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Installation

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

:::{attention} There are no pre-built wheels or images for this device, so you must build vLLM from source. :::

Requirements

Supported Hardware: Intel Data Center GPU, Intel ARC GPU
OneAPI requirements: oneAPI 2025.0

Set up using Python

Pre-built wheels

Currently, there are no pre-built XPU wheels.

Build wheel from source

First, install required driver and Intel OneAPI 2025.0 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

:::{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. :::

Set up using Docker

Pre-built images

Currently, there are no pre-built XPU images.

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

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:

python -m vllm.entrypoints.openai.api_server \
     --model=facebook/opt-13b \
     --dtype=bfloat16 \
     --device=xpu \
     --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.

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