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

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:

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

• Finally, due to a known issue of conflict dependency(oneapi related) in torch-xpu 2.6 and ipex-xpu 2.6, we install ipex here. This will be fixed in the ipex-xpu 2.7.

pip install intel-extension-for-pytorch==2.6.10+xpu \
    --extra-index-url=https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

:::{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 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.

There are some new features coming with ipex-xpu 2.6, e.g. chunked prefill, V1 engine support, lora, MoE, etc.