vllm/docs/getting_started/installation/gpu.xpu.inc.md

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vLLM initially supports basic model inference and serving on Intel GPU platform.

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• Supported Hardware: Intel Data Center GPU, Intel ARC GPU
• Dependency: vllm-xpu-kernels: a package provide all necessary vllm custom kernel when running vLLM on Intel GPU platform,
• Python: 3.12 !!! warning The provided vllm-xpu-kernels whl is Python3.12 specific so this version is a MUST.

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There is no extra information on creating a new Python environment for this device.

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Currently, there are no pre-built XPU wheels.

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• First, install required driver.
• Second, install Python packages for vLLM XPU backend building (Intel OneAPI dependencies are installed automatically as part of torch-xpu, see PyTorch XPU get started):

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install --upgrade pip
pip install -v -r requirements/xpu.txt

• Then, install the correct Triton package for Intel XPU.

  The default triton package (for NVIDIA GPUs) may be installed as a transitive dependency (e.g., via xgrammar). For Intel XPU, you must replace it with triton-xpu:

  pip uninstall -y triton triton-xpu
  pip install triton-xpu==3.6.0 --extra-index-url https://download.pytorch.org/whl/xpu
  

  !!! note - triton (without suffix) is for NVIDIA GPUs only. On XPU, using it instead of triton-xpu can cause correctness or runtime issues. - For torch 2.10 (the version used in requirements/xpu.txt), the matching package is triton-xpu==3.6.0. If you use a different version of torch, check the corresponding triton-xpu version in docker/Dockerfile.xpu.

• Finally, build and install vLLM XPU backend:

VLLM_TARGET_DEVICE=xpu pip install --no-build-isolation -e . -v

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Currently, we release prebuilt XPU images at docker hub based on vLLM released version. For more information, please refer release note.

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docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
docker run -it \
             --rm \
             --network=host \
             --device /dev/dri:/dev/dri \
             -v /dev/dri/by-path:/dev/dri/by-path \
             --ipc=host \
             --privileged \
             vllm-xpu-env

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XPU platform supports tensor parallel inference/serving and also supports pipeline parallel as a beta feature for online serving. For pipeline parallel, we support it on single node with mp as the backend. For example, a reference execution like following:

vllm serve facebook/opt-13b \
     --dtype=bfloat16 \
     --max_model_len=1024 \
     --distributed-executor-backend=mp \
     --pipeline-parallel-size=2 \
     -tp=8

By default, a ray instance will be launched automatically if no existing one is detected in the system, with num-gpus equals to parallel_config.world_size. We recommend properly starting a ray cluster before execution, referring to the examples/online_serving/run_cluster.sh helper script.

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XPU platform uses torch-ccl for torch<2.8 and xccl for torch>=2.8 as distributed backend, since torch 2.8 supports xccl as built-in backend for XPU.

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