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Migrate docs from Sphinx to MkDocs (#18145)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
2025-05-23 11:09:53 +02:00
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# --8<-- [start:installation]
vLLM contains pre-compiled C++ and CUDA (12.8) binaries.
# --8<-- [end:installation]
# --8<-- [start:requirements]
- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]
### Create a new Python environment
!!! note
PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
In order to be performant, vLLM has to compile many cuda kernels. The compilation unfortunately introduces binary incompatibility with other CUDA versions and PyTorch versions, even for the same PyTorch version with different building configurations.
Therefore, it is recommended to install vLLM with a **fresh new** environment. If either you have a different CUDA version or you want to use an existing PyTorch installation, you need to build vLLM from source. See [below][build-from-source] for more details.
# --8<-- [end:set-up-using-python]
# --8<-- [start:pre-built-wheels]
You can install vLLM using either `pip` or `uv pip`:
```console
# Install vLLM with CUDA 12.8.
# If you are using pip.
pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128
# If you are using uv.
uv pip install vllm --torch-backend=auto
```
We recommend leveraging `uv` to [automatically select the appropriate PyTorch index at runtime](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection) by inspecting the installed CUDA driver version via `--torch-backend=auto` (or `UV_TORCH_BACKEND=auto`). To select a specific backend (e.g., `cu126`), set `--torch-backend=cu126` (or `UV_TORCH_BACKEND=cu126`). If this doesn't work, try running `uv self update` to update `uv` first.
!!! note
NVIDIA Blackwell GPUs (B200, GB200) require a minimum of CUDA 12.8, so make sure you are installing PyTorch wheels with at least that version. PyTorch itself offers a [dedicated interface](https://pytorch.org/get-started/locally/) to determine the appropriate pip command to run for a given target configuration.
As of now, vLLM's binaries are compiled with CUDA 12.8 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 12.6, 11.8, and public PyTorch release versions:
```console
# Install vLLM with CUDA 11.8.
export VLLM_VERSION=0.6.1.post1
export PYTHON_VERSION=312
uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
```
[](){ #install-the-latest-code }
#### Install the latest code
LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on a x86 platform with CUDA 12 for every commit since `v0.5.3`.
##### Install the latest code using `pip`
```console
pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```
`--pre` is required for `pip` to consider pre-released versions.
Another way to install the latest code is to use `uv`:
```console
uv pip install -U vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly
```
##### Install specific revisions using `pip`
If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), due to the limitation of `pip`, you have to specify the full URL of the wheel file by embedding the commit hash in the URL:
```console
export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
pip install https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
```
Note that the wheels are built with Python 3.8 ABI (see [PEP 425](https://peps.python.org/pep-0425/) for more details about ABI), so **they are compatible with Python 3.8 and later**. The version string in the wheel file name (`1.0.0.dev`) is just a placeholder to have a unified URL for the wheels, the actual versions of wheels are contained in the wheel metadata (the wheels listed in the extra index url have correct versions). Although we don't support Python 3.8 any more (because PyTorch 2.5 dropped support for Python 3.8), the wheels are still built with Python 3.8 ABI to keep the same wheel name as before.
##### Install specific revisions using `uv`
If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:
```console
export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}
```
The `uv` approach works for vLLM `v0.6.6` and later and offers an easy-to-remember command. A unique feature of `uv` is that packages in `--extra-index-url` have [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes). If the latest public release is `v0.6.6.post1`, `uv`'s behavior allows installing a commit before `v0.6.6.post1` by specifying the `--extra-index-url`. In contrast, `pip` combines packages from `--extra-index-url` and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.
# --8<-- [end:pre-built-wheels]
# --8<-- [start:build-wheel-from-source]
#### Set up using Python-only build (without compilation)
If you only need to change Python code, you can build and install vLLM without compilation. Using `pip`'s [`--editable` flag](https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs), changes you make to the code will be reflected when you run vLLM:
```console
git clone https://github.com/vllm-project/vllm.git
cd vllm
VLLM_USE_PRECOMPILED=1 pip install --editable .
```
This command will do the following:
1. Look for the current branch in your vLLM clone.
2. Identify the corresponding base commit in the main branch.
3. Download the pre-built wheel of the base commit.
4. Use its compiled libraries in the installation.
!!! note
1. If you change C++ or kernel code, you cannot use Python-only build; otherwise you will see an import error about library not found or undefined symbol.
2. If you rebase your dev branch, it is recommended to uninstall vllm and re-run the above command to make sure your libraries are up to date.
In case you see an error about wheel not found when running the above command, it might be because the commit you based on in the main branch was just merged and the wheel is being built. In this case, you can wait for around an hour to try again, or manually assign the previous commit in the installation using the `VLLM_PRECOMPILED_WHEEL_LOCATION` environment variable.
```console
export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
pip install --editable .
```
You can find more information about vLLM's wheels in [install-the-latest-code][install-the-latest-code].
!!! note
There is a possibility that your source code may have a different commit ID compared to the latest vLLM wheel, which could potentially lead to unknown errors.
It is recommended to use the same commit ID for the source code as the vLLM wheel you have installed. Please refer to [install-the-latest-code][install-the-latest-code] for instructions on how to install a specified wheel.
#### Full build (with compilation)
If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes:
```console
git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e .
```
!!! tip
Building from source requires a lot of compilation. If you are building from source repeatedly, it's more efficient to cache the compilation results.
For example, you can install [ccache](https://github.com/ccache/ccache) using `conda install ccache` or `apt install ccache` .
As long as `which ccache` command can find the `ccache` binary, it will be used automatically by the build system. After the first build, subsequent builds will be much faster.
When using `ccache` with `pip install -e .`, you should run `CCACHE_NOHASHDIR="true" pip install --no-build-isolation -e .`. This is because `pip` creates a new folder with a random name for each build, preventing `ccache` from recognizing that the same files are being built.
[sccache](https://github.com/mozilla/sccache) works similarly to `ccache`, but has the capability to utilize caching in remote storage environments.
The following environment variables can be set to configure the vLLM `sccache` remote: `SCCACHE_BUCKET=vllm-build-sccache SCCACHE_REGION=us-west-2 SCCACHE_S3_NO_CREDENTIALS=1`. We also recommend setting `SCCACHE_IDLE_TIMEOUT=0`.
##### Use an existing PyTorch installation
There are scenarios where the PyTorch dependency cannot be easily installed via pip, e.g.:
- Building vLLM with PyTorch nightly or a custom PyTorch build.
- Building vLLM with aarch64 and CUDA (GH200), where the PyTorch wheels are not available on PyPI. Currently, only the PyTorch nightly has wheels for aarch64 with CUDA. You can run `pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124` to [install PyTorch nightly](https://pytorch.org/get-started/locally/), and then build vLLM on top of it.
To build vLLM using an existing PyTorch installation:
```console
git clone https://github.com/vllm-project/vllm.git
cd vllm
python use_existing_torch.py
pip install -r requirements/build.txt
pip install --no-build-isolation -e .
```
##### Use the local cutlass for compilation
Currently, before starting the build process, vLLM fetches cutlass code from GitHub. However, there may be scenarios where you want to use a local version of cutlass instead.
To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to point to your local cutlass directory.
```console
git clone https://github.com/vllm-project/vllm.git
cd vllm
VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
```
##### Troubleshooting
To avoid your system being overloaded, you can limit the number of compilation jobs
to be run simultaneously, via the environment variable `MAX_JOBS`. For example:
```console
export MAX_JOBS=6
pip install -e .
```
This is especially useful when you are building on less powerful machines. For example, when you use WSL it only [assigns 50% of the total memory by default](https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings), so using `export MAX_JOBS=1` can avoid compiling multiple files simultaneously and running out of memory.
A side effect is a much slower build process.
Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
```console
# Use `--ipc=host` to make sure the shared memory is large enough.
docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
```
If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from [the official website](https://developer.nvidia.com/cuda-toolkit-archive). After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
```console
export CUDA_HOME=/usr/local/cuda
export PATH="${CUDA_HOME}/bin:$PATH"
```
Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
```console
nvcc --version # verify that nvcc is in your PATH
${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
```
#### Unsupported OS build
vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems.
Simply disable the `VLLM_TARGET_DEVICE` environment variable before installing:
```console
export VLLM_TARGET_DEVICE=empty
pip install -e .
```
# --8<-- [end:build-wheel-from-source]
# --8<-- [start:set-up-using-docker]
# --8<-- [end:set-up-using-docker]
# --8<-- [start:pre-built-images]
See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for instructions on using the official Docker image.
Another way to access the latest code is to use the docker images:
```console
export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
docker pull public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:${VLLM_COMMIT}
```
These docker images are used for CI and testing only, and they are not intended for production use. They will be expired after several days.
The latest code can contain bugs and may not be stable. Please use it with caution.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
See [deployment-docker-build-image-from-source][deployment-docker-build-image-from-source] for instructions on building the Docker image.
## Supported features
See [feature-x-hardware][feature-x-hardware] compatibility matrix for feature support information.
# --8<-- [end:extra-information]

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# --8<-- [start:installation]
vLLM supports AMD GPUs with ROCm 6.3.
!!! warning
There are no pre-built wheels for this device, so you must either use the pre-built Docker image or build vLLM from source.
# --8<-- [end:installation]
# --8<-- [start:requirements]
- GPU: MI200s (gfx90a), MI300 (gfx942), Radeon RX 7900 series (gfx1100/1101), Radeon RX 9000 series (gfx1200/1201)
- ROCm 6.3
# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]
# --8<-- [end:set-up-using-python]
# --8<-- [start:pre-built-wheels]
Currently, there are no pre-built ROCm wheels.
# --8<-- [end:pre-built-wheels]
# --8<-- [start:build-wheel-from-source]
0. Install prerequisites (skip if you are already in an environment/docker with the following installed):
- [ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html)
- [PyTorch](https://pytorch.org/)
For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.3_ubuntu24.04_py3.12_pytorch_release_2.4.0`, `rocm/pytorch-nightly`. If you are using docker image, you can skip to Step 3.
Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/). Example:
```console
# Install PyTorch
$ pip uninstall torch -y
$ pip install --no-cache-dir --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3
```
1. Install [Triton flash attention for ROCm](https://github.com/ROCm/triton)
Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
```console
python3 -m pip install ninja cmake wheel pybind11
pip uninstall -y triton
git clone https://github.com/OpenAI/triton.git
cd triton
git checkout e5be006
cd python
pip3 install .
cd ../..
```
!!! note
If you see HTTP issue related to downloading packages during building triton, please try again as the HTTP error is intermittent.
2. Optionally, if you choose to use CK flash attention, you can install [flash attention for ROCm](https://github.com/ROCm/flash-attention)
Install ROCm's flash attention (v2.7.2) following the instructions from [ROCm/flash-attention](https://github.com/ROCm/flash-attention#amd-rocm-support)
Alternatively, wheels intended for vLLM use can be accessed under the releases.
For example, for ROCm 6.3, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
```console
git clone https://github.com/ROCm/flash-attention.git
cd flash-attention
git checkout b7d29fb
git submodule update --init
GPU_ARCHS="gfx90a" python3 setup.py install
cd ..
```
!!! note
You might need to downgrade the "ninja" version to 1.10 as it is not used when compiling flash-attention-2 (e.g. `pip install ninja==1.10.2.4`)
3. If you choose to build AITER yourself to use a certain branch or commit, you can build AITER using the following steps:
```console
python3 -m pip uninstall -y aiter
git clone --recursive https://github.com/ROCm/aiter.git
cd aiter
git checkout $AITER_BRANCH_OR_COMMIT
git submodule sync; git submodule update --init --recursive
python3 setup.py develop
```
!!! note
You will need to config the `$AITER_BRANCH_OR_COMMIT` for your purpose.
4. Build vLLM. For example, vLLM on ROCM 6.3 can be built with the following steps:
```bash
$ pip install --upgrade pip
# Build & install AMD SMI
$ pip install /opt/rocm/share/amd_smi
# Install dependencies
$ pip install --upgrade numba scipy huggingface-hub[cli,hf_transfer] setuptools_scm
$ pip install "numpy<2"
$ pip install -r requirements/rocm.txt
# Build vLLM for MI210/MI250/MI300.
$ export PYTORCH_ROCM_ARCH="gfx90a;gfx942"
$ python3 setup.py develop
```
This may take 5-10 minutes. Currently, `pip install .` does not work for ROCm installation.
!!! tip
- Triton flash attention is used by default. For benchmarking purposes, it is recommended to run a warm up step before collecting perf numbers.
- Triton flash attention does not currently support sliding window attention. If using half precision, please use CK flash-attention for sliding window support.
- To use CK flash-attention or PyTorch naive attention, please use this flag `export VLLM_USE_TRITON_FLASH_ATTN=0` to turn off triton flash attention.
- The ROCm version of PyTorch, ideally, should match the ROCm driver version.
!!! tip
- For MI300x (gfx942) users, to achieve optimal performance, please refer to [MI300x tuning guide](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/index.html) for performance optimization and tuning tips on system and workflow level.
For vLLM, please refer to [vLLM performance optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#vllm-performance-optimization).
## Set up using Docker (Recommended)
# --8<-- [end:set-up-using-docker]
# --8<-- [start:pre-built-images]
The [AMD Infinity hub for vLLM](https://hub.docker.com/r/rocm/vllm/tags) offers a prebuilt, optimized
docker image designed for validating inference performance on the AMD Instinct™ MI300X accelerator.
!!! tip
Please check [LLM inference performance validation on AMD Instinct MI300X](https://rocm.docs.amd.com/en/latest/how-to/performance-validation/mi300x/vllm-benchmark.html)
for instructions on how to use this prebuilt docker image.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
Building the Docker image from source is the recommended way to use vLLM with ROCm.
#### (Optional) Build an image with ROCm software stack
Build a docker image from <gh-file:docker/Dockerfile.rocm_base> which setup ROCm software stack needed by the vLLM.
**This step is optional as this rocm_base image is usually prebuilt and store at [Docker Hub](https://hub.docker.com/r/rocm/vllm-dev) under tag `rocm/vllm-dev:base` to speed up user experience.**
If you choose to build this rocm_base image yourself, the steps are as follows.
It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
```console
{
"features": {
"buildkit": true
}
}
```
To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
```console
DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm_base -t rocm/vllm-dev:base .
```
#### Build an image with vLLM
First, build a docker image from <gh-file:docker/Dockerfile.rocm> and launch a docker container from the image.
It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
```console
{
"features": {
"buildkit": true
}
}
```
<gh-file:docker/Dockerfile.rocm> uses ROCm 6.3 by default, but also supports ROCm 5.7, 6.0, 6.1, and 6.2, in older vLLM branches.
It provides flexibility to customize the build of docker image using the following arguments:
- `BASE_IMAGE`: specifies the base image used when running `docker build`. The default value `rocm/vllm-dev:base` is an image published and maintained by AMD. It is being built using <gh-file:docker/Dockerfile.rocm_base>
- `USE_CYTHON`: An option to run cython compilation on a subset of python files upon docker build
- `BUILD_RPD`: Include RocmProfileData profiling tool in the image
- `ARG_PYTORCH_ROCM_ARCH`: Allows to override the gfx architecture values from the base docker image
Their values can be passed in when running `docker build` with `--build-arg` options.
To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
```console
DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .
```
To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should pick the alternative base image:
```console
DOCKER_BUILDKIT=1 docker build --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" -f docker/Dockerfile.rocm -t vllm-rocm .
```
To run the above docker image `vllm-rocm`, use the below command:
```console
docker run -it \
--network=host \
--group-add=video \
--ipc=host \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--device /dev/kfd \
--device /dev/dri \
-v <path/to/model>:/app/model \
vllm-rocm \
bash
```
Where the `<path/to/model>` is the location where the model is stored, for example, the weights for llama2 or llama3 models.
## Supported features
See [feature-x-hardware][feature-x-hardware] compatibility matrix for feature support information.
# --8<-- [end:extra-information]

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# --8<-- [start:installation]
vLLM initially supports basic model inference and serving on Intel GPU platform.
!!! warning
There are no pre-built wheels or images for this device, so you must build vLLM from source.
# --8<-- [end:installation]
# --8<-- [start:requirements]
- Supported Hardware: Intel Data Center GPU, Intel ARC GPU
- OneAPI requirements: oneAPI 2025.0
# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]
# --8<-- [end:set-up-using-python]
# --8<-- [start:pre-built-wheels]
Currently, there are no pre-built XPU wheels.
# --8<-- [end:pre-built-wheels]
# --8<-- [start:build-wheel-from-source]
- First, install required driver and Intel OneAPI 2025.0 or later.
- Second, install Python packages for vLLM XPU backend building:
```console
git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install --upgrade pip
pip install -v -r requirements/xpu.txt
```
- Then, build and install vLLM XPU backend:
```console
VLLM_TARGET_DEVICE=xpu python setup.py install
```
!!! note
- FP16 is the default data type in the current XPU backend. The BF16 data
type is supported on Intel Data Center GPU, not supported on Intel Arc GPU yet.
# --8<-- [end:build-wheel-from-source]
# --8<-- [start:set-up-using-docker]
# --8<-- [end:set-up-using-docker]
# --8<-- [start:pre-built-images]
Currently, there are no pre-built XPU images.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
```console
$ docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
$ docker run -it \
--rm \
--network=host \
--device /dev/dri \
-v /dev/dri/by-path:/dev/dri/by-path \
vllm-xpu-env
```
## Supported features
XPU platform supports **tensor parallel** inference/serving and also supports **pipeline parallel** as a beta feature for online serving. We require Ray as the distributed runtime backend. For example, a reference execution like following:
```console
python -m vllm.entrypoints.openai.api_server \
--model=facebook/opt-13b \
--dtype=bfloat16 \
--max_model_len=1024 \
--distributed-executor-backend=ray \
--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 <gh-file:examples/online_serving/run_cluster.sh> helper script.
# --8<-- [end:extra-information]