
+        # Unconditional dep — always include.
+        lines.append(r.strip())
+        continue
+
+    req_part, _, marker_part = r.partition(";")
+    marker_part = marker_part.strip()
+
+    extra_matches = EXTRA_RE.findall(marker_part)
+    if not extra_matches:
+        # Non-extra marker (python_version, etc.) — keep as-is.
+        lines.append(r.strip())
+        continue
+
+    if not ACTIVE_EXTRAS.intersection(extra_matches):
+        continue  # Skip inactive extras (tensorizer, bench, …).
+
+    # Strip the extra== conditions but keep any remaining markers
+    # (e.g. python_version).
+    cleaned = EXTRA_RE.sub("", marker_part)
+    cleaned = re.sub(r"\band\b\s*\band\b", "and", cleaned)
+    cleaned = re.sub(r"^\s*and\s+|\s+and\s*$", "", cleaned).strip()
+
+    if cleaned:
+        lines.append(f"{req_part.strip()} ; {cleaned}")
+    else:
+        lines.append(req_part.strip())
+
+with open(out_path, "w") as f:
+    for line in lines:
+        f.write(line + "\n")
+
+print(f"Wrote {len(lines)} constraints to {out_path}")
+PYEOF
+
+echo ">>> Installed vllm deps (first 20 lines):"
+head -20 "${WORK_DIR}/vllm-constraints.txt"
+
+# Remove Ray's vllm pin — the installed vllm's transitive deps
+# (written above) replace it in the resolution. vllm itself cannot
+# be resolved from PyPI for in-development versions, so we test
+# whether Ray's requirements can coexist with vllm's dependency
+# constraints instead.
+sed -i '/^vllm/d' "${WORK_DIR}/llm-requirements.txt"
+
+# Install uv if needed
+if ! command -v uv &>/dev/null; then
+    echo ">>> Installing uv"
+    pip install uv -q
+fi
+
+# Resolve: given vllm's constraints, can Ray compile a lock file?
+#
+# vllm's dependency constraints are the fixed side — Ray is flexible and
+# can regenerate its lock files. We pass vllm's constraints via -c so
+# the resolver treats them as non-negotiable bounds, then check whether
+# Ray's own requirements can still be satisfied within those bounds.
+echo ""
+echo "============================================================"
+echo ">>> Resolving: Can Ray generate compatible lock files?"
+echo "============================================================"
+
+EXTRA_INDEX_ARGS=()
+if [[ -n "${TORCH_INDEX_URL}" ]]; then
+    EXTRA_INDEX_ARGS+=(--extra-index-url "${TORCH_INDEX_URL}")
+fi
+
+set +e
+uv pip compile \
+    "${WORK_DIR}/requirements.txt" \
+    "${WORK_DIR}/cloud-requirements.txt" \
+    "${WORK_DIR}/base-test-requirements.txt" \
+    "${WORK_DIR}/llm-requirements.txt" \
+    "${WORK_DIR}/llm-test-requirements.txt" \
+    -c "${WORK_DIR}/vllm-constraints.txt" \
+    --python-version 3.12 \
+    --python-platform x86_64-manylinux_2_31 \
+    "${EXTRA_INDEX_ARGS[@]}" \
+    --index-strategy unsafe-best-match \
+    --unsafe-package setuptools \
+    --unsafe-package ray \
+    --no-header \
+    -o "${WORK_DIR}/resolved.txt" \
+    2>&1
+EXIT_CODE=$?
+set -e
+
+echo ""
+echo "=========================================="
+if [ $EXIT_CODE -eq 0 ]; then
+    echo "SUCCESS: Ray can generate lock files compatible with this vllm."
+    echo ""
+    echo "Key resolved versions:"
+    grep -E '^(protobuf|torch|numpy|transformers)==' \
+        "${WORK_DIR}/resolved.txt" | sort || true
+    echo "=========================================="
+    exit 0
+fi
+
+echo "FAILURE: Ray cannot generate lock files compatible with this vllm."
+echo "This means a fundamental dependency conflict exists that Ray"
+echo "cannot resolve by regenerating its lock files."
+echo "See: https://github.com/vllm-project/vllm/issues/33599"
+echo "=========================================="
+
+# Buildkite annotation
+if [ -f /usr/bin/buildkite-agent ]; then
+    buildkite-agent annotate --style 'warning' --context 'ray-compat' << EOF
+### :warning: Ray Dependency Compatibility Warning
+This PR introduces dependencies that **cannot** be resolved with Ray's requirements.
+Ray would not be able to regenerate its lock files to accommodate this vllm version.
+
+Please check the **Ray Dependency Compatibility Check** step logs for details.
+See [issue #33599](https://github.com/vllm-project/vllm/issues/33599) for context.
+EOF
+fi
+
+# Notify Slack if webhook is configured and PR/branch are valid.
+if [ -n "$RAY_COMPAT_SLACK_WEBHOOK_URL" ]; then
+    PR="${BUILDKITE_PULL_REQUEST:-}"
+    BRANCH="${BUILDKITE_BRANCH:-}"
+
+    # Skip notification if PR is invalid or branch is empty
+    if [[ "$PR" = "false" || -z "$PR" || -z "$BRANCH" ]]; then
+        echo ">>> Skipping Slack notification (invalid PR or empty branch: PR=$PR, branch=$BRANCH)"
+    else
+        echo ">>> Sending Slack notification"
+        # Single quotes are intentional: the f-string expressions are Python, not shell.
+        # shellcheck disable=SC2016
+        PAYLOAD=$(python3 -c '
+import json, os, sys
+pr = os.getenv("BUILDKITE_PULL_REQUEST", "N/A")
+branch = os.getenv("BUILDKITE_BRANCH", "unknown")
+url = os.getenv("BUILDKITE_BUILD_URL", "#")
+data = {
+    "text": ":warning: Ray Dependency Compatibility Check Failed",
+    "blocks": [{
+        "type": "section",
+        "text": {
+            "type": "mrkdwn",
+            "text": (
+                "*:warning: Ray Dependency Compatibility Check Failed*\n"
+                f"PR #{pr} on branch `{branch}` introduces dependencies "
+                f"that cannot be resolved with Ray'\''s requirements.\n"
+                f"<{url}|View Build>"
+            ),
+        },
+    }],
+}
+print(json.dumps(data))
+')
+
+        HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST "$RAY_COMPAT_SLACK_WEBHOOK_URL" \
+            -H 'Content-type: application/json' \
+            -d "$PAYLOAD")
+        echo "    Slack webhook response: $HTTP_CODE"
+    fi
+else
+    echo ">>> Skipping Slack notification (RAY_COMPAT_SLACK_WEBHOOK_URL not set)"
+fi
+
+exit 1

.buildkite/scripts/cherry-pick-from-milestone.sh

@@ -134,7 +134,7 @@ log_info "Fetching merged PRs from milestone '${MILESTONE}'..."
 
 # Store PR data in a temp file
 PR_DATA=$(mktemp)
-trap "rm -f $PR_DATA" EXIT
+trap 'rm -f "$PR_DATA"' EXIT
 
 if ! gh pr list --state merged --search "milestone:${MILESTONE}" \
     --limit 1000 \

.buildkite/scripts/cleanup-nightly-builds.sh

@@ -4,16 +4,19 @@ set -ex
 
 # Clean up old nightly builds from DockerHub, keeping only the last 14 builds
 # This script uses DockerHub API to list and delete old tags with specified prefix
-# Usage: cleanup-nightly-builds.sh [TAG_PREFIX]
-# Example: cleanup-nightly-builds.sh "nightly-" or cleanup-nightly-builds.sh "cu130-nightly-"
+# Usage: cleanup-nightly-builds.sh [TAG_PREFIX] [REPO]
+# Example: cleanup-nightly-builds.sh "nightly-"
+# Example: cleanup-nightly-builds.sh "cu130-nightly-"
+# Example: cleanup-nightly-builds.sh "nightly-" "vllm/vllm-openai-rocm"
 
-# Get tag prefix from argument, default to "nightly-" if not provided
+# Get tag prefix and repo from arguments
 TAG_PREFIX="${1:-nightly-}"
+REPO="${2:-vllm/vllm-openai}"
 
-echo "Cleaning up tags with prefix: $TAG_PREFIX"
+echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
 
-# DockerHub API endpoint for vllm/vllm-openai repository
-REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
+# DockerHub API endpoint for the repository
+REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
 
 # Get DockerHub credentials from environment
 if [ -z "$DOCKERHUB_TOKEN" ]; then
@@ -70,7 +73,7 @@ delete_tag() {
     local tag_name="$1"
     echo "Deleting tag: $tag_name"
     
-    local delete_url="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags/$tag_name"
+    local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
     set +x
     local response=$(curl -s -X DELETE -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
     set -x

.buildkite/scripts/generate-and-upload-nightly-index.sh

@@ -0,0 +1,84 @@
+#!/usr/bin/env bash
+
+set -ex
+
+# Generate and upload wheel indices for all wheels in the commit directory.
+# This script should run once after all wheels have been built and uploaded.
+
+# ======== setup ========
+
+BUCKET="vllm-wheels"
+INDICES_OUTPUT_DIR="indices"
+DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+PYTHON="${PYTHON_PROG:-python3}" # try to read from env var, otherwise use python3
+SUBPATH=$BUILDKITE_COMMIT
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+# detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
+if [[ "$has_new_python" -eq 0 ]]; then
+    # use new python from docker
+    docker pull python:3-slim
+    PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
+
+# ======== generate and upload indices ========
+
+# list all wheels in the commit directory
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+obj_json="objects.json"
+aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
+mkdir -p "$INDICES_OUTPUT_DIR"
+
+# call script to generate indices for all existing wheels
+# these indices have relative paths that work as long as they are next to the wheel directory in s3
+# i.e., the wheels are always in s3://vllm-wheels/<commit>/
+# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
+alias_args=()
+if [[ -n "$DEFAULT_VARIANT_ALIAS" ]]; then
+    alias_args=(--alias-to-default "$DEFAULT_VARIANT_ALIAS")
+fi
+
+# HACK: we do not need regex module here, but it is required by pre-commit hook
+# To avoid any external dependency, we simply replace it back to the stdlib re module
+sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
+$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" "${alias_args[@]}"
+
+# copy indices to /<commit>/ unconditionally
+echo "Uploading indices to $S3_COMMIT_PREFIX"
+aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
+
+# copy to /nightly/ only if it is on the main branch and not a PR
+if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
+    echo "Uploading indices to overwrite /nightly/"
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
+fi
+
+# detect version from any wheel in the commit directory
+# download the first wheel we find to extract version metadata
+first_wheel_key=$($PYTHON -c "import json; obj=json.load(open('$obj_json')); print(next((c['Key'] for c in obj.get('Contents', []) if c['Key'].endswith('.whl')), ''))")
+if [[ -z "$first_wheel_key" ]]; then
+    echo "Error: No wheels found in $S3_COMMIT_PREFIX"
+    exit 1
+fi
+first_wheel=$(basename "$first_wheel_key")
+aws s3 cp "s3://$BUCKET/${first_wheel_key}" "/tmp/${first_wheel}"
+version=$(unzip -p "/tmp/${first_wheel}" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
+rm -f "/tmp/${first_wheel}"
+echo "Version in wheel: $version"
+pure_version="${version%%+*}"
+echo "Pure version (without variant): $pure_version"
+
+# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
+if [[ "$version" != *"dev"* ]]; then
+    echo "Re-generating indices for /$pure_version/"
+    rm -rf "${INDICES_OUTPUT_DIR:?}"
+    mkdir -p "$INDICES_OUTPUT_DIR"
+    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
+    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" "${alias_args[@]}"
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
+fi

.buildkite/scripts/generate-nightly-index.py

@@ -112,7 +112,7 @@ def parse_from_filename(file: str) -> WheelFileInfo:
 
 def generate_project_list(subdir_names: list[str], comment: str = "") -> str:
     """
-    Generate project list HTML content linking to each project & variant sub-directory.
+    Generate project list HTML content linking to each project & variant subdirectory.
     """
     href_tags = []
     for name in sorted(subdir_names):
@@ -168,23 +168,23 @@ def generate_index_and_metadata(
         comment (str | None): Optional comment to include in the generated HTML files.
 
     First, parse all wheel files to extract metadata.
-    We need to collect all wheel files for each variant, and generate an index for it (in a sub-directory).
+    We need to collect all wheel files for each variant, and generate an index for it (in a subdirectory).
     The index for the default variant (if any) is generated in the root index directory.
 
     If `default_variant` is provided, all wheels must have variant suffixes, and the default variant index
     is purely a copy of the corresponding variant index, with only the links adjusted.
     Otherwise, all wheels without variant suffixes are treated as the default variant.
 
-    If `alias_to_default` is provided, an additional alias sub-directory is created, it has the same content
+    If `alias_to_default` is provided, an additional alias subdirectory is created, it has the same content
     as the default variant index, but the links are adjusted accordingly.
 
     Index directory structure:
         index_base_dir/ (hosted at wheels.vllm.ai/{nightly,$commit,$version}/)
-            index.html  # project list, linking to "vllm/" and other packages, and all variant sub-directories
+            index.html  # project list, linking to "vllm/" and other packages, and all variant subdirectories
             vllm/
                 index.html # package index, pointing to actual files in wheel_base_dir (relative path)
                 metadata.json # machine-readable metadata for all wheels in this package
-            cpu/ # cpu variant sub-directory
+            cpu/ # cpu variant subdirectory
                 index.html
                 vllm/
                     index.html
@@ -194,7 +194,7 @@ def generate_index_and_metadata(
                 vllm/
                     index.html
                     metadata.json
-            cu130/ # cu130 variant sub-directory
+            cu130/ # cu130 variant subdirectory
                 index.html
                 vllm/
                     index.html

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -1,25 +1,40 @@
 #!/bin/bash
 
-# This script runs test inside the corresponding ROCm docker container.
+# This script runs tests inside the corresponding ROCm docker container.
+# It handles both single-node and multi-node test configurations.
+#
+# Multi-node detection: Instead of matching on fragile group names, we detect
+# multi-node jobs structurally by looking for the bracket command syntax
+# "[node0_cmds] && [node1_cmds]" or via the NUM_NODES environment variable.
+#
+###############################################################################
+# QUOTING / COMMAND PASSING
+#
+# Passing commands as positional arguments ($*) is fragile when the command
+# string itself contains double quotes, e.g.:
+#
+#   bash run-amd-test.sh "export FLAGS="value" && pytest -m "not slow""
+#
+# The outer shell resolves the nested quotes *before* this script runs, so
+# the script receives mangled input it cannot fully recover.
+#
+# Preferred: pass commands via the VLLM_TEST_COMMANDS environment variable:
+#
+#   export VLLM_TEST_COMMANDS='export FLAGS="value" && pytest -m "not slow"'
+#   bash run-amd-test.sh
+#
+# Single-quoted assignment preserves all inner double quotes verbatim.
+# The $* path is kept for backward compatibility but callers should migrate.
+###############################################################################
 set -o pipefail
 
 # Export Python path
 export PYTHONPATH=".."
 
-# Print ROCm version
-echo "--- Confirming Clean Initial State"
-while true; do
-        sleep 3
-        if grep -q clean /opt/amdgpu/etc/gpu_state; then
-                echo "GPUs state is \"clean\""
-                break
-        fi
-done
+###############################################################################
+# Helper Functions
+###############################################################################
 
-echo "--- ROCm info"
-rocminfo
-
-# cleanup older docker images
 cleanup_docker() {
   # Get Docker's root directory
   docker_root=$(docker info -f '{{.DockerRootDir}}')
@@ -28,15 +43,12 @@ cleanup_docker() {
     exit 1
   fi
   echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
+
   disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
   threshold=70
   if [ "$disk_usage" -gt "$threshold" ]; then
     echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
     docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
     docker volume prune -f && docker system prune --force --filter "until=72h" --all
     echo "Docker images and volumes cleanup completed."
   else
@@ -44,201 +56,443 @@ cleanup_docker() {
   fi
 }
 
-# Call the cleanup docker function
+cleanup_network() {
+  local max_nodes=${NUM_NODES:-2}
+  for node in $(seq 0 $((max_nodes - 1))); do
+    if docker ps -a -q -f name="node${node}" | grep -q .; then
+      docker stop "node${node}" || true
+    fi
+  done
+  if docker network ls | grep -q docker-net; then
+    docker network rm docker-net || true
+  fi
+}
+
+is_multi_node() {
+  local cmds="$1"
+  # Primary signal: NUM_NODES environment variable set by the pipeline
+  if [[ "${NUM_NODES:-1}" -gt 1 ]]; then
+    return 0
+  fi
+  # Fallback: detect the bracket syntax structurally
+  # Pattern: [...] && [...] (per-node command arrays)
+  if [[ "$cmds" =~ \[.*\].*\&\&.*\[.*\] ]]; then
+    return 0
+  fi
+  return 1
+}
+
+handle_pytest_exit() {
+  local exit_code=$1
+  if [ "$exit_code" -eq 5 ]; then
+    echo "Pytest exit code 5 (no tests collected) - treating as success."
+    exit 0
+  fi
+  exit "$exit_code"
+}
+
+###############################################################################
+# Pytest marker/keyword re-quoting
+#
+# When commands are passed through Buildkite -> shell -> $* -> bash -c,
+# quotes around multi-word pytest -m/-k expressions get stripped:
+#   pytest -v -s -m 'not cpu_test' v1/core
+# becomes:
+#   pytest -v -s -m not cpu_test v1/core
+#
+# pytest then interprets "cpu_test" as a file path, not part of the marker.
+#
+# This function detects unquoted expressions after -m/-k and re-quotes them
+# by collecting tokens until a recognizable boundary is reached:
+#   - test path (contains '/')
+#   - test file (ends with '.py')
+#   - another pytest flag (--xxx or -x single-char flags)
+#   - command separator (&& || ; |)
+#   - environment variable assignment (FOO=bar)
+#
+# Single-word markers (e.g. -m cpu_test, -m hybrid_model) pass through
+# unquoted since they have no spaces and work fine.
+#
+# Already-quoted expressions (containing literal single quotes) are passed
+# through untouched to avoid double-quoting values injected by
+# apply_rocm_test_overrides.
+#
+# NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
+# double-quotes stripped by the calling shell (see header comment).
+# Use VLLM_TEST_COMMANDS to avoid the problem entirely.
+###############################################################################
+re_quote_pytest_markers() {
+  local input="$1"
+  local output=""
+  local collecting=false
+  local marker_buf=""
+
+  # Strip backslash-newline continuations, then flatten remaining newlines
+  local flat="${input//$'\\\n'/ }"
+  flat="${flat//$'\n'/ }"
+
+  # Disable globbing to prevent *.py etc. from expanding during read -ra
+  local restore_glob
+  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
+  set -o noglob
+  local -a words
+  read -ra words <<< "$flat"
+  eval "$restore_glob"
+
+  for word in "${words[@]}"; do
+    if $collecting; then
+      # If the token we're about to collect already contains a literal
+      # single quote, the expression was already quoted upstream.
+      # Flush and stop collecting.
+      if [[ "$word" == *"'"* ]]; then
+        if [[ -n "$marker_buf" ]]; then
+          # Should not normally happen (partial buf + quote), flush raw
+          output+="${marker_buf} "
+          marker_buf=""
+        fi
+        output+="${word} "
+        collecting=false
+        continue
+      fi
+
+      local is_boundary=false
+      case "$word" in
+        # Line-continuation artifact
+        "\\")
+          is_boundary=true ;;
+        # Command separators
+        "&&"|"||"|";"|"|")
+          is_boundary=true ;;
+        # Long flags (--ignore, --shard-id, etc.)
+        --*)
+          is_boundary=true ;;
+        # Short flags (-v, -s, -x, etc.) but NOT negative marker tokens
+        # like "not" which don't start with "-". Also skip -k/-m which
+        # would start a new marker (handled below).
+        -[a-zA-Z])
+          is_boundary=true ;;
+        # Test path (contains /)
+        */*)
+          is_boundary=true ;;
+        # Test file (ends with .py, possibly with ::method)
+        *.py|*.py::*)
+          is_boundary=true ;;
+        # Environment variable assignment preceding a command (FOO=bar)
+        *=*)
+          # Only treat as boundary if it looks like VAR=value, not
+          # pytest filter expressions like num_gpus=2 inside markers
+          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
+            is_boundary=true
+          fi
+          ;;
+      esac
+
+      if $is_boundary; then
+        # Strip surrounding double quotes if present (from upstream
+        # single-to-double conversion); without this, wrapping below
+        # would produce '"expr"' with literal double-quote characters.
+        if [[ "$marker_buf" == '"'*'"' ]]; then
+          marker_buf="${marker_buf#\"}"
+          marker_buf="${marker_buf%\"}"
+        fi
+        # Flush the collected marker expression
+        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+          output+="'${marker_buf}' "
+        else
+          output+="${marker_buf} "
+        fi
+        collecting=false
+        marker_buf=""
+        # Check if this boundary word itself starts a new -m/-k
+        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
+          output+="${word} "
+          collecting=true
+        # Drop stray backslash tokens silently
+        elif [[ "$word" == "\\" ]]; then
+          :
+        else
+          output+="${word} "
+        fi
+      else
+        # Accumulate into marker buffer
+        if [[ -n "$marker_buf" ]]; then
+          marker_buf+=" ${word}"
+        else
+          marker_buf="${word}"
+        fi
+      fi
+    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
+      output+="${word} "
+      collecting=true
+      marker_buf=""
+    else
+      output+="${word} "
+    fi
+  done
+
+  # Flush any trailing marker expression (marker at end of command)
+  if $collecting && [[ -n "$marker_buf" ]]; then
+    # Strip surrounding double quotes (see mid-stream flush comment)
+    if [[ "$marker_buf" == '"'*'"' ]]; then
+      marker_buf="${marker_buf#\"}"
+      marker_buf="${marker_buf%\"}"
+    fi
+    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+      output+="'${marker_buf}'"
+    else
+      output+="${marker_buf}"
+    fi
+  fi
+
+  echo "${output% }"
+}
+
+###############################################################################
+# ROCm-specific pytest command rewrites
+#
+# These apply ignore flags and environment overrides for tests that are not
+# yet supported or behave differently on ROCm hardware. Kept as a single
+# function so new exclusions are easy to add in one place.
+###############################################################################
+
+apply_rocm_test_overrides() {
+  local cmds="$1"
+
+  # --- Model registry filter ---
+  if [[ $cmds == *"pytest -v -s models/test_registry.py"* ]]; then
+    cmds=${cmds//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+  fi
+
+  # --- LoRA: disable custom paged attention ---
+  if [[ $cmds == *"pytest -v -s lora"* ]]; then
+    cmds=${cmds//"pytest -v -s lora"/"pytest -v -s lora"}
+  fi
+
+  # --- Kernel ignores ---
+  if [[ $cmds == *" kernels/core"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/core/test_fused_quant_layernorm.py \
+    --ignore=kernels/core/test_permute_cols.py"
+  fi
+
+  if [[ $cmds == *" kernels/attention"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/attention/test_attention_selector.py \
+    --ignore=kernels/attention/test_encoder_decoder_attn.py \
+    --ignore=kernels/attention/test_flash_attn.py \
+    --ignore=kernels/attention/test_flashinfer.py \
+    --ignore=kernels/attention/test_prefix_prefill.py \
+    --ignore=kernels/attention/test_cascade_flash_attn.py \
+    --ignore=kernels/attention/test_mha_attn.py \
+    --ignore=kernels/attention/test_lightning_attn.py \
+    --ignore=kernels/attention/test_attention.py"
+  fi
+
+  if [[ $cmds == *" kernels/quantization"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/quantization/test_int8_quant.py \
+    --ignore=kernels/quantization/test_machete_mm.py \
+    --ignore=kernels/quantization/test_block_fp8.py \
+    --ignore=kernels/quantization/test_block_int8.py \
+    --ignore=kernels/quantization/test_marlin_gemm.py \
+    --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+    --ignore=kernels/quantization/test_int8_kernel.py"
+  fi
+
+  if [[ $cmds == *" kernels/mamba"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/mamba/test_mamba_mixer2.py \
+    --ignore=kernels/mamba/test_causal_conv1d.py \
+    --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+  fi
+
+  if [[ $cmds == *" kernels/moe"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/moe/test_moe.py \
+    --ignore=kernels/moe/test_cutlass_moe.py"
+  fi
+
+  # --- Entrypoint ignores ---
+  if [[ $cmds == *" entrypoints/openai "* ]]; then
+    cmds=${cmds//" entrypoints/openai "/" entrypoints/openai \
+    --ignore=entrypoints/openai/chat_completion/test_audio.py \
+    --ignore=entrypoints/openai/completion/test_shutdown.py \
+    --ignore=entrypoints/openai/test_completion.py \
+    --ignore=entrypoints/openai/models/test_models.py \
+    --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+    --ignore=entrypoints/openai/chat_completion/test_root_path.py \
+    --ignore=entrypoints/openai/completion/test_prompt_validation.py "}
+  fi
+
+  if [[ $cmds == *" entrypoints/serve"* ]]; then
+    cmds="${cmds} \
+    --ignore=entrypoints/serve/lora/test_lora_adapters.py"
+  fi
+
+  if [[ $cmds == *" entrypoints/llm "* ]]; then
+    cmds=${cmds//" entrypoints/llm "/" entrypoints/llm \
+    --ignore=entrypoints/llm/test_chat.py \
+    --ignore=entrypoints/llm/test_accuracy.py \
+    --ignore=entrypoints/llm/test_init.py \
+    --ignore=entrypoints/llm/test_prompt_validation.py "}
+  fi
+
+  # Clean up escaped newlines from --ignore appends
+  cmds=$(echo "$cmds" | sed 's/ \\ / /g')
+
+  echo "$cmds"
+}
+
+###############################################################################
+# Main
+###############################################################################
+
+# --- GPU initialization ---
+echo "--- ROCm info"
+rocminfo
+
+# --- Docker housekeeping ---
 cleanup_docker
 
-echo "--- Resetting GPUs"
-
-echo "reset" > /opt/amdgpu/etc/gpu_state
-
-while true; do
-        sleep 3
-        if grep -q clean /opt/amdgpu/etc/gpu_state; then
-                echo "GPUs state is \"clean\""
-                break
-        fi
-done
-
+# --- Pull test image ---
 echo "--- Pulling container"
 image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 docker pull "${image_name}"
 
 remove_docker_container() {
-   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
+  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT
 
+# --- Prepare commands ---
 echo "--- Running container"
 
 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
 
-commands=$@
-echo "Commands:$commands"
-
-commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"pytest -v -s basic_correctness/test_basic_correctness.py"}
-
-if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
-  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+# ---- Command source selection ----
+# Prefer VLLM_TEST_COMMANDS (preserves all inner quoting intact).
+# Fall back to $* for backward compatibility, but warn that inner
+# double-quotes will have been stripped by the calling shell.
+if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
+  commands="${VLLM_TEST_COMMANDS}"
+  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
+else
+  commands="$*"
+  if [[ -z "$commands" ]]; then
+    echo "Error: No test commands provided." >&2
+    echo "Usage:" >&2
+    echo "  Preferred:  VLLM_TEST_COMMANDS='...' bash $0" >&2
+    echo "  Legacy:     bash $0 \"commands here\"" >&2
+    exit 1
+  fi
+  echo "Commands sourced from positional args (legacy mode)"
+  echo "WARNING: Inner double-quotes in the command string may have been"
+  echo "  stripped by the calling shell. If you see syntax errors, switch to:"
+  echo "  export VLLM_TEST_COMMANDS='your commands here'"
+  echo "  bash $0"
 fi
 
-commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"pytest -v -s compile/test_basic_correctness.py"}
+echo "Raw commands: $commands"
 
-if [[ $commands == *"pytest -v -s lora"* ]]; then
-  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
-fi
+# Fix quoting before ROCm overrides (so overrides see correct structure)
+commands=$(re_quote_pytest_markers "$commands")
+echo "After re-quoting: $commands"
 
-#ignore certain kernels tests
-if [[ $commands == *" kernels/core"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/core/test_fused_quant_layernorm.py \
-  --ignore=kernels/core/test_permute_cols.py"
-fi
+commands=$(apply_rocm_test_overrides "$commands")
+echo "Final commands: $commands"
 
-if [[ $commands == *" kernels/attention"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/attention/test_attention_selector.py \
-  --ignore=kernels/attention/test_encoder_decoder_attn.py \
-  --ignore=kernels/attention/test_flash_attn.py \
-  --ignore=kernels/attention/test_flashinfer.py \
-  --ignore=kernels/attention/test_prefix_prefill.py \
-  --ignore=kernels/attention/test_cascade_flash_attn.py \
-  --ignore=kernels/attention/test_mha_attn.py \
-  --ignore=kernels/attention/test_lightning_attn.py \
-  --ignore=kernels/attention/test_attention.py"
-fi
-
-if [[ $commands == *" kernels/quantization"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/quantization/test_int8_quant.py \
-  --ignore=kernels/quantization/test_machete_mm.py \
-  --ignore=kernels/quantization/test_block_fp8.py \
-  --ignore=kernels/quantization/test_block_int8.py \
-  --ignore=kernels/quantization/test_marlin_gemm.py \
-  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
-  --ignore=kernels/quantization/test_int8_kernel.py"
-fi
-
-if [[ $commands == *" kernels/mamba"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/mamba/test_mamba_mixer2.py \
-  --ignore=kernels/mamba/test_causal_conv1d.py \
-  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
-fi
-
-if [[ $commands == *" kernels/moe"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/moe/test_moe.py \
-  --ignore=kernels/moe/test_cutlass_moe.py \
-  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
-fi
-
-#ignore certain Entrypoints/openai tests
-if [[ $commands == *" entrypoints/openai "* ]]; then
-  commands=${commands//" entrypoints/openai "/" entrypoints/openai \
-  --ignore=entrypoints/openai/test_audio.py \
-  --ignore=entrypoints/openai/test_shutdown.py \
-  --ignore=entrypoints/openai/test_completion.py \
-  --ignore=entrypoints/openai/test_models.py \
-  --ignore=entrypoints/openai/test_lora_adapters.py \
-  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
-  --ignore=entrypoints/openai/test_root_path.py \
-  --ignore=entrypoints/openai/test_tokenization.py \
-  --ignore=entrypoints/openai/test_prompt_validation.py "}
-fi
-
-#ignore certain Entrypoints/llm tests
-if [[ $commands == *" entrypoints/llm "* ]]; then
-  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
-  --ignore=entrypoints/llm/test_chat.py \
-  --ignore=entrypoints/llm/test_accuracy.py \
-  --ignore=entrypoints/llm/test_init.py \
-  --ignore=entrypoints/llm/test_prompt_validation.py "}
-fi
-
-# --ignore=entrypoints/openai/test_encoder_decoder.py \
-# --ignore=entrypoints/openai/test_embedding.py \
-# --ignore=entrypoints/openai/test_oot_registration.py
-# --ignore=entrypoints/openai/test_accuracy.py \
-# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13
-
-
-PARALLEL_JOB_COUNT=8
 MYPYTHONPATH=".."
 
-# Test that we're launching on the machine that has
-# proper access to GPUs
+# Verify GPU access
 render_gid=$(getent group render | cut -d: -f3)
 if [[ -z "$render_gid" ]]; then
   echo "Error: 'render' group not found. This is required for GPU access." >&2
   exit 1
 fi
 
-# check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs.
-if [[ $commands == *"--shard-id="* ]]; then
-  # assign job count as the number of shards used
-  commands=$(echo "$commands" | sed -E "s/--num-shards[[:blank:]]*=[[:blank:]]*[0-9]*/--num-shards=${PARALLEL_JOB_COUNT} /g" | sed 's/ \\ / /g')
-  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
-    # assign shard-id for each shard
-    commands_gpu=$(echo "$commands" | sed -E "s/--shard-id[[:blank:]]*=[[:blank:]]*[0-9]*/--shard-id=${GPU} /g" | sed 's/ \\ / /g')
-    echo "Shard ${GPU} commands:$commands_gpu"
-    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
-    docker run \
-        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-        --network=host \
-        --shm-size=16gb \
-        --group-add "$render_gid" \
-        --rm \
-        -e HIP_VISIBLE_DEVICES="${GPU}" \
-        -e HF_TOKEN \
-        -e AWS_ACCESS_KEY_ID \
-        -e AWS_SECRET_ACCESS_KEY \
-        -v "${HF_CACHE}:${HF_MOUNT}" \
-        -e "HF_HOME=${HF_MOUNT}" \
-        -e "PYTHONPATH=${MYPYTHONPATH}" \
-        --name "${container_name}_${GPU}" \
-        "${image_name}" \
-        /bin/bash -c "${commands_gpu}" \
-        |& while read -r line; do echo ">>Shard $GPU: $line"; done &
-    PIDS+=($!)
-  done
-  #wait for all processes to finish and collect exit codes
-  for pid in "${PIDS[@]}"; do
-    wait "${pid}"
-    STATUS+=($?)
-  done
-  at_least_one_shard_with_tests=0
-  for st in "${STATUS[@]}"; do
-    if [[ ${st} -ne 0 ]] && [[ ${st} -ne 5 ]]; then
-      echo "One of the processes failed with $st"
-      exit "${st}"
-    elif [[ ${st} -eq 5 ]]; then
-      echo "Shard exited with status 5 (no tests collected) - treating as success"
-    else # This means st is 0
-      at_least_one_shard_with_tests=1
+# --- RDMA device passthrough (conditional) ---
+# If the host has RDMA devices, pass them through so tests like
+# test_moriio_connector can access ibverbs. On hosts without RDMA
+# hardware the tests will gracefully skip via _rdma_available().
+RDMA_FLAGS=""
+if [ -d /dev/infiniband ]; then
+  echo "RDMA devices detected on host, enabling passthrough"
+  RDMA_FLAGS="--device /dev/infiniband --cap-add=IPC_LOCK"
+else
+  echo "No RDMA devices found on host, RDMA tests will be skipped"
+fi
+
+# --- Route: multi-node vs single-node ---
+if is_multi_node "$commands"; then
+  echo "--- Multi-node job detected"
+  export DCKR_VER=$(docker --version | sed 's/Docker version \(.*\), build .*/\1/')
+
+  # Parse the bracket syntax:  prefix ; [node0_cmds] && [node1_cmds]
+  #   BASH_REMATCH[1] = prefix (everything before first bracket)
+  #   BASH_REMATCH[2] = comma-separated node0 commands
+  #   BASH_REMATCH[3] = comma-separated node1 commands
+  if [[ "$commands" =~ ^(.*)\[(.*)"] && ["(.*)\]$ ]]; then
+    prefix=$(echo "${BASH_REMATCH[1]}" | sed 's/;//g')
+    echo "PREFIX: ${prefix}"
+
+    export composite_command="(command rocm-smi || true)"
+    saved_IFS=$IFS
+    IFS=','
+    read -ra node0 <<< "${BASH_REMATCH[2]}"
+    read -ra node1 <<< "${BASH_REMATCH[3]}"
+    IFS=$saved_IFS
+
+    if [[ ${#node0[@]} -ne ${#node1[@]} ]]; then
+      echo "Warning: node0 has ${#node0[@]} commands, node1 has ${#node1[@]}. They will be paired by index."
     fi
-  done
-  if [[ ${#STATUS[@]} -gt 0 && ${at_least_one_shard_with_tests} -eq 0 ]]; then
-    echo "All shards reported no tests collected. Failing the build."
-    exit 1
+
+    for i in "${!node0[@]}"; do
+      command_node_0=$(echo "${node0[i]}" | sed 's/\"//g')
+      command_node_1=$(echo "${node1[i]}" | sed 's/\"//g')
+
+      step_cmd="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
+      echo "COMMANDS: ${step_cmd}"
+      composite_command="${composite_command} && ${step_cmd}"
+    done
+
+    /bin/bash -c "${composite_command}"
+    exit_code=$?
+    cleanup_network
+    handle_pytest_exit "$exit_code"
+  else
+    echo "Multi-node job detected but failed to parse bracket command syntax."
+    echo "Expected format: prefix ; [node0_cmd1, node0_cmd2] && [node1_cmd1, node1_cmd2]"
+    echo "Got: $commands"
+    cleanup_network
+    exit 111
   fi
 else
+  echo "--- Single-node job"
   echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
+
   docker run \
-          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-          --network=host \
-          --shm-size=16gb \
-          --group-add "$render_gid" \
-          --rm \
-          -e HF_TOKEN \
-          -e AWS_ACCESS_KEY_ID \
-          -e AWS_SECRET_ACCESS_KEY \
-          -v "${HF_CACHE}:${HF_MOUNT}" \
-          -e "HF_HOME=${HF_MOUNT}" \
-          -e "PYTHONPATH=${MYPYTHONPATH}" \
-          --name "${container_name}" \
-          "${image_name}" \
-          /bin/bash -c "${commands}"
+    --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+    $RDMA_FLAGS \
+    --network=host \
+    --shm-size=16gb \
+    --group-add "$render_gid" \
+    --rm \
+    -e HF_TOKEN \
+    -e AWS_ACCESS_KEY_ID \
+    -e AWS_SECRET_ACCESS_KEY \
+    -e BUILDKITE_PARALLEL_JOB \
+    -e BUILDKITE_PARALLEL_JOB_COUNT \
+    -v "${HF_CACHE}:${HF_MOUNT}" \
+    -e "HF_HOME=${HF_MOUNT}" \
+    -e "PYTHONPATH=${MYPYTHONPATH}" \
+    -e "PYTORCH_ROCM_ARCH=" \
+    --name "${container_name}" \
+    "${image_name}" \
+    /bin/bash -c "${commands}"
+
+  exit_code=$?
+  handle_pytest_exit "$exit_code"
 fi

.buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+set -euox pipefail
+
+export VLLM_CPU_KVCACHE_SPACE=1 
+export VLLM_CPU_CI_ENV=1
+# Reduce sub-processes for acceleration
+export TORCH_COMPILE_DISABLE=1 
+export VLLM_ENABLE_V1_MULTIPROCESSING=0
+
+SDE_ARCHIVE="sde-external-10.7.0-2026-02-18-lin.tar.xz"
+SDE_CHECKSUM="CA3D4086DE4ACB3FAEDF9F57B541C6936B7D5E19AE2BF763B6EA933573A0A217"
+wget "https://downloadmirror.intel.com/913594/${SDE_ARCHIVE}"
+echo "${SDE_CHECKSUM}  ${SDE_ARCHIVE}" | sha256sum --check
+mkdir -p sde
+tar -xvf "./${SDE_ARCHIVE}" --strip-components=1 -C ./sde/
+
+wait_for_pid_and_check_log() {
+    local pid="$1"
+    local log_file="$2"
+    local exit_status
+
+    if [ -z "$pid" ] || [ -z "$log_file" ]; then
+        echo "Usage: wait_for_pid_and_check_log <PID> <LOG_FILE>"
+        return 1
+    fi
+
+    echo "Waiting for process $pid to finish..."
+    
+    # Use the 'wait' command to pause the script until the specific PID exits.
+    # The 'wait' command's own exit status will be that of the waited-for process.
+    if wait "$pid"; then
+        exit_status=$?
+        echo "Process $pid finished with exit status $exit_status (Success)."
+    else
+        exit_status=$?
+        echo "Process $pid finished with exit status $exit_status (Failure)."
+    fi
+
+    if [ "$exit_status" -ne 0 ]; then
+        echo "Process exited with a non-zero status."
+        echo "--- Last few lines of log file: $log_file ---"
+        tail -n 50 "$log_file"
+        echo "---------------------------------------------"
+        return 1 # Indicate failure based on exit status
+    fi
+
+    echo "No errors detected in log file and process exited successfully."
+    return 0
+}
+
+# Test Sky Lake (AVX512F)
+./sde/sde64 -skl -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_0.log 2>&1 &
+PID_TEST_0=$!
+
+# Test Cascade Lake (AVX512F + VNNI)
+./sde/sde64 -clx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_1.log 2>&1 &
+PID_TEST_1=$!
+
+# Test Cooper Lake (AVX512F + VNNI + BF16)
+./sde/sde64 -cpx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_2.log 2>&1 &
+PID_TEST_2=$!
+
+wait_for_pid_and_check_log $PID_TEST_0 test_0.log
+wait_for_pid_and_check_log $PID_TEST_1 test_1.log
+wait_for_pid_and_check_log $PID_TEST_2 test_2.log

.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+set -euox pipefail
+export VLLM_CPU_CI_ENV=0
+export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
+
+echo "--- PP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 --max-model-len=4096 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+vllm bench serve \
+    --backend vllm \
+    --dataset-name random \
+    --model meta-llama/Llama-3.2-3B-Instruct \
+    --num-prompts 20 \
+    --result-dir ./test_results \
+    --result-filename tp_pp.json \
+    --save-result \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/tp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+  echo "Some requests were failed!"
+  exit 1
+fi
+
+#echo "--- DP+TP"
+#vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 --max-model-len=4096 &
+#server_pid=$!
+#timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+#vllm bench serve \
+#    --backend vllm \
+#    --dataset-name random \
+#    --model meta-llama/Llama-3.2-3B-Instruct \
+#    --num-prompts 20 \
+#    --result-dir ./test_results \
+#    --result-filename dp_pp.json \
+#    --save-result \
+#    --endpoint /v1/completions
+#kill -s SIGTERM $server_pid; wait $server_pid || true
+#failed_req=$(jq '.failed' ./test_results/dp_pp.json)
+#if [ "$failed_req" -ne 0 ]; then
+#  echo "Some requests were failed!"
+#  exit 1
+#fi

.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

@@ -5,8 +5,8 @@
 set -ex
 
 # allow to bind to different cores
-CORE_RANGE=${CORE_RANGE:-0-16}
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-16}
+CORE_RANGE=${CORE_RANGE:-0-31}
+OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-31}
 
 export CMAKE_BUILD_PARALLEL_LEVEL=16
 
@@ -34,13 +34,18 @@ function cpu_tests() {
   # offline inference
   docker exec cpu-test bash -c "
     set -e
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m"
 
   # Run model tests
   docker exec cpu-test bash -c "
     set -e
     pytest -x -v -s tests/models/multimodal/generation/test_whisper.py -m cpu_model"
 
+  # Run quantized model tests
+  docker exec cpu-test bash -c "
+    set -e
+    pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
+
   # Run kernel tests
   docker exec cpu-test bash -c "
     set -e

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -27,7 +27,7 @@ function cpu_tests() {
   podman exec -it "$container_id" bash -c "
     export TORCH_COMPILE_DISABLE=1
     set -xve
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> $HOME/test_basic.log
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m" >> "$HOME"/test_basic.log
 
   # Run basic model test
   podman exec -it "$container_id" bash -c "
@@ -43,7 +43,7 @@ function cpu_tests() {
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-False-5-32-google/gemma-1.1-2b-it]
     pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
     # TODO: Below test case tests/models/language/pooling/test_embedding.py::test_models[True-ssmits/Qwen2-7B-Instruct-embed-base] fails on ppc64le. Disabling it for time being.
-    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> $HOME/test_rest.log
+    # pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model" >> "$HOME"/test_rest.log
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -2,119 +2,19 @@
 
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
-set -ex
+set -euox pipefail
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
-# used for TP/PP E2E test
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
+IMAGE_NAME="cpu-test-$NUMA_NODE"
+TIMEOUT_VAL=$1
+TEST_COMMAND=$2
 
-export CMAKE_BUILD_PARALLEL_LEVEL=32
-
-# Setup cleanup
-remove_docker_container() {
-    set -e;
-    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
-}
-trap remove_docker_container EXIT
-remove_docker_container
-
-# Try building the docker image
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --progress plain --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
+# building the docker image
+echo "--- :docker: Building Docker image"
+docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=16 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
-
-function cpu_tests() {
-  set -e
-  export NUMA_NODE=$2
-
-  # list packages
-  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
-    set -e
-    pip list"
-
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pip list"
-
-  # offline inference
-  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
-    set -e
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
-
-  # Run kernel tests
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
-    pytest -x -v -s tests/kernels/test_onednn.py"
-
-  # Run basic model test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    # Note: disable until supports V1
-    # pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
-    # pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
-
-    pytest -x -v -s tests/models/language/generation -m cpu_model
-    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model
-
-    pytest -x -v -s tests/models/language/pooling -m cpu_model
-    pytest -x -v -s tests/models/multimodal/generation \
-                --ignore=tests/models/multimodal/generation/test_pixtral.py \
-                -m cpu_model"
-
-  # Run compressed-tensor test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pytest -x -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
-
-  # Run AWQ/GPTQ test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pytest -x -s -v \
-    tests/quantization/test_cpu_wna16.py"
-
-  # Run multi-lora tests
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
-    set -e
-    pytest -x -s -v \
-    tests/lora/test_qwenvl.py"
-
-  # online serving: tp+pp
-  docker exec cpu-test-"$NUMA_NODE" bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
-    server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model meta-llama/Llama-3.2-3B-Instruct \
-      --num-prompts 20 \
-      --endpoint /v1/completions
-    kill -s SIGTERM $server_pid &'
-
-  # online serving: tp+dp
-  docker exec cpu-test-"$NUMA_NODE" bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
-    server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model meta-llama/Llama-3.2-3B-Instruct \
-      --num-prompts 20 \
-      --endpoint /v1/completions
-    kill -s SIGTERM $server_pid &'
-}
-
-# All of CPU tests are expected to be finished less than 40 mins.
-export -f cpu_tests
-timeout 2.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g "$IMAGE_NAME" \
+        timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -25,5 +25,5 @@ remove_docker_container
 
 # Run the image and test offline inference
 docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
-    python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
+    python3 examples/basic/offline_inference/generate.py --model meta-llama/Llama-3.2-1B
 '

.buildkite/scripts/hardware_ci/run-hpu-test.sh

@@ -1,21 +1,49 @@
 #!/bin/bash
 
-# This script build the CPU docker image and run the offline inference inside the container.
+# This script builds the HPU docker image and runs the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
+#
+# vllm-gaudi compatibility pinning:
+#   The vllm-gaudi plugin is installed on top of the vllm upstream checkout used by this CI job.
+#   When upstream vllm changes its API, the plugin may break before it has been updated.
+#   To handle this, the vllm-gaudi repository maintains a file:
+#     vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT
+#   The first line of that file controls what version of vllm is used inside the Docker image:
+#     - "latest"        : no checkout override; the current Buildkite CI commit is used as-is.
+#     - "<commit SHA>"  : vllm is checked out to that specific commit before building, pinning
+#                         the test to a known-compatible baseline.
+#   To unpin (resume testing against the live vllm tip), set the file content back to "latest".
 set -exuo pipefail
 
+# Fetch the vllm community commit reference from vllm-gaudi (first line only).
+VLLM_COMMUNITY_COMMIT=$(curl -s \
+  https://raw.githubusercontent.com/vllm-project/vllm-gaudi/vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT \
+  | head -1 | tr -d '\n')
+
+echo "Using vllm community commit: ${VLLM_COMMUNITY_COMMIT}"
+
 # Try building the docker image
-cat <<EOF | docker build -t hpu-plugin-v1-test-env -f - .
+image_name="hpu/upstream-vllm-ci:${BUILDKITE_COMMIT}"
+container_name="hpu-upstream-vllm-ci-${BUILDKITE_COMMIT}-container"
+cat <<EOF | docker build -t "${image_name}" -f - .
 FROM gaudi-base-image:latest
 
 COPY ./ /workspace/vllm
 
+# If VLLM_COMMUNITY_COMMIT is a specific commit (not "latest"), check it out to pin vllm
+# to the version known to be compatible with vllm-gaudi. When the value is "latest",
+# the current checkout (the Buildkite CI commit) is used unchanged.
+RUN if [ "${VLLM_COMMUNITY_COMMIT}" != "latest" ]; then \
+      cd /workspace/vllm && git fetch --unshallow 2>/dev/null || true && git checkout ${VLLM_COMMUNITY_COMMIT}; \
+    fi
+
 WORKDIR /workspace/vllm
 
 ENV no_proxy=localhost,127.0.0.1
 ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
 
-RUN VLLM_TARGET_DEVICE=empty pip install .
+RUN bash -c 'pip install -r <(sed "/^torch/d" requirements/build.txt)'
+RUN VLLM_TARGET_DEVICE=empty pip install --no-build-isolation -e .
 RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
 
 # install development dependencies (for testing)
@@ -36,15 +64,20 @@ EOF
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-remove_docker_containers() { docker rm -f hpu-plugin-v1-test || true; }
+remove_docker_containers() { docker rm -f "${container_name}" || true; }
 trap 'remove_docker_containers; exit $EXITCODE;' EXIT
 remove_docker_containers
 
 echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
+docker run --rm --runtime=habana --name="${container_name}" --network=host \
   -e HABANA_VISIBLE_DEVICES=all \
-  hpu-plugin-v1-test-env \
-  /bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
+  -e VLLM_SKIP_WARMUP=true \
+  -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true \
+  -e PT_HPU_LAZY_MODE=1 \
+  "${image_name}" \
+  /bin/bash -c '
+  cd vllm; timeout 120s python -u examples/basic/offline_inference/generate.py --model facebook/opt-125m
+'
 
 EXITCODE=$?
 if [ $EXITCODE -eq 0 ]; then

.buildkite/scripts/hardware_ci/run-intel-test.sh

@@ -0,0 +1,292 @@
+#!/bin/bash
+
+# This script runs tests inside the Intel XPU docker container.
+# It mirrors the structure of run-amd-test.sh while keeping Intel-specific
+# container setup and allowing commands to be sourced from YAML or env.
+#
+# Command sources (in priority order):
+#   1) VLLM_TEST_COMMANDS env var (preferred, preserves quoting)
+#   2) Positional args (legacy)
+#   3) One or more YAML files with a commands list (test-area style)
+###############################################################################
+set -o pipefail
+
+DRY_RUN=${DRY_RUN:-0}
+if [[ "${1:-}" == "--dry-run" ]]; then
+  DRY_RUN=1
+  shift
+fi
+
+# Export Python path
+export PYTHONPATH=".."
+
+###############################################################################
+# Helper Functions
+###############################################################################
+
+cleanup_docker() {
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory." >&2
+    exit 1
+  fi
+  echo "Docker root directory: $docker_root"
+
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    docker image prune -f
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
+}
+
+re_quote_pytest_markers() {
+  local input="$1"
+  local output=""
+  local collecting=false
+  local marker_buf=""
+
+  local flat="${input//$'\n'/ }"
+  local restore_glob
+  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
+  set -o noglob
+  local -a words
+  read -ra words <<< "$flat"
+  eval "$restore_glob"
+
+  for word in "${words[@]}"; do
+    if $collecting; then
+      if [[ "$word" == *"'"* ]]; then
+        if [[ -n "$marker_buf" ]]; then
+          output+="${marker_buf} "
+          marker_buf=""
+        fi
+        output+="${word} "
+        collecting=false
+        continue
+      fi
+
+      local is_boundary=false
+      case "$word" in
+        "&&"|"||"|";"|"|")
+          is_boundary=true ;;
+        --*)
+          is_boundary=true ;;
+        -[a-zA-Z])
+          is_boundary=true ;;
+        */*)
+          is_boundary=true ;;
+        *.py|*.py::*)
+          is_boundary=true ;;
+        *=*)
+          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
+            is_boundary=true
+          fi
+          ;;
+      esac
+
+      if $is_boundary; then
+        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+          output+="'${marker_buf}' "
+        else
+          output+="${marker_buf} "
+        fi
+        collecting=false
+        marker_buf=""
+        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
+          output+="${word} "
+          collecting=true
+        else
+          output+="${word} "
+        fi
+      else
+        if [[ -n "$marker_buf" ]]; then
+          marker_buf+=" ${word}"
+        else
+          marker_buf="${word}"
+        fi
+      fi
+    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
+      output+="${word} "
+      collecting=true
+      marker_buf=""
+    else
+      output+="${word} "
+    fi
+  done
+
+  if $collecting && [[ -n "$marker_buf" ]]; then
+    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+      output+="'${marker_buf}'"
+    else
+      output+="${marker_buf}"
+    fi
+  fi
+
+  echo "${output% }"
+}
+
+apply_intel_test_overrides() {
+  local cmds="$1"
+  # Placeholder for Intel-specific exclusions/overrides.
+  echo "$cmds"
+}
+
+is_yaml_file() {
+  local p="$1"
+  [[ -f "$p" && "$p" == *.yaml ]]
+}
+
+extract_yaml_commands() {
+  local yaml_path="$1"
+  awk '
+    $1 == "commands:" { in_cmds=1; next }
+    in_cmds && $0 ~ /^[[:space:]]*-[[:space:]]/ {
+      sub(/^[[:space:]]*-[[:space:]]/, "");
+      print;
+      next
+    }
+    in_cmds && $0 ~ /^[^[:space:]]/ { exit }
+  ' "$yaml_path"
+}
+
+###############################################################################
+# Main
+###############################################################################
+
+default_image_name="${REGISTRY}/${REPO}:${BUILDKITE_COMMIT}-xpu"
+#default_image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-xpu"
+image_name="${IMAGE_TAG_XPU:-${default_image_name}}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
+# ---- Command source selection ----
+commands=""
+if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
+  commands="${VLLM_TEST_COMMANDS}"
+  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
+elif [[ $# -gt 0 ]]; then
+  all_yaml=true
+  for arg in "$@"; do
+    if ! is_yaml_file "$arg"; then
+      all_yaml=false
+      break
+    fi
+  done
+
+  if $all_yaml; then
+    for yaml in "$@"; do
+      mapfile -t COMMANDS < <(extract_yaml_commands "$yaml")
+      if [[ ${#COMMANDS[@]} -eq 0 ]]; then
+        echo "Error: No commands found in ${yaml}" >&2
+        exit 1
+      fi
+      for cmd in "${COMMANDS[@]}"; do
+        if [[ -z "$commands" ]]; then
+          commands="${cmd}"
+        else
+          commands+=" && ${cmd}"
+        fi
+      done
+    done
+    echo "Commands sourced from YAML files: $*"
+  else
+    commands="$*"
+    echo "Commands sourced from positional args (legacy mode)"
+  fi
+else
+  SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+  DEFAULT_YAML="${SCRIPT_DIR}/intel-test.yaml"
+  if [[ ! -f "${DEFAULT_YAML}" ]]; then
+    echo "Error: YAML file not found: ${DEFAULT_YAML}" >&2
+    exit 1
+  fi
+  mapfile -t COMMANDS < <(extract_yaml_commands "${DEFAULT_YAML}")
+  if [[ ${#COMMANDS[@]} -eq 0 ]]; then
+    echo "Error: No commands found in ${DEFAULT_YAML}" >&2
+    exit 1
+  fi
+  for cmd in "${COMMANDS[@]}"; do
+    if [[ -z "$commands" ]]; then
+      commands="${cmd}"
+    else
+      commands+=" && ${cmd}"
+    fi
+  done
+  echo "Commands sourced from default YAML: ${DEFAULT_YAML}"
+fi
+
+if [[ -z "$commands" ]]; then
+  echo "Error: No test commands provided." >&2
+  exit 1
+fi
+
+echo "Raw commands: $commands"
+commands=$(re_quote_pytest_markers "$commands")
+echo "After re-quoting: $commands"
+commands=$(apply_intel_test_overrides "$commands")
+echo "Final commands: $commands"
+
+# Dry-run mode prints final commands and exits before Docker.
+if [[ "$DRY_RUN" == "1" ]]; then
+  echo "DRY_RUN=1 set, skipping Docker execution."
+  exit 0
+fi
+
+# --- Docker housekeeping ---
+cleanup_docker
+
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+
+# --- Build or pull test image ---
+IMAGE="${IMAGE_TAG_XPU:-${image_name}}"
+
+echo "Using image: ${IMAGE}"
+
+if docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  echo "Image already exists locally, skipping pull"
+else
+  echo "Image not found locally, waiting for lock..."
+
+  flock /tmp/docker-pull.lock bash -c "
+    if docker image inspect '${IMAGE}' >/dev/null 2>&1; then
+      echo 'Image already pulled by another runner'
+    else
+      echo 'Pulling image...'
+      timeout 900 docker pull '${IMAGE}'
+    fi
+  "
+
+  echo "Pull step completed"
+fi
+
+remove_docker_container() {
+  docker rm -f "${container_name}" || true
+  docker image rm -f "${image_name}" || true
+  docker system prune -f || true
+}
+trap remove_docker_container EXIT
+
+# --- Single-node job ---
+
+if [[ -z "${ZE_AFFINITY_MASK:-}" ]]; then
+  echo "Warning: ZE_AFFINITY_MASK is not set. Proceeding without device affinity." >&2
+fi
+
+docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    --privileged \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN:-}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK:-}" \
+    -e "CMDS=${commands}" \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"'

.buildkite/scripts/hardware_ci/run-npu-test.sh

@@ -41,6 +41,7 @@ get_config() {
         echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2
         exit 1
     fi
+    # shellcheck source=/dev/null
     source "${TEST_RUN_CONFIG_FILE}"
     echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}"
     return 0
@@ -48,9 +49,8 @@ get_config() {
 
 # get test running configuration.
 fetch_vllm_test_cfg
-get_config
 # Check if the function call was successful. If not, exit the script.
-if [ $? -ne 0 ]; then
+if ! get_config; then
   exit 1
 fi
 
@@ -62,14 +62,14 @@ agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
 echo "agent_idx: ${agent_idx}"
 builder_name="cachebuilder${agent_idx}"
 builder_cache_dir="/mnt/docker-cache${agent_idx}"
-mkdir -p ${builder_cache_dir}
+mkdir -p "${builder_cache_dir}"
 
 # Try building the docker image
 cat <<EOF | DOCKER_BUILDKIT=1 docker build \
-    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:${PYPI_CACHE_HOST} \
-    --builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
-                           --cache-to type=local,dest=${builder_cache_dir},mode=max \
-    --progress=plain --load -t ${image_name} -f - .
+    --add-host cache-service-vllm.nginx-pypi-cache.svc.cluster.local:"${PYPI_CACHE_HOST}" \
+    --builder "${builder_name}" --cache-from type=local,src="${builder_cache_dir}" \
+                           --cache-to type=local,dest="${builder_cache_dir}",mode=max \
+    --progress=plain --load -t "${image_name}" -f - .
 FROM ${BASE_IMAGE_NAME}
 
 # Define environments
@@ -116,7 +116,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
     source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
+    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -i)-linux/devlib && \
     python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/
 
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
@@ -139,7 +139,7 @@ trap remove_docker_container EXIT
 # Generate corresponding --device args based on BUILDKITE_AGENT_NAME
 # Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
 #   e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
-#   returns --device /dev/davinci0 --device /dev/davinci1
+#   returns one argument per line: --device, /dev/davinciX, ...
 parse_and_gen_devices() {
     local input="$1"
     local index cards_num
@@ -151,29 +151,24 @@ parse_and_gen_devices() {
         return 1
     fi
 
-    local devices=""
     local i=0
     while (( i < cards_num )); do
         local dev_idx=$(((index - 1)*cards_num + i ))
-        devices="$devices --device /dev/davinci${dev_idx}"
+        printf '%s\n' "--device"
+        printf '%s\n' "/dev/davinci${dev_idx}"
         ((i++))
     done
-
-    # trim leading space
-    devices="${devices#"${devices%%[![:space:]]*}"}"
-    # Output devices: assigned to the caller variable
-    printf '%s' "$devices"
 }
 
-devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
+mapfile -t device_args < <(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
 
 # Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
 # This test checks whether the OOT platform interface is functioning properly in conjunction with
 # the hardware plugin vllm-ascend.
 model_cache_dir=/mnt/modelscope${agent_idx}
-mkdir -p ${model_cache_dir}
+mkdir -p "${model_cache_dir}"
 docker run \
-    ${devices} \
+    "${device_args[@]}" \
     --device /dev/davinci_manager \
     --device /dev/devmm_svm \
     --device /dev/hisi_hdc \
@@ -182,7 +177,7 @@ docker run \
     -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
     -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v ${model_cache_dir}:/root/.cache/modelscope \
+    -v "${model_cache_dir}":/root/.cache/modelscope \
     --entrypoint="" \
     --name "${container_name}" \
     "${image_name}" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 
@@ -127,7 +127,7 @@ run_and_track_test() {
 
 # --- Actual Test Execution ---
 run_and_track_test 1 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
 run_and_track_test 2 "test_moe_pallas.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
 run_and_track_test 3 "test_lora.py" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.9.2" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -8,7 +8,7 @@ image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 
 # Try building the docker image
-docker build -t ${image_name} -f docker/Dockerfile.xpu .
+docker build -t "${image_name}" -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() {
@@ -33,20 +33,23 @@ docker run \
     bash -c '
     set -e
     echo $ZE_AFFINITY_MASK
-    pip install tblib==3.1.0
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    python3 examples/offline_inference/basic/generate.py --model Intel/Qwen2.5-0.5B-W4A16-G128-AutoRound-LLMC-TEST-ONLY --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
+    python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager --max-model-len 8192
+    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
+    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
+    python3 examples/basic/offline_inference/generate.py --model OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc --block-size 64 --enforce-eager --max-model-len 8192
     cd tests
-    pytest -v -s v1/core
+    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py
     pytest -v -s v1/engine
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
-    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
+    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py
     pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py
+    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py
     pytest -v -s v1/test_serial_utils.py
 '

.buildkite/scripts/push-nightly-builds-rocm.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Push ROCm nightly base image and nightly image from ECR 
+# to Docker Hub as vllm/vllm-openai-rocm:base-nightly and vllm/vllm-openai-rocm:nightly
+# and vllm/vllm-openai-rocm:base-nightly-<commit> and vllm/vllm-openai-rocm:nightly-<commit>.
+# Run when NIGHTLY=1 after build-rocm-release-image has pushed to ECR.
+#
+# Local testing (no push to Docker Hub):
+#   BUILDKITE_COMMIT=<commit-with-rocm-image-in-ecr> DRY_RUN=1 bash .buildkite/scripts/push-nightly-builds-rocm.sh
+# Requires: AWS CLI configured (for ECR public login), Docker. For full run: Docker Hub login.
+
+set -ex
+
+# Use BUILDKITE_COMMIT from env (required; set to a commit that has ROCm image in ECR for local test)
+BUILDKITE_COMMIT="${BUILDKITE_COMMIT:?Set BUILDKITE_COMMIT to the commit SHA that has the ROCm image in ECR (e.g. from a previous release pipeline run)}"
+DRY_RUN="${DRY_RUN:-0}"
+
+# Get the base image ECR tag (set by build-rocm-release-image pipeline step)
+BASE_ORIG_TAG="$(buildkite-agent meta-data get rocm-base-ecr-tag 2>/dev/null || echo "")"
+if [ -z "$BASE_ORIG_TAG" ]; then
+  echo "WARNING: rocm-base-ecr-tag metadata not found, falling back to commit-based tag"
+  BASE_ORIG_TAG="public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base"
+fi
+
+ORIG_TAG="${BUILDKITE_COMMIT}-rocm"
+BASE_TAG_NAME="base-nightly"
+TAG_NAME="nightly"
+BASE_TAG_NAME_COMMIT="base-nightly-${BUILDKITE_COMMIT}"
+TAG_NAME_COMMIT="nightly-${BUILDKITE_COMMIT}"
+
+echo "Pushing ROCm base image from ECR: $BASE_ORIG_TAG"
+echo "Pushing ROCm release image from ECR tag: $ORIG_TAG to Docker Hub as $TAG_NAME and $TAG_NAME_COMMIT"
+[[ "$DRY_RUN" == "1" ]] && echo "[DRY_RUN] Skipping push to Docker Hub"
+
+# Login to ECR and pull the image built by build-rocm-release-image
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+docker pull "$BASE_ORIG_TAG"
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG"
+
+# Tag for Docker Hub (base-nightly and base-nightly-<commit>, nightly and nightly-<commit>)
+docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
+docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME"
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
+
+if [[ "$DRY_RUN" == "1" ]]; then
+  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
+  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"
+  echo "[DRY_RUN] Local tags created. Exiting without push."
+  exit 0
+fi
+
+# Push to Docker Hub (docker-login plugin runs before this step in CI)
+docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
+docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
+docker push vllm/vllm-openai-rocm:"$TAG_NAME"
+docker push vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
+
+echo "Pushed vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
+echo "Pushed vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"

.buildkite/scripts/push-nightly-builds.sh

@@ -21,16 +21,16 @@ echo "Pushing original tag $ORIG_TAG_NAME$ORIG_TAG_SUFFIX to new nightly tag nam
 
 # pull original arch-dependent images from AWS ECR Public
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX"
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX"
 # tag arch-dependent images
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-x86_64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$ORIG_TAG_NAME-aarch64$ORIG_TAG_SUFFIX vllm/vllm-openai:$TAG_NAME-aarch64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-aarch64"$ORIG_TAG_SUFFIX" vllm/vllm-openai:"$TAG_NAME"-aarch64
 # push arch-dependent images to DockerHub
-docker push vllm/vllm-openai:$TAG_NAME-x86_64
-docker push vllm/vllm-openai:$TAG_NAME-aarch64
+docker push vllm/vllm-openai:"$TAG_NAME"-x86_64
+docker push vllm/vllm-openai:"$TAG_NAME"-aarch64
 # push arch-independent manifest to DockerHub
-docker manifest create vllm/vllm-openai:$TAG_NAME vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
-docker manifest create vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT vllm/vllm-openai:$TAG_NAME-x86_64 vllm/vllm-openai:$TAG_NAME-aarch64 --amend
-docker manifest push vllm/vllm-openai:$TAG_NAME
-docker manifest push vllm/vllm-openai:$TAG_NAME-$BUILDKITE_COMMIT
+docker manifest create vllm/vllm-openai:"$TAG_NAME" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
+docker manifest create vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT" vllm/vllm-openai:"$TAG_NAME"-x86_64 vllm/vllm-openai:"$TAG_NAME"-aarch64 --amend
+docker manifest push vllm/vllm-openai:"$TAG_NAME"
+docker manifest push vllm/vllm-openai:"$TAG_NAME"-"$BUILDKITE_COMMIT"

.buildkite/scripts/run-prime-rl-test.sh

@@ -1,64 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Setup script for Prime-RL integration tests
-# This script prepares the environment for running Prime-RL tests with nightly vLLM
-
-set -euo pipefail
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
-PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
-PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
-
-if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
-    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
-    exit 0
-fi
-
-echo "Setting up Prime-RL integration test environment..."
-
-# Clean up any existing Prime-RL directory
-if [ -d "${PRIME_RL_DIR}" ]; then
-    echo "Removing existing Prime-RL directory..."
-    rm -rf "${PRIME_RL_DIR}"
-fi
-
-# Install UV if not available
-if ! command -v uv &> /dev/null; then
-    echo "Installing UV package manager..."
-    curl -LsSf https://astral.sh/uv/install.sh | sh
-    source $HOME/.local/bin/env
-fi
-
-# Clone Prime-RL repository at specific branch for reproducible tests
-PRIME_RL_BRANCH="integ-vllm-main"
-echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
-git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
-cd "${PRIME_RL_DIR}"
-
-echo "Setting up UV project environment..."
-export UV_PROJECT_ENVIRONMENT=/usr/local
-ln -s /usr/bin/python3 /usr/local/bin/python
-
-# Remove vllm pin from pyproject.toml
-echo "Removing vllm pin from pyproject.toml..."
-sed -i '/vllm==/d' pyproject.toml
-
-# Sync Prime-RL dependencies
-echo "Installing Prime-RL dependencies..."
-uv sync --inexact && uv sync --inexact --all-extras
-
-# Verify installation
-echo "Verifying installations..."
-uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
-uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
-
-echo "Prime-RL integration test environment setup complete!"
-
-echo "Running Prime-RL integration tests..."
-export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
-uv run pytest -vs tests/integration/test_rl.py -m gpu
-
-echo "Prime-RL integration tests completed!"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -43,7 +43,6 @@ trap cleanup EXIT
 
 for BACK in "${BACKENDS[@]}"; do
   VLLM_DEEP_GEMM_WARMUP=skip \
-  VLLM_ALL2ALL_BACKEND=$BACK \
   vllm serve "$MODEL" \
     --enforce-eager \
     --tensor-parallel-size 2 \
@@ -52,13 +51,14 @@ for BACK in "${BACKENDS[@]}"; do
     --enable-eplb \
     --trust-remote-code \
     --max-model-len 2048 \
-    --port $PORT &
+    --all2all-backend "$BACK" \
+    --port "$PORT" &
   SERVER_PID=$!
-  wait_for_server $PORT
+  wait_for_server "$PORT"
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -0,0 +1,69 @@
+#!/usr/bin/env bash
+set -euxo pipefail
+# Nightly e2e test for prefetch offloading with a MoE model.
+# Runs DeepSeek-V2-Lite with prefetch offloading of MoE expert weights
+# and validates GSM8K accuracy matches baseline (no offloading).
+#
+# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
+#
+# Environment variables:
+#   ATTENTION_BACKEND   - attention backend to use (e.g., FLASH_ATTN,
+#                         ROCM_ATTN, FLASHINFER). If unset, uses vllm default.
+THRESHOLD=${1:-0.25}
+NUM_Q=${2:-1319}
+PORT=${3:-8030}
+OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
+mkdir -p "${OUT_DIR}"
+
+wait_for_server() {
+  local port=$1
+  timeout 600 bash -c '
+    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
+      sleep 1
+    done'
+}
+
+MODEL="deepseek-ai/DeepSeek-V2-Lite"
+
+# ── Build optional vllm serve flags ─────────────────────────────────────
+
+EXTRA_ARGS=()
+if [[ -n "${ATTENTION_BACKEND:-}" ]]; then
+  echo "Using attention backend: ${ATTENTION_BACKEND}"
+  EXTRA_ARGS+=(--attention-backend "${ATTENTION_BACKEND}")
+fi
+
+cleanup() {
+  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+    kill "${SERVER_PID}" 2>/dev/null || true
+    for _ in {1..20}; do
+      kill -0 "${SERVER_PID}" 2>/dev/null || break
+      sleep 0.5
+    done
+    kill -9 "${SERVER_PID}" 2>/dev/null || true
+  fi
+}
+trap cleanup EXIT
+
+vllm serve "$MODEL" \
+  --max-model-len 2048 \
+  --offload-group-size 8 \
+  --offload-num-in-group 2 \
+  --offload-prefetch-step 1 \
+  --offload-params w13_weight w2_weight \
+  --port "$PORT" \
+  ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
+SERVER_PID=$!
+wait_for_server "$PORT"
+
+TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
+OUT="${OUT_DIR}/${TAG}_prefetch_offload.json"
+python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+python3 - <<PY
+import json; acc=json.load(open('${OUT}'))['accuracy']
+print(f"${MODEL} prefetch_offload: accuracy {acc:.3f}")
+assert acc >= ${THRESHOLD}, f"${MODEL} prefetch_offload accuracy {acc}"
+PY
+
+cleanup
+SERVER_PID=

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh

@@ -47,20 +47,20 @@ for BACK in "${BACKENDS[@]}"; do
   vllm serve "$MODEL" \
     --enforce-eager \
     --enable-eplb \
-    --all2all-backend $BACK \
+    --all2all-backend "$BACK" \
     --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true}' \
-    --tensor-parallel-size ${TENSOR_PARALLEL_SIZE} \
-    --data-parallel-size ${DATA_PARALLEL_SIZE} \
+    --tensor-parallel-size "${TENSOR_PARALLEL_SIZE}" \
+    --data-parallel-size "${DATA_PARALLEL_SIZE}" \
     --enable-expert-parallel \
     --trust-remote-code \
     --max-model-len 2048 \
-    --port $PORT &
+    --port "$PORT" &
   SERVER_PID=$!
-  wait_for_server $PORT
+  wait_for_server "$PORT"
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -24,7 +24,7 @@ if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:
   BACKENDS=("allgather_reducescatter")
   # Disable MOE padding for ROCm since it is causing eplb to fail
   export VLLM_ROCM_MOE_PADDING=0
-  PLATFORM_ARGS=("--no-async-scheduling")
+  PLATFORM_ARGS=("--no-async-scheduling" "--attention-backend=TRITON_ATTN")
   echo "Disabled async scheduling for ROCm platform due to issues with spec decode."
 else
   # Non-ROCm platform (CUDA/other)
@@ -51,20 +51,20 @@ for BACK in "${BACKENDS[@]}"; do
     --tensor-parallel-size 4 \
     --enable-expert-parallel \
     --enable-eplb \
-    --all2all-backend $BACK \
+    --all2all-backend "$BACK" \
     --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
     --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
     --trust-remote-code \
     --max-model-len 2048 \
     --gpu-memory-utilization 0.9 \
     "${PLATFORM_ARGS[@]}" \
-    --port $PORT &
+    --port "$PORT" &
   SERVER_PID=$!
-  wait_for_server $PORT
+  wait_for_server "$PORT"
 
   TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
   OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
+  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
   python3 - <<PY
 import json; acc=json.load(open('${OUT}'))['accuracy']
 print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")

.buildkite/scripts/tool_call/run-bfcl-eval.sh

@@ -0,0 +1,248 @@
+#!/bin/bash
+# Run BFCL (Berkeley Function Call Leaderboard) tool-calling correctness
+# evaluation against a local vLLM server.
+#
+# Usage:
+#   # Run with defaults (gpt-oss-20b, multi_turn)
+#   bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+#   # Run with gpt-oss-120b and multiple test categories
+#   BFCL_MODEL="openai/gpt-oss-120b" BFCL_TP_SIZE=4 \
+#     BFCL_TEST_CATEGORY="live_simple, multiple, parallel_multiple" \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+#   # Chain both API types (use BFCL_OUTPUT_DIR to avoid overwriting results)
+#   BFCL_OUTPUT_DIR=./bfcl-chat-completions BFCL_API_TYPE=chat_completions \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh && \
+#   BFCL_OUTPUT_DIR=./bfcl-responses BFCL_API_TYPE=responses \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+# Environment variables (all optional, with defaults):
+#   BFCL_MODEL          - HF model name (default: openai/gpt-oss-20b)
+#   BFCL_API_TYPE       - API type: "chat_completions" or "responses" (default: chat_completions)
+#   BFCL_OUTPUT_DIR     - Directory for BFCL results (default: current working directory)
+#   BFCL_TEST_CATEGORY  - BFCL test categories (default: multi_turn)
+#   BFCL_TOOL_CALL_PARSER - Tool call parser name (default: openai)
+#   BFCL_NUM_THREADS    - Threads for BFCL generate (default: 8)
+#   BFCL_TP_SIZE        - Tensor parallel size (default: 1)
+#   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
+#   BFCL_PORT           - Server port (default: 8000)
+#   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
+#   BFCL_EXTRA_ARGS     - Additional vLLM server args
+
+set -euo pipefail
+
+# ---- Configuration ----
+MODEL="${BFCL_MODEL:-openai/gpt-oss-20b}"
+API_TYPE="${BFCL_API_TYPE:-chat_completions}"
+OUTPUT_DIR="${BFCL_OUTPUT_DIR:-}"
+TEST_CATEGORY="${BFCL_TEST_CATEGORY:-multi_turn}"
+TOOL_CALL_PARSER="${BFCL_TOOL_CALL_PARSER:-openai}"
+NUM_THREADS="${BFCL_NUM_THREADS:-8}"
+TP_SIZE="${BFCL_TP_SIZE:-1}"
+MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
+PORT="${BFCL_PORT:-8000}"
+REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
+EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
+
+# Set up output directory
+if [ -n "$OUTPUT_DIR" ]; then
+    mkdir -p "$OUTPUT_DIR"
+    OUTPUT_DIR="$(cd "$OUTPUT_DIR" && pwd)"
+fi
+
+echo "============================================"
+echo "BFCL Tool Call Correctness Evaluation"
+echo "============================================"
+echo "Model:          $MODEL"
+echo "Tool parser:    $TOOL_CALL_PARSER"
+echo "API type:       $API_TYPE"
+echo "Output dir:     ${OUTPUT_DIR:-<cwd>}"
+echo "Test category:  $TEST_CATEGORY"
+echo "TP size:        $TP_SIZE"
+echo "Max model len:  $MAX_MODEL_LEN"
+echo "Port:           $PORT"
+echo "Num threads:    $NUM_THREADS"
+echo "============================================"
+
+# ---- Install bfcl-eval if missing ----
+if ! python3 -c "import bfcl_eval" 2>/dev/null; then
+    echo "Installing bfcl-eval..."
+    pip install "bfcl-eval>=2025.10.20.1,<2026"
+fi
+
+# ---- Cleanup handler ----
+SERVER_PID=""
+cleanup() {
+    if [ -n "$SERVER_PID" ]; then
+        echo "Stopping vLLM server (pid=$SERVER_PID)..."
+        kill "$SERVER_PID" 2>/dev/null || true
+        wait "$SERVER_PID" 2>/dev/null || true
+    fi
+    # Remove BFCL lock files (created by filelock for thread-safe writes)
+    rm -rf .file_locks/
+    if [ -n "${OUTPUT_DIR:-}" ]; then
+        rm -rf "$OUTPUT_DIR/.file_locks/"
+    fi
+}
+trap cleanup EXIT
+
+# ---- Start vLLM server ----
+echo "Starting vLLM server..."
+
+SERVE_ARGS=(
+    "$MODEL"
+    --port "$PORT"
+    --enable-auto-tool-choice
+    --tool-call-parser "$TOOL_CALL_PARSER"
+    --tensor-parallel-size "$TP_SIZE"
+    --max-model-len "$MAX_MODEL_LEN"
+    --enforce-eager
+    --no-enable-prefix-caching
+)
+
+# Append reasoning parser if specified
+if [ -n "$REASONING_PARSER" ]; then
+    SERVE_ARGS+=(--reasoning-parser "$REASONING_PARSER")
+fi
+
+# Append any extra args
+if [ -n "$EXTRA_ARGS" ]; then
+    read -ra EXTRA_ARGS_ARRAY <<< "$EXTRA_ARGS"
+    SERVE_ARGS+=("${EXTRA_ARGS_ARRAY[@]}")
+fi
+
+echo "Command: vllm serve ${SERVE_ARGS[*]}"
+vllm serve "${SERVE_ARGS[@]}" &
+SERVER_PID=$!
+
+# ---- Wait for server to be ready ----
+echo "Waiting for vLLM server to start (timeout: 600s)..."
+SECONDS_WAITED=0
+until curl -sf "http://localhost:${PORT}/health" > /dev/null 2>&1; do
+    if [ $SECONDS_WAITED -ge 600 ]; then
+        echo ""
+        echo "ERROR: vLLM server failed to start within 600s"
+        exit 1
+    fi
+    if (( SECONDS_WAITED % 30 == 0 && SECONDS_WAITED > 0 )); then
+        echo "  Still waiting... (${SECONDS_WAITED}s elapsed)"
+    fi
+    sleep 2
+    SECONDS_WAITED=$((SECONDS_WAITED + 2))
+done
+echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
+
+# ---- Run BFCL evaluation ----
+# bfcl-eval has no CLI entry point; generate() and evaluate() are Typer
+# functions that must be called from Python. The MODEL_CONFIG_MAPPING must
+# be patched in-process so BFCL knows to use the OpenAI-compatible handler
+# against our local vLLM server.
+bfcl_exit_code=0
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+import os
+import sys
+
+model = sys.argv[1]
+test_category = sys.argv[2]
+num_threads = int(sys.argv[3])
+port = sys.argv[4]
+api_type = sys.argv[5]
+output_dir = sys.argv[6] if len(sys.argv) > 6 and sys.argv[6] else os.getcwd()
+
+os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
+os.environ["OPENAI_API_KEY"] = "dummy"
+os.environ["BFCL_PROJECT_ROOT"] = output_dir
+
+import bfcl_eval.constants.model_config as bfcl_model_config
+from bfcl_eval.constants.model_config import ModelConfig
+from bfcl_eval.model_handler.api_inference.openai_completion import (
+    OpenAICompletionsHandler,
+)
+from bfcl_eval.model_handler.api_inference.openai_response import (
+    OpenAIResponsesHandler,
+)
+
+if api_type == "responses":
+    handler = OpenAIResponsesHandler
+else:
+    handler = OpenAICompletionsHandler
+
+bfcl_model_config.MODEL_CONFIG_MAPPING[model] = ModelConfig(
+    model_name=model,
+    display_name=f"{model} (FC) (vLLM)",
+    url=f"https://huggingface.co/{model}",
+    org="",
+    license="apache-2.0",
+    model_handler=handler,
+    input_price=None,
+    output_price=None,
+    is_fc_model=True,
+    underscore_to_dot=True,
+)
+
+from bfcl_eval.__main__ import evaluate, generate
+import inspect
+import typer
+
+
+def _get_default_kwargs(function):
+    kwargs = {}
+    for k, v in inspect.signature(function).parameters.items():
+        if v.default is not inspect.Parameter.empty:
+            default = v.default
+            if isinstance(default, typer.models.OptionInfo):
+                default = default.default
+            kwargs[k] = default
+    return kwargs
+
+
+# ---- generate ----
+print(f"=== BFCL generate: model={model} test_category={test_category} ===")
+gen_kwargs = _get_default_kwargs(generate)
+gen_kwargs["model"] = [model]
+gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
+gen_kwargs["skip_server_setup"] = True
+gen_kwargs["num_threads"] = num_threads
+generate(**gen_kwargs)
+
+# ---- evaluate ----
+print(f"=== BFCL evaluate: model={model} test_category={test_category} ===")
+eval_kwargs = _get_default_kwargs(evaluate)
+eval_kwargs["model"] = [model]
+eval_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
+evaluate(**eval_kwargs)
+
+print("=== BFCL evaluation completed successfully ===")
+PYEOF
+
+# ---- Upload results to buildkite ----
+if command -v buildkite-agent &>/dev/null; then
+    if [ $bfcl_exit_code -eq 0 ]; then
+        STYLE="success"
+        STATUS="PASSED"
+    else
+        STYLE="error"
+        STATUS="FAILED"
+    fi
+
+    buildkite-agent annotate --style "$STYLE" --context "bfcl-results" <<EOF
+### BFCL Tool Call Correctness - ${STATUS}
+- **Model:** \`${MODEL}\`
+- **Parser:** \`${TOOL_CALL_PARSER}\`
+- **API type:** \`${API_TYPE}\`
+- **Test category:** \`${TEST_CATEGORY}\`
+EOF
+
+    # BFCL writes results to $BFCL_PROJECT_ROOT/result/ and scores to
+    # $BFCL_PROJECT_ROOT/score/
+    RESULTS_ROOT="${OUTPUT_DIR:-.}"
+    if [ -d "$RESULTS_ROOT/result" ]; then
+        buildkite-agent artifact upload "$RESULTS_ROOT/result/**/*"
+    fi
+    if [ -d "$RESULTS_ROOT/score" ]; then
+        buildkite-agent artifact upload "$RESULTS_ROOT/score/**/*"
+    fi
+fi
+
+exit $bfcl_exit_code

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -9,10 +9,11 @@ ENV_FILE=$1
 
 # For testing on local vm, use `set -a` to export all variables
 source /etc/environment
-source $ENV_FILE
+# shellcheck source=/dev/null
+source "$ENV_FILE"
 
 remove_docker_container() { 
-    docker rm -f $CONTAINER_NAME || true;
+    docker rm -f "$CONTAINER_NAME" || true;
 }
 
 trap remove_docker_container EXIT
@@ -41,13 +42,13 @@ echo
 echo "starting docker...$CONTAINER_NAME"
 echo    
 docker run \
- -v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
- --env-file $ENV_FILE \
+ -v "$DOWNLOAD_DIR":"$DOWNLOAD_DIR" \
+ --env-file "$ENV_FILE" \
  -e HF_TOKEN="$HF_TOKEN" \
- -e TARGET_COMMIT=$BUILDKITE_COMMIT \
- -e MODEL=$MODEL \
+ -e TARGET_COMMIT="$BUILDKITE_COMMIT" \
+ -e MODEL="$MODEL" \
  -e WORKSPACE=/workspace \
- --name $CONTAINER_NAME \
+ --name "$CONTAINER_NAME" \
  -d \
  --privileged \
  --network host \

.buildkite/scripts/tpu/run_bm.sh

@@ -42,21 +42,21 @@ echo "lanching vllm..."
 echo "logging to $VLLM_LOG"
 echo
 
-vllm serve $MODEL \
+vllm serve "$MODEL" \
  --seed 42 \
- --max-num-seqs $MAX_NUM_SEQS \
- --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
- --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
+ --max-num-seqs "$MAX_NUM_SEQS" \
+ --max-num-batched-tokens "$MAX_NUM_BATCHED_TOKENS" \
+ --tensor-parallel-size "$TENSOR_PARALLEL_SIZE" \
  --no-enable-prefix-caching \
- --download_dir $DOWNLOAD_DIR \
- --max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
+ --download_dir "$DOWNLOAD_DIR" \
+ --max-model-len "$MAX_MODEL_LEN" > "$VLLM_LOG" 2>&1 &
 
 
 echo "wait for 20 minutes.."
 echo
 # sleep 1200
 # wait for 10 minutes...
-for i in {1..120}; do
+for _ in {1..120}; do
     # TODO: detect other type of errors.
     if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
         echo "Detected RuntimeError, exiting."
@@ -78,11 +78,11 @@ echo "logging to $BM_LOG"
 echo
 vllm bench serve \
     --backend vllm \
-    --model $MODEL  \
+    --model "$MODEL"  \
     --dataset-name sonnet \
     --dataset-path benchmarks/sonnet_4x.txt \
-    --sonnet-input-len $INPUT_LEN \
-    --sonnet-output-len $OUTPUT_LEN \
+    --sonnet-input-len "$INPUT_LEN" \
+    --sonnet-output-len "$OUTPUT_LEN" \
     --ignore-eos > "$BM_LOG"
 
 echo "completed..."

.buildkite/scripts/upload-nightly-wheels.sh

@@ -2,27 +2,14 @@
 
 set -ex
 
-# ======== part 0: setup ========
+# Upload a single wheel to S3 (rename linux -> manylinux).
+# Index generation is handled separately by generate-and-upload-nightly-index.sh.
 
 BUCKET="vllm-wheels"
-INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
-PYTHON=${PYTHON_PROG:=python3} # try to read from env var, otherwise use python3
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# detect if python3.10+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
-if [[ "$has_new_python" -eq 0 ]]; then
-    # use new python from docker
-    docker pull python:3-slim
-    PYTHON="docker run --rm -v $(pwd):/app -w /app python:3-slim python3"
-fi
-
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
-
-# ========= part 1: collect, rename & upload the wheel ==========
+# ========= collect, rename & upload the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -52,57 +39,8 @@ echo "Renamed wheel to: $wheel"
 # Extract the version from the wheel
 version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
 echo "Version in wheel: $version"
-pure_version="${version%%+*}"
-echo "Pure version (without variant): $pure_version"
 
 # copy wheel to its own bucket
 aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"
 
-# ========= part 2: generate and upload indices ==========
-# generate indices for all existing wheels in the commit directory
-# this script might be run multiple times if there are multiple variants being built
-# so we need to guarantee there is little chance for "TOCTOU" issues
-# i.e., one process is generating indices while another is uploading a new wheel
-# so we need to ensure no time-consuming operations happen below
-
-# list all wheels in the commit directory
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-obj_json="objects.json"
-aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
-mkdir -p "$INDICES_OUTPUT_DIR"
-
-# call script to generate indicies for all existing wheels
-# this indices have relative paths that could work as long as it is next to the wheel directory in s3
-# i.e., the wheels are always in s3://vllm-wheels/<commit>/
-# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
-if [[ ! -z "$DEFAULT_VARIANT_ALIAS" ]]; then
-    alias_arg="--alias-to-default $DEFAULT_VARIANT_ALIAS"
-else
-    alias_arg=""
-fi
-
-# HACK: we do not need regex module here, but it is required by pre-commit hook
-# To avoid any external dependency, we simply replace it back to the stdlib re module
-sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" $alias_arg
-
-# copy indices to /<commit>/ unconditionally
-echo "Uploading indices to $S3_COMMIT_PREFIX"
-aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
-
-# copy to /nightly/ only if it is on the main branch and not a PR 
-if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
-    echo "Uploading indices to overwrite /nightly/"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
-fi
-
-# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
-if [[ "$version" != *"dev"* ]]; then
-    echo "Re-generating indices for /$pure_version/"
-    rm -rf "$INDICES_OUTPUT_DIR/*"
-    mkdir -p "$INDICES_OUTPUT_DIR"
-    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" $alias_arg
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
-fi
+echo "Wheel uploaded. Index generation is handled by a separate step."

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -0,0 +1,73 @@
+#!/usr/bin/env bash
+
+set -e
+
+BUCKET="vllm-wheels"
+SUBPATH=$BUILDKITE_COMMIT
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
+GIT_VERSION=$(git describe --exact-match --tags "$BUILDKITE_COMMIT" 2>/dev/null)
+
+echo "Release version from Buildkite: $RELEASE_VERSION"
+
+if [[ -z "$GIT_VERSION" ]]; then
+    echo "[FATAL] Not on a git tag, cannot create release."
+    exit 1
+else
+    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
+fi
+# sanity check for version mismatch
+if [[ "$RELEASE_VERSION" != "$GIT_VERSION" ]]; then
+  if [[ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]]; then
+    echo "[WARNING] Force release and ignore version mismatch"
+  else
+    echo "[FATAL] Release version from Buildkite does not match Git version."
+    exit 1
+  fi
+fi
+PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
+
+# check pypi token
+if [[ -z "$PYPI_TOKEN" ]]; then
+  echo "[FATAL] PYPI_TOKEN is not set."
+  exit 1
+else
+  export TWINE_USERNAME="__token__"
+  export TWINE_PASSWORD="$PYPI_TOKEN"
+fi
+
+set -x # avoid printing secrets above
+
+# install twine from pypi
+python3 -m venv /tmp/vllm-release-env
+source /tmp/vllm-release-env/bin/activate
+pip install twine
+python3 -m twine --version
+
+# copy release wheels to local directory
+DIST_DIR=/tmp/vllm-release-dist
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+echo "Copying wheels to local directory"
+mkdir -p $DIST_DIR
+# include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
+aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
+echo "Wheels copied to local directory"
+# generate source distribution using setup.py
+python setup.py sdist --dist-dir=$DIST_DIR
+ls -la $DIST_DIR
+
+SDIST_FILE=$(find $DIST_DIR -name "vllm*.tar.gz")
+echo "Found sdist: $SDIST_FILE"
+
+# upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
+PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
+if [[ -z "$PYPI_WHEEL_FILES" ]]; then
+  echo "No default variant wheels found, quitting..."
+  exit 1
+fi
+
+python3 -m twine check "$PYPI_WHEEL_FILES" "$SDIST_FILE"
+python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES" "$SDIST_FILE"
+echo "Wheels and source distribution uploaded to PyPI"

.buildkite/scripts/upload-release-wheels.sh

@@ -1,104 +0,0 @@
-#!/usr/bin/env bash
-
-set -e
-
-BUCKET="vllm-wheels"
-SUBPATH=$BUILDKITE_COMMIT
-S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
-
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version)
-echo "Release version from Buildkite: $RELEASE_VERSION"
-GIT_VERSION=$(git describe --exact-match --tags $BUILDKITE_COMMIT 2>/dev/null)
-if [ -z "$GIT_VERSION" ]; then
-    echo "[FATAL] Not on a git tag, cannot create release."
-    exit 1
-else
-    echo "Git version for commit $BUILDKITE_COMMIT: $GIT_VERSION"
-fi
-# sanity check for version mismatch
-if [ "$RELEASE_VERSION" != "$GIT_VERSION" ]; then
-  if [ "$FORCE_RELEASE_IGNORE_VERSION_MISMATCH" == "true" ]; then
-    echo "[WARNING] Force release and ignore version mismatch"
-  else
-    echo "[FATAL] Release version from Buildkite does not match Git version."
-    exit 1
-  fi
-fi
-PURE_VERSION=${RELEASE_VERSION#v} # remove leading 'v'
-
-# check pypi token
-if [ -z "$PYPI_TOKEN" ]; then
-  echo "[FATAL] PYPI_TOKEN is not set."
-  exit 1
-else
-  export TWINE_USERNAME="__token__"
-  export TWINE_PASSWORD="$PYPI_TOKEN"
-fi
-
-# check github token
-if [ -z "$GITHUB_TOKEN" ]; then
-  echo "[FATAL] GITHUB_TOKEN is not set."
-  exit 1
-else
-  export GH_TOKEN="$GITHUB_TOKEN"
-fi
-
-set -x # avoid printing secrets above
-
-# download gh CLI from github
-# Get latest gh CLI version from GitHub API
-GH_VERSION=$(curl -s https://api.github.com/repos/cli/cli/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/' | sed 's/^v//')
-if [ -z "$GH_VERSION" ]; then
-  echo "[FATAL] Failed to get latest gh CLI version from GitHub"
-  exit 1
-fi
-echo "Downloading gh CLI version: $GH_VERSION"
-GH_TARBALL="gh_${GH_VERSION}_linux_amd64.tar.gz"
-GH_URL="https://github.com/cli/cli/releases/download/v${GH_VERSION}/${GH_TARBALL}"
-GH_INSTALL_DIR="/tmp/gh-install"
-mkdir -p "$GH_INSTALL_DIR"
-pushd "$GH_INSTALL_DIR"
-curl -L -o "$GH_TARBALL" "$GH_URL"
-tar -xzf "$GH_TARBALL"
-GH_BIN=$(realpath $(find . -name "gh" -type f -executable | head -n 1))
-if [ -z "$GH_BIN" ]; then
-  echo "[FATAL] Failed to find gh CLI executable"
-  exit 1
-fi
-echo "gh CLI downloaded successfully, version: $($GH_BIN --version)"
-echo "Last 5 releases on GitHub:" # as a sanity check of gh and GH_TOKEN
-command "$GH_BIN" release list --limit 5
-popd
-
-# install twine from pypi
-python3 -m venv /tmp/vllm-release-env
-source /tmp/vllm-release-env/bin/activate
-pip install twine
-python3 -m twine --version
-
-# copy release wheels to local directory
-DIST_DIR=/tmp/vllm-release-dist
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-echo "Copying wheels to local directory"
-mkdir -p $DIST_DIR
-# include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
-aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
-echo "Wheels copied to local directory"
-# generate source tarball
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" $BUILDKITE_COMMIT
-ls -la $DIST_DIR
-
-
-# upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
-PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
-if [ -z "$PYPI_WHEEL_FILES" ]; then
-  echo "No default variant wheels found, quitting..."
-  exit 1
-fi
-python3 -m twine check $PYPI_WHEEL_FILES
-python3 -m twine --non-interactive --verbose upload $PYPI_WHEEL_FILES
-echo "Wheels uploaded to PyPI"
-
-# create release on GitHub with the release version and all wheels
-command "$GH_BIN" release create $GIT_VERSION -d --latest --notes-from-tag --verify-tag $DIST_DIR/*.whl

.buildkite/scripts/upload-rocm-wheels.sh

@@ -55,7 +55,7 @@ mkdir -p all-rocm-wheels
 cp artifacts/rocm-base-wheels/*.whl all-rocm-wheels/ 2>/dev/null || true
 cp artifacts/rocm-vllm-wheel/*.whl all-rocm-wheels/ 2>/dev/null || true
 
-WHEEL_COUNT=$(ls all-rocm-wheels/*.whl 2>/dev/null | wc -l)
+WHEEL_COUNT=$(find all-rocm-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
 echo "Total wheels to upload: $WHEEL_COUNT"
 
 if [ "$WHEEL_COUNT" -eq 0 ]; then
@@ -115,7 +115,7 @@ if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]] |
 fi
 
 # Extract version from vLLM wheel and update version-specific index
-VLLM_WHEEL=$(ls all-rocm-wheels/vllm*.whl 2>/dev/null | head -1)
+VLLM_WHEEL=$(find all-rocm-wheels -maxdepth 1 -name 'vllm*.whl' 2>/dev/null | head -1)
 if [ -n "$VLLM_WHEEL" ]; then
     VERSION=$(unzip -p "$VLLM_WHEEL" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
     echo "Version in wheel: $VERSION"

.buildkite/test_areas/attention.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: V1 attention (H100)
   timeout_in_minutes: 30
-  gpu: h100
+  device: h100
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention
@@ -15,7 +15,7 @@ steps:
 
 - label: V1 attention (B200)
   timeout_in_minutes: 30
-  gpu: b200
+  device: b200
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention

.buildkite/test_areas/basic_correctness.yaml

@@ -4,6 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
   timeout_in_minutes: 30
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/basic_correctness/test_basic_correctness

.buildkite/test_areas/benchmarks.yaml

@@ -2,18 +2,23 @@ group: Benchmarks
 depends_on: 
   - image-build
 steps:
-- label: Benchmarks
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/.buildkite"
-  source_file_dependencies:
-  - benchmarks/
-  commands:
-  - bash scripts/run-benchmarks.sh
-
 - label: Benchmarks CLI Test
   timeout_in_minutes: 20
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/benchmarks/
   commands:
   - pytest -v -s benchmarks/
+
+- label: Attention Benchmarks Smoke Test (B200)
+  device: b200
+  num_gpus: 2
+  optional: true
+  working_dir: "/vllm-workspace/"
+  timeout_in_minutes: 10
+  source_file_dependencies:
+  - benchmarks/attention_benchmarks/
+  - vllm/v1/attention/
+  commands:
+  - python3 benchmarks/attention_benchmarks/benchmark.py --backends flash flashinfer --batch-specs "8q1s1k" --repeats 1 --warmup-iters 1

.buildkite/test_areas/compile.yaml

@@ -2,56 +2,212 @@ group: Compile
 depends_on: 
   - image-build
 steps:
-- label: Fusion and Compile Tests (B200)
-  timeout_in_minutes: 40
+- label: Sequence Parallel Correctness Tests (2 GPUs)
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
-  gpu: b200
+  num_devices: 2
   source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/model_executor/layers/
+  - vllm/compilation/
   - vllm/v1/worker/
   - vllm/v1/cudagraph_dispatcher.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/test_fusion_attn.py
-  - tests/compile/test_silu_mul_quant_fusion.py
-  - tests/compile/distributed/test_fusion_all_reduce.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  - tests/compile/fullgraph/test_full_graph.py
+  - tests/compile/correctness_e2e/test_sequence_parallel.py
   commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/test_fusion_attn.py
-    - pytest -v -s tests/compile/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
-    # Wrap with quotes to escape yaml
-    - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
-    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
-- label: Fusion E2E (2 GPUs)(B200)
-  timeout_in_minutes: 40
+- label: Sequence Parallel Correctness Tests (2xH100)
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
-  gpu: b200
+  device: h100
   optional: true
-  num_gpus: 2
+  num_devices: 2
+  commands:
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
+
+- label: AsyncTP Correctness Tests (2xH100)
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/"
+  device: h100
+  optional: true
+  num_devices: 2
+  commands:
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
+
+- label: AsyncTP Correctness Tests (B200)
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/"
+  device: b200
+  optional: true
+  num_devices: 2
+  commands:
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
+
+- label: Distributed Compile Unit Tests (2xH100)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/compilation/
+  - vllm/model_executor/layers
+  - tests/compile/passes/distributed/
+  commands:
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -s -v tests/compile/passes/distributed
+
+- label: Fusion and Compile Unit Tests (2xB200)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/"
+  device: b200
   source_file_dependencies:
   - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/
-  # can affect pattern matching
+  - vllm/model_executor/layers/quantization/
   - vllm/model_executor/layers/layernorm.py
   - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/distributed/test_fusions_e2e.py
+  - vllm/model_executor/layers/attention/attention.py
+  - vllm/v1/attention/backends/flashinfer.py
+  - vllm/compilation/ # TODO(luka) limit to vllm/compilation/passes
+  - tests/compile/passes/test_fusion_attn.py
+  - tests/compile/passes/test_mla_attn_quant_fusion.py
+  - tests/compile/passes/test_silu_mul_quant_fusion.py
+  - tests/compile/passes/distributed/test_fusion_all_reduce.py
+  - tests/compile/fullgraph/test_full_graph.py
+  commands:
+    # b200 runners are limited, so we limit the tests to the minimum set only supported on Blackwell
+    - nvidia-smi
+    - pytest -v -s tests/compile/passes/test_fusion_attn.py -k FLASHINFER
+    - pytest -v -s tests/compile/passes/test_mla_attn_quant_fusion.py
+    - pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py
+    # this runner has 2 GPUs available even though num_devices=2 is not set
+    - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
+    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
+    # TODO(luka) move to H100 once pass tests run on H100
+    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+
+- label: Fusion E2E Quick (H100)
+  timeout_in_minutes: 15
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 1
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/model_executor/
+    - vllm/v1/attention/
+    - vllm/compilation/
+    - tests/compile/fusions_e2e/
   commands:
     - nvidia-smi
-    # Run all e2e fusion tests
-    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
+    # Run all models and attn backends but only Inductor partition and native custom ops
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and not +quant_fp8"
+    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
 
+- label: Fusion E2E Config Sweep (H100)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 1
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/compilation/
+    # can affect pattern matching
+    - vllm/model_executor/layers/layernorm.py
+    - vllm/model_executor/layers/activation.py
+    - vllm/model_executor/layers/attention/attention.py
+    - vllm/model_executor/layers/quantization/input_quant_fp8.py
+    - tests/compile/fusions_e2e/
+  commands:
+    - nvidia-smi
+    # Run just llama3 (fp8) for all config combinations
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
+
+- label: Fusion E2E Config Sweep (B200)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/"
+  device: b200
+  num_devices: 1
+  optional: true
+  commands:
+    - nvidia-smi
+    # Run all models but only FLASHINFER, Inductor partition and native custom ops
+    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    # Run just llama3 (fp8 & fp4) for all config combinations (only inductor partition)
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
+
+- label: Fusion E2E TP2 Quick (H100)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/model_executor/
+    - vllm/v1/attention/
+    - vllm/compilation/
+    - tests/compile/fusions_e2e/
+  commands:
+    - nvidia-smi
+    # Run all models and attn backends but only Inductor partition and native custom ops
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
+
+- label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
+  timeout_in_minutes: 40
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/compilation/
+    # can affect pattern matching
+    - vllm/model_executor/layers/layernorm.py
+    - vllm/model_executor/layers/activation.py
+    - vllm/model_executor/layers/attention/attention.py
+    - vllm/model_executor/layers/quantization/input_quant_fp8.py
+    - tests/compile/fusions_e2e/
+  commands:
+    - nvidia-smi
+    # Run just llama3 (fp8 & bf16) for all config combinations
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
+
+- label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
+  timeout_in_minutes: 40
+  working_dir: "/vllm-workspace/"
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/compilation/
+    # can affect pattern matching
+    - vllm/model_executor/layers/layernorm.py
+    - vllm/model_executor/layers/activation.py
+    - vllm/model_executor/layers/attention/attention.py
+    - vllm/model_executor/layers/quantization/input_quant_fp8.py
+    - tests/compile/fusions_e2e/
+  commands:
+    - nvidia-smi
+    # Run just llama3 (fp8 & bf16) for all config combinations
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
+
+- label: Fusion E2E TP2 (B200)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/"
+  device: b200
+  num_devices: 2
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/model_executor/
+    - vllm/v1/attention/
+    - vllm/compilation/
+    - tests/compile/fusions_e2e/
+  commands:
+    - nvidia-smi
+    # Run all models but only FLASHINFER, Inductor partition and native custom ops
+    # include qwen/deepseek with +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    # for ar-rms-quant-fp4, also sweep llama3
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "(FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))) or Llama-3.1-8B-Instruct-FP4"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"

.buildkite/test_areas/cuda.yaml

@@ -4,11 +4,13 @@ depends_on:
 steps:
 - label: Platform Tests (CUDA)
   timeout_in_minutes: 15
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/cuda
   commands:
     - pytest -v -s cuda/test_cuda_context.py
+    - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
   timeout_in_minutes: 20

.buildkite/test_areas/distributed.yaml

@@ -5,7 +5,7 @@ steps:
 - label: Distributed Comm Ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  num_devices: 2
   source_file_dependencies:
   - vllm/distributed
   - tests/distributed
@@ -15,10 +15,31 @@ steps:
   - pytest -v -s distributed/test_shm_buffer.py
   - pytest -v -s distributed/test_shm_storage.py
 
-- label: Distributed (2 GPUs)
-  timeout_in_minutes: 90
+- label: Distributed DP Tests (2 GPUs)
+  timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/v1/distributed
+  - tests/entrypoints/openai/test_multi_api_servers.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
+
+- label: Distributed Compile + RPC Tests (2 GPUs)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
   source_file_dependencies:
   - vllm/compilation/
   - vllm/distributed/
@@ -29,61 +50,80 @@ steps:
   - vllm/v1/worker/
   - tests/compile/fullgraph/test_basic_correctness.py
   - tests/compile/test_wrapper.py
-  - tests/distributed/
   - tests/entrypoints/llm/test_collective_rpc.py
-  - tests/v1/distributed
-  - tests/v1/entrypoints/openai/test_multi_api_servers.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
+
+- label: Distributed Torchrun + Shutdown Tests (2 GPUs)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/distributed/
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - pytest -v -s distributed/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
-- label: Distributed Tests (4 GPUs)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
+- label: Distributed Torchrun + Examples (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace"
+  num_devices: 4
   source_file_dependencies:
   - vllm/distributed/
-  - tests/distributed/test_utils
-  - tests/distributed/test_pynccl
-  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - examples/offline_inference/rlhf.py
+  - tests/distributed/test_torchrun_example.py
+  - tests/distributed/test_torchrun_example_moe.py
   - examples/offline_inference/rlhf_colocate.py
+  - examples/rl/
   - tests/examples/offline_inference/data_parallel.py
-  - tests/v1/distributed
-  - tests/v1/engine/test_engine_core_client.py
-  - tests/distributed/test_symm_mem_allreduce.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
-  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
-  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - PP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=4 and dp=1
-  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - TP_SIZE=4 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2, pp=2 and dp=1
-  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=1 and dp=4 with ep
-  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2 and dp=2 with ep
-  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
+  - python3 examples/offline_inference/data_parallel.py --enforce-eager
+  # rlhf examples
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
+
+- label: Distributed DP Tests (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - tests/v1/distributed
+  - tests/v1/engine/test_engine_core_client.py
+  - tests/distributed/test_utils
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -91,20 +131,32 @@ steps:
   - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
+
+- label: Distributed Compile + Comm (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - tests/distributed/test_pynccl
+  - tests/distributed/test_events
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/distributed/test_symm_mem_allreduce.py
+  - tests/distributed/test_multiproc_executor.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   - pytest -v -s compile/fullgraph/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
   - pytest -v -s distributed/test_symm_mem_allreduce.py
-  # TODO: create a dedicated test section for multi-GPU example tests
-  # when we have multiple distributed example tests
-  - cd ../examples/offline_inference
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  # test multi-node TP with multiproc executor (simulated on single node)
+  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
   timeout_in_minutes: 10
-  gpu: h100
-  num_gpus: 8
+  device: h100
+  num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - examples/offline_inference/torchrun_dp_example.py
@@ -120,9 +172,9 @@ steps:
   - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
-  gpu: a100
+  device: a100
   optional: true
-  num_gpus: 4
+  num_devices: 4
   source_file_dependencies:
   - vllm/
   commands:
@@ -133,26 +185,23 @@ steps:
   - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
   - pytest -v -s -x lora/test_mixtral.py
 
-- label: Distributed Tests (2 GPUs)(H200)
-  gpu: h200
+- label: Distributed Tests (2 GPUs)(H100)
+  timeout_in_minutes: 15
+  device: h100
   optional: true
   working_dir: "/vllm-workspace/"
-  num_gpus: 2
+  num_devices: 2
   commands:
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_async_tp.py
-    - pytest -v -s tests/compile/distributed/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - CUDA_VISIBLE_DEVICES=1,2 VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  gpu: b200
+  device: b200
   optional: true
   working_dir: "/vllm-workspace/"
-  num_gpus: 2
+  num_devices: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
@@ -161,8 +210,10 @@ steps:
 - label: 2 Node Test (4 GPUs)
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  num_devices: 2
   num_nodes: 2
+  no_plugin: true
+  optional: true # TODO: revert once infra issue solved
   source_file_dependencies:
   - vllm/distributed/
   - vllm/engine/
@@ -171,12 +222,12 @@ steps:
   - tests/distributed/
   - tests/examples/offline_inference/data_parallel.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:0bec63fa317e1fbd62e19b0fc31c43c81bf89077 "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
+  num_devices: 4
   source_file_dependencies:
     - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
@@ -184,10 +235,56 @@ steps:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Pipeline + Context Parallelism (4 GPUs))
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs)
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
+  timeout_in_minutes: 30
+  device: a100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+    - vllm/v1/worker/kv_connector_model_runner_mixin.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+
+- label: Pipeline + Context Parallelism (4 GPUs)
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
+  num_devices: 4
   source_file_dependencies:
   - vllm/distributed/
   - vllm/engine/
@@ -196,4 +293,24 @@ steps:
   - tests/distributed/
   commands:
   - pytest -v -s distributed/test_pp_cudagraph.py
-  - pytest -v -s distributed/test_pipeline_parallel.py
+  - pytest -v -s distributed/test_pipeline_parallel.py
+
+- label: RayExecutorV2 (4 GPUs)
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/v1/executor/ray_executor_v2.py
+  - vllm/v1/executor/abstract.py
+  - vllm/v1/executor/multiproc_executor.py
+  - tests/distributed/test_ray_v2_executor.py
+  - tests/distributed/test_ray_v2_executor_e2e.py
+  - tests/distributed/test_pipeline_parallel.py
+  - tests/basic_correctness/test_basic_correctness.py
+  commands:
+  - export VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - pytest -v -s distributed/test_ray_v2_executor.py
+  - pytest -v -s distributed/test_ray_v2_executor_e2e.py
+  - pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
+  - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -k "ray"

.buildkite/test_areas/e2e_integration.yaml

@@ -4,39 +4,36 @@ depends_on:
 steps:
 - label: DeepSeek V2-Lite Accuracy
   timeout_in_minutes: 60
-  gpu: h100
+  device: h100
   optional: true
-  num_gpus: 4
+  num_devices: 4
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
   timeout_in_minutes: 60
-  gpu: h100
+  device: h100
   optional: true
-  num_gpus: 4
+  num_devices: 4
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
   timeout_in_minutes: 60
-  gpu: b200
+  device: b200
   optional: true
-  num_gpus: 2
+  num_devices: 2
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: Prime-RL Integration (2 GPUs)
-  timeout_in_minutes: 30
+- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
+  timeout_in_minutes: 60
+  device: h100
   optional: true
-  soft_fail: true
-  num_gpus: 2
+  num_devices: 1
   working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - vllm/
-  - .buildkite/scripts/run-prime-rl-test.sh
   commands:
-    - bash .buildkite/scripts/run-prime-rl-test.sh
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh 0.25 200 8030

.buildkite/test_areas/engine.yaml

@@ -1,9 +1,10 @@
 group: Engine
-depends_on: 
+depends_on:
   - image-build
 steps:
 - label: Engine
   timeout_in_minutes: 15
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/engine
@@ -14,13 +15,72 @@ steps:
   commands:
   - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
-- label: V1 e2e + engine
-  timeout_in_minutes: 45
+- label: Engine (1 GPU)
+  timeout_in_minutes: 30
+  source_file_dependencies:
+    - vllm/v1/engine/
+    - tests/v1/engine/
+  commands:
+    - pytest -v -s v1/engine/test_preprocess_error_handling.py
+    - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
+
+- label: e2e Scheduling (1 GPU)
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/
+    - tests/v1/e2e/general/
+  commands:
+    - pytest -v -s v1/e2e/general/test_async_scheduling.py
+
+- label: e2e Core (1 GPU)
+  timeout_in_minutes: 30
+  source_file_dependencies:
+    - vllm/v1/
+    - tests/v1/e2e/general/
+  commands:
+    - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
+
+- label: V1 e2e (2 GPUs)
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  optional: true
+  num_devices: 2
   source_file_dependencies:
     - vllm/
-    - tests/v1
+    - tests/v1/e2e
   commands:
-    # TODO: accuracy does not match, whether setting
-    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
-    - pytest -v -s v1/e2e
-    - pytest -v -s v1/engine
+    # Only run tests that need exactly 2 GPUs
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
+  mirror:
+    amd:
+      device: mi325_2
+      depends_on:
+      - image-build-amd
+
+- label: V1 e2e (4 GPUs)
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  optional: true
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/e2e
+  commands:
+    # Only run tests that need 4 GPUs
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
+  mirror:
+    amd:
+      device: mi325_4
+      depends_on:
+      - image-build-amd
+
+- label: V1 e2e (4xH100)
+  timeout_in_minutes: 60
+  device: h100
+  num_devices: 4
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/attention/backends/utils.py
+    - vllm/v1/worker/gpu_model_runner.py
+    - tests/v1/e2e/test_hybrid_chunked_prefill.py
+  commands:
+    - pytest -v -s v1/e2e/test_hybrid_chunked_prefill.py

.buildkite/test_areas/entrypoints.yaml

@@ -10,7 +10,7 @@ steps:
   - tests/entrypoints/
   commands:
   - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
+  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
   timeout_in_minutes: 40
@@ -25,8 +25,8 @@ steps:
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
-- label: Entrypoints Integration (API Server 1)
-  timeout_in_minutes: 130
+- label: Entrypoints Integration (API Server openai - Part 1)
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -34,23 +34,55 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
+  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
+
+- label: Entrypoints Integration (API Server openai - Part 2)
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
+  commands:
+  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - pytest -v -s entrypoints/openai/speech_to_text/
   - pytest -v -s entrypoints/test_chat_utils.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
+- label: Entrypoints Integration (API Server openai - Part 3)
+  timeout_in_minutes: 50
+  device: h200_18gb
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (API Server 2)
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/tool_use
-  - tests/entrypoints/sleep
-  - tests/entrypoints/instrumentator
   - tests/entrypoints/rpc
+  - tests/entrypoints/serve/instrumentator
+  - tests/tool_use
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/serve/instrumentator
   - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
-  - pytest -v -s entrypoints/instrumentator
-  - pytest -v -s entrypoints/sleep
   - pytest -v -s tool_use
 
 - label: Entrypoints Integration (Pooling)
@@ -72,16 +104,9 @@ steps:
   commands:
   - pytest -v -s entrypoints/openai/responses
 
-- label: Entrypoints V1
-  timeout_in_minutes: 50
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  commands:
-    - pytest -v -s v1/entrypoints
-
 - label: OpenAI API Correctness
   timeout_in_minutes: 30
+  device: h200_18gb
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/

.buildkite/test_areas/expert_parallelism.yaml

@@ -4,20 +4,37 @@ depends_on:
 steps:
 - label: EPLB Algorithm
   timeout_in_minutes: 15
+  device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/distributed/eplb
   - tests/distributed/test_eplb_algo.py
+  - tests/distributed/test_eplb_utils.py
   commands:
   - pytest -v -s distributed/test_eplb_algo.py
+  - pytest -v -s distributed/test_eplb_utils.py
 
-- label: EPLB Execution
-  timeout_in_minutes: 20
+- label: EPLB Execution # 17min
+  timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
+  num_devices: 4
   source_file_dependencies:
   - vllm/distributed/eplb
   - tests/distributed/test_eplb_execute.py
   commands:
   - pytest -v -s distributed/test_eplb_execute.py
-  - pytest -v -s distributed/test_eplb_spec_decode.py
+  - pytest -v -s distributed/test_eplb_spec_decode.py
+
+- label: Elastic EP Scaling Test
+  timeout_in_minutes: 20
+  device: h100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/compilation/
+  - tests/distributed/
+  commands:
+  - pytest -v -s distributed/test_elastic_ep.py

.buildkite/test_areas/kernels.yaml

@@ -2,21 +2,34 @@ group: Kernels
 depends_on: 
   - image-build
 steps:
+- label: vLLM IR Tests
+  timeout_in_minutes: 10
+  device: h200_18gb
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+    - vllm/ir
+    - vllm/kernels
+  commands:
+    - pytest -v -s tests/ir
+    - pytest -v -s tests/kernels/ir
+
 - label: Kernels Core Operation Test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
+  - tests/kernels/test_concat_mla_q.py
   commands:
-    - pytest -v -s kernels/core kernels/test_top_k_per_row.py
+    - pytest -v -s kernels/core kernels/test_concat_mla_q.py
 
 - label: Kernels Attention Test %N
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
-  - vllm/attention
   - vllm/v1/attention
+    # TODO: remove this dependency (https://github.com/vllm-project/vllm/issues/32267)
+  - vllm/model_executor/layers/attention
+  - vllm/utils/flashinfer.py
   - tests/kernels/attention
   commands:
     - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
@@ -33,7 +46,7 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test %N
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
   - csrc/moe/
@@ -43,8 +56,9 @@ steps:
   - vllm/envs.py
   - vllm/config
   commands:
-    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 5
 
 - label: Kernels Mamba Test
   timeout_in_minutes: 45
@@ -57,8 +71,8 @@ steps:
 
 - label: Kernels DeepGEMM Test (H100)
   timeout_in_minutes: 45
-  gpu: h100
-  num_gpus: 1
+  device: h100
+  num_devices: 1
   source_file_dependencies:
   - tools/install_deepgemm.sh
   - vllm/utils/deep_gemm.py
@@ -69,7 +83,7 @@ steps:
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
   commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
+    - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
@@ -77,7 +91,7 @@ steps:
 - label: Kernels (B200)
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  gpu: b200
+  device: b200
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
@@ -85,16 +99,17 @@ steps:
   - csrc/quantization/cutlass_w8a8/moe/
   - vllm/model_executor/layers/fused_moe/cutlass_moe.py
   - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_a2a_prepare_finalize.py
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
   - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
+  - tests/kernels/test_top_k_per_row.py
   commands:
     - nvidia-smi
-    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/basic/offline_inference/chat.py
     # Attention
     # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
     - pytest -v -s tests/kernels/attention/test_attention_selector.py
@@ -102,6 +117,7 @@ steps:
     - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
     - pytest -v -s tests/kernels/attention/test_cutlass_mla_decode.py
     - pytest -v -s tests/kernels/attention/test_flashinfer_mla_decode.py
+    - pytest -v -s tests/kernels/test_top_k_per_row.py
     # Quantization
     - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
     - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
@@ -114,4 +130,72 @@ steps:
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
-    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
+    - pytest -v -s tests/kernels/moe/test_flashinfer_moe.py
+    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
+    # e2e
+    - pytest -v -s tests/models/quantization/test_nvfp4.py
+
+- label: Kernels Helion Test
+  timeout_in_minutes: 30
+  device: h100
+  source_file_dependencies:
+  - vllm/utils/import_utils.py
+  - tests/kernels/helion/
+  commands:
+    - pip install helion==0.3.3
+    - pytest -v -s kernels/helion/
+
+ 
+- label: Kernels FP8 MoE Test (1 H100)
+  timeout_in_minutes: 90
+  device: h100
+  num_devices: 1
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_cutlass_moe.py
+    - pytest -v -s kernels/moe/test_flashinfer.py
+    - pytest -v -s kernels/moe/test_gpt_oss_triton_kernels.py
+    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py
+    - pytest -v -s kernels/moe/test_moe.py
+    # - pytest -v -s kernels/moe/test_block_fp8.py - failing on main
+    - pytest -v -s kernels/moe/test_block_int8.py
+    - pytest -v -s kernels/moe/test_triton_moe_no_act_mul.py
+    - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
+
+- label: Kernels FP8 MoE Test (2 H100s)
+  timeout_in_minutes: 90
+  device: h100
+  num_devices: 2
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_deepep_deepgemm_moe.py
+    - pytest -v -s kernels/moe/test_deepep_moe.py
+
+- label: Kernels Fp4 MoE Test (B200)
+  timeout_in_minutes: 60
+  device: b200
+  num_devices: 1
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_cutedsl_moe.py
+    - pytest -v -s kernels/moe/test_flashinfer_moe.py
+    - pytest -v -s kernels/moe/test_nvfp4_moe.py
+    - pytest -v -s kernels/moe/test_ocp_mx_moe.py
+
+
+- label: Kernels FusedMoE Layer Test (2 H100s)
+  timeout_in_minutes: 90
+  device: h100
+  num_devices: 2
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_moe_layer.py
+
+
+- label: Kernels FusedMoE Layer Test (2 B200s)
+  timeout_in_minutes: 90
+  device: b200
+  num_devices: 2
+  optional: true
+  commands:
+    - pytest -v -s kernels/moe/test_moe_layer.py

.buildkite/test_areas/lm_eval.yaml

@@ -11,22 +11,22 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
 
-- label: LM Eval Large Models (4 GPUs)(A100)
-  gpu: a100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+# - label: LM Eval Large Models (4 GPUs)(A100)
+#   device: a100
+#   optional: true
+#   num_devices: 4
+#   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+#   source_file_dependencies:
+#   - csrc/
+#   - vllm/model_executor/layers/quantization
+#   commands:
+#   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
-  gpu: h100
+  device: h100
   optional: true
-  num_gpus: 4
+  num_devices: 4
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
   - csrc/
@@ -37,10 +37,82 @@ steps:
 
 - label: LM Eval Small Models (B200)
   timeout_in_minutes: 120
-  gpu: b200
+  device: b200
   optional: true
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
+
+- label: LM Eval Qwen3.5 Models (B200)
+  timeout_in_minutes: 120
+  device: b200
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/model_executor/models/qwen3_5.py
+  - vllm/model_executor/models/qwen3_5_mtp.py
+  - vllm/transformers_utils/configs/qwen3_5.py
+  - vllm/transformers_utils/configs/qwen3_5_moe.py
+  - vllm/model_executor/models/qwen3_next.py
+  - vllm/model_executor/models/qwen3_next_mtp.py
+  - vllm/model_executor/layers/fla/ops/
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
+
+- label: LM Eval Large Models (H200)
+  timeout_in_minutes: 60
+  device: h200
+  optional: true
+  num_devices: 8
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
+
+- label: MoE Refactor Integration Test (H100 - TEMPORARY)
+  device: h100
+  optional: true
+  num_devices: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
+  
+- label: MoE Refactor Integration Test (B200 - TEMPORARY)
+  device: b200
+  optional: true
+  num_devices: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
+
+- label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
+  device: b200
+  optional: true
+  num_devices: 2
+  commands:
+    - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
+
+
+- label: GPQA Eval (GPT-OSS) (H100)
+  timeout_in_minutes: 120
+  device: h100
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/evals/gpt_oss/
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
+
+- label: GPQA Eval (GPT-OSS) (B200)
+  timeout_in_minutes: 120
+  device: b200
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/evals/gpt_oss/
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt

.buildkite/test_areas/lora.yaml

@@ -8,13 +8,13 @@ steps:
   - vllm/lora
   - tests/lora
   commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py
+    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
   parallelism: 4
 
 
 - label: LoRA TP (Distributed)
   timeout_in_minutes: 30
-  num_gpus: 4
+  num_devices: 4
   source_file_dependencies:
   - vllm/lora
   - tests/lora
@@ -30,4 +30,5 @@ steps:
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/misc.yaml

@@ -2,36 +2,81 @@ group: Miscellaneous
 depends_on: 
   - image-build
 steps:
-- label: V1 Others
-  timeout_in_minutes: 60
+- label: V1 Spec Decode
+  timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/
-    - tests/v1
+    - tests/v1/spec_decode
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    # TODO: create another `optional` test group for slow tests
+    - pytest -v -s -m 'not slow_test' v1/spec_decode
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
+- label: V1 Sample + Logits
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/sample
+    - tests/v1/logits_processors
+    - tests/v1/test_oracle.py
+    - tests/v1/test_request.py
+    - tests/v1/test_outputs.py
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -v -s v1/sample
+    - pytest -v -s v1/logits_processors
+    - pytest -v -s v1/test_oracle.py
+    - pytest -v -s v1/test_request.py
+    - pytest -v -s v1/test_outputs.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
+- label: V1 Core + KV + Metrics
+  timeout_in_minutes: 30
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/core
+    - tests/v1/executor
+    - tests/v1/kv_offload
+    - tests/v1/worker
+    - tests/v1/kv_connector/unit
+    - tests/v1/metrics
+    - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
     - pytest -v -s v1/kv_offload
-    - pytest -v -s v1/sample
-    - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
-    - pytest -v -s v1/spec_decode
     - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
-    - pytest -v -s v1/test_oracle.py
-    - pytest -v -s v1/test_request.py
-    - pytest -v -s v1/test_outputs.py
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: V1 Others (CPU)
-  depends_on: ~
+  depends_on:
+    - image-build-cpu
   source_file_dependencies:
     - vllm/
     - tests/v1
-  no_gpu: true
+  device: cpu-small
   commands:
     # split the test to avoid interference
     - pytest -v -s -m 'cpu_test' v1/core
@@ -42,6 +87,7 @@ steps:
 
 - label: Regression
   timeout_in_minutes: 20
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -59,19 +105,20 @@ steps:
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
-    - python3 offline_inference/basic/chat.py # for basic
-    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
-    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
+     # for basic
+    - python3 basic/offline_inference/chat.py
+    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    - python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
+    - python3 basic/offline_inference/classify.py
+    - python3 basic/offline_inference/embed.py
+    - python3 basic/offline_inference/score.py
     # for multi-modal models
     - python3 offline_inference/audio_language.py --seed 0
     - python3 offline_inference/vision_language.py --seed 0
     - python3 offline_inference/vision_language_multi_image.py --seed 0
     - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
      # for pooling models
-    - python3 pooling/pooling/vision_language_pooling.py --seed 0
+    - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
     - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
@@ -82,7 +129,7 @@ steps:
 
 - label: Metrics, Tracing (2 GPUs)
   timeout_in_minutes: 20
-  num_gpus: 2
+  num_devices: 2
   source_file_dependencies:
   - vllm/
   - tests/v1/tracing
@@ -107,55 +154,50 @@ steps:
   timeout_in_minutes: 50
   source_file_dependencies:
   - vllm/
+  - tests/detokenizer
   - tests/multimodal
   - tests/utils_
   commands:
+  - pytest -v -s detokenizer
   - pytest -v -s -m 'not cpu_test' multimodal
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  depends_on: ~
+  depends_on: 
+  - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/
   - tests/test_inputs.py
   - tests/test_outputs.py
+  - tests/test_pooling_params.py
+  - tests/test_ray_env.py
   - tests/multimodal
   - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
+  - tests/reasoning
   - tests/tool_parsers
   - tests/transformers_utils
   - tests/config
-  no_gpu: true
+  device: cpu-small
   commands:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
+  - pytest -v -s test_pooling_params.py
+  - pytest -v -s test_ray_env.py
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py --ignore=reasoning/test_gemma4_reasoning_parser.py
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
-- label: GPT-OSS Eval (B200)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  optional: true
-  source_file_dependencies:
-  - tests/evals/gpt_oss
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/flashinfer.py
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
-
 - label: Batch Invariance (H100)
-  timeout_in_minutes: 25
-  gpu: h100
+  timeout_in_minutes: 30
+  device: h100
   source_file_dependencies:
     - vllm/v1/attention
     - vllm/model_executor/layers
@@ -164,4 +206,35 @@ steps:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     - pip install pytest-timeout pytest-forked
     - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+
+- label: Batch Invariance (B200)
+  timeout_in_minutes: 30
+  device: b200
+  source_file_dependencies:
+    - vllm/v1/attention
+    - vllm/model_executor/layers
+    - tests/v1/determinism/
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pip install pytest-timeout pytest-forked
+    - pytest -v -s v1/determinism/test_batch_invariance.py
+    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+  
+- label: Acceptance Length Test (Large Models) # optional
+  timeout_in_minutes: 25
+  gpu: h100
+  optional: true
+  num_gpus: 1
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/spec_decode/
+  - vllm/model_executor/models/mlp_speculator.py
+  - tests/v1/spec_decode/test_acceptance_length.py
+  commands:
+    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+    - pytest -v -s v1/spec_decode/test_acceptance_length.py -m slow_test

.buildkite/test_areas/model_executor.yaml

@@ -9,9 +9,9 @@ steps:
   - vllm/config/model.py
   - vllm/model_executor
   - tests/model_executor
-  - tests/entrypoints/openai/test_tensorizer_entrypoint.py
+  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
   commands:
     - apt-get update && apt-get install -y curl libsodium23
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
-    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
+    - pytest -v -s model_executor -m '(not slow_test)'
+    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -0,0 +1,112 @@
+group: Model Runner V2
+depends_on:
+  - image-build
+steps:
+- label: Model Runner V2 Core Tests
+  timeout_in_minutes: 45
+  source_file_dependencies:
+  - vllm/v1/worker/gpu/
+  - vllm/v1/worker/gpu_worker.py
+  - vllm/v1/core/sched/
+  - vllm/v1/attention/
+  - tests/v1/engine/test_llm_engine.py
+  - tests/v1/e2e/
+  - tests/entrypoints/llm/test_struct_output_generate.py
+  commands:
+  - set -x
+  - export VLLM_USE_V2_MODEL_RUNNER=1
+  - pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
+  # This requires eager until we sort out CG correctness issues.
+  # TODO: remove ENFORCE_EAGER here after https://github.com/vllm-project/vllm/pull/32936 is merged.
+  - ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
+  - pytest -v -s v1/e2e/general/test_context_length.py
+  - pytest -v -s v1/e2e/general/test_min_tokens.py
+  # Temporary hack filter to exclude ngram spec decoding based tests.
+  - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
+
+- label: Model Runner V2 Examples
+  timeout_in_minutes: 45
+  working_dir: "/vllm-workspace/examples"
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/core/sched/
+    - vllm/v1/worker/gpu_worker.py
+    - examples/offline_inference/
+    - examples/basic/offline_inference/
+    - examples/pooling/embed/vision_embedding_offline.py
+    - examples/others/tensorize_vllm_model.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    - pip install tensorizer # for tensorizer test
+    - python3 basic/offline_inference/chat.py # for basic
+    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
+    #- python3 basic/offline_inference/embed.py   # TODO
+    # for multi-modal models
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    # for pooling models
+    - python3 pooling/embed/vision_embedding_offline.py --seed 0
+    # for features demo
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+
+- label: Model Runner V2 Distributed (2 GPUs)
+  timeout_in_minutes: 45
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/worker/gpu_worker.py
+    - tests/basic_correctness/test_basic_correctness.py
+    - tests/v1/distributed/test_async_llm_dp.py
+    - tests/v1/distributed/test_eagle_dp.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    # The "and not True" here is a hacky way to exclude the prompt_embeds cases which aren't yet supported.
+    - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
+    # https://github.com/NVIDIA/nccl/issues/1838
+    - export NCCL_CUMEM_HOST_ENABLE=0
+    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
+    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+
+- label: Model Runner V2 Pipeline Parallelism (4 GPUs)
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/worker/gpu_worker.py
+    - tests/distributed/test_pipeline_parallel.py
+    - tests/distributed/test_pp_cudagraph.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    - pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
+    - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
+
+- label: Model Runner V2 Spec Decode
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/worker/gpu/
+  - vllm/v1/worker/gpu_worker.py
+  - tests/v1/spec_decode/test_max_len.py
+  - tests/v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
+  - tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+  - tests/v1/e2e/spec_decode/test_spec_decode.py
+  commands:
+  - set -x
+  - export VLLM_USE_V2_MODEL_RUNNER=1
+  - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
+  - pytest -v -s v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
+  - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+  - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"

.buildkite/test_areas/models_basic.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Models Tests (Initialization)
   timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
+  device: h200_18gb
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -16,7 +16,6 @@ steps:
 
 - label: Basic Models Tests (Extra Initialization) %N
   timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -33,18 +32,27 @@ steps:
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
+  - tests/models/test_terratorch.py
   - tests/models/test_transformers.py
   - tests/models/test_registry.py
   commands:
-    - pytest -v -s models/test_transformers.py models/test_registry.py
+    - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+    
 
 - label: Basic Models Test (Other CPU) # 5min
+  depends_on: 
+  - image-build-cpu
   timeout_in_minutes: 10
   source_file_dependencies:
   - vllm/
   - tests/models/test_utils.py
   - tests/models/test_vision.py
-  no_gpu: true
+  device: cpu-small
   commands:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
@@ -58,7 +66,7 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/basic/offline_inference/chat.py
     - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

.buildkite/test_areas/models_distributed.yaml

@@ -5,7 +5,7 @@ steps:
 - label: Distributed Model Tests (2 GPUs)
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  num_devices: 2
   source_file_dependencies:
   - vllm/model_executor/model_loader/sharded_state_loader.py
   - vllm/model_executor/models/
@@ -14,9 +14,10 @@ steps:
   - tests/models/
   commands:
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
   # Avoid importing model tests that cause CUDA reinitialization error
   - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/language -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
+  - pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
   - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'

.buildkite/test_areas/models_language.yaml

@@ -4,7 +4,6 @@ depends_on:
 steps:
 - label: Language Models Tests (Standard)
   timeout_in_minutes: 25
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -16,7 +15,6 @@ steps:
 
 - label: Language Models Tests (Extra Standard) %N
   timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -32,7 +30,6 @@ steps:
 
 - label: Language Models Tests (Hybrid) %N
   timeout_in_minutes: 75
-  mirror_hardwares: [amdexperimental]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -40,15 +37,14 @@ steps:
   commands:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     # Shard hybrid language model tests
     - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
   parallelism: 2
 
 - label: Language Models Test (Extended Generation) # 80min
   timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -56,13 +52,22 @@ steps:
   commands:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+      commands:
+      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+      - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
   timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
+  device: h200_18gb
   optional: true
   source_file_dependencies:
   - vllm/
@@ -72,17 +77,21 @@ steps:
 
 - label: Language Models Test (Extended Pooling)  # 36min
   timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental]
   optional: true
   source_file_dependencies:
   - vllm/
   - tests/models/language/pooling
   commands:
     - pytest -v -s models/language/pooling -m 'not core_model'
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: Language Models Test (MTEB)
   timeout_in_minutes: 110
-  mirror_hardwares: [amdexperimental]
+  device: h200_18gb
   optional: true
   source_file_dependencies:
   - vllm/