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.

!!! warning There are no pre-built wheels for this device, so you need build vLLM from source. Or you can use pre-built images which are based on vLLM released versions.

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• Supported Hardware: Intel Data Center GPU, Intel ARC GPU
• OneAPI requirements: oneAPI 2025.1
• Python: 3.12 !!! warning The provided IPEX whl is Python3.12 specific so this version is a MUST.

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--8<-- [start:set-up-using-python]

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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--8<-- [start:build-wheel-from-source]

• First, install required driver and Intel OneAPI 2025.1 or later.
• Second, install Python packages for vLLM XPU backend building:

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:

VLLM_TARGET_DEVICE=xpu python setup.py install

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--8<-- [start:pre-built-images]

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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--8<-- [start:build-image-from-source]

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

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--8<-- [start:supported-features]

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:

python -m vllm.entrypoints.openai.api_server \
     --model=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 gh-file:examples/online_serving/run_cluster.sh helper script.

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--8<-- [start:distributed-backend]

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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