# XPU

The document addresses how to set up the [SGLang](https://github.com/sgl-project/sglang) environment and run LLM inference on Intel GPU, [see more context about Intel GPU support within PyTorch ecosystem](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).

Specifically, SGLang is optimized for [Intel® Arc™ Pro B-Series Graphics](https://www.intel.com/content/www/us/en/ark/products/series/242616/intel-arc-pro-b-series-graphics.html) and [
Intel® Arc™ B-Series Graphics](https://www.intel.com/content/www/us/en/ark/products/series/240391/intel-arc-b-series-graphics.html).

## Optimized Model List

A list of LLMs have been optimized on Intel GPU, and more are on the way:

| Model Name | BF16 |
|:---:|:---:|
| Llama-3.2-3B | [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
| Llama-3.1-8B | [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) |
| Qwen2.5-1.5B |   [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) |

**Note:** The model identifiers listed in the table above
have been verified on [Intel® Arc™ B580 Graphics](https://www.intel.com/content/www/us/en/products/sku/241598/intel-arc-b580-graphics/specifications.html).

## Installation

### Install From Source

Currently SGLang XPU only supports installation from source. Please refer to ["Getting Started on Intel GPU"](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html) to install XPU dependency.

```bash
# Create and activate a conda environment
conda create -n sgl-xpu python=3.12 -y
conda activate sgl-xpu

# Set PyTorch XPU as primary pip install channel to avoid installing the larger CUDA-enabled version and prevent potential runtime issues.
pip3 install torch==2.11.0+xpu torchao torchvision torchaudio==2.11.0+xpu --index-url https://download.pytorch.org/whl/xpu
pip3 install xgrammar --no-deps # xgrammar will introduce CUDA-enabled triton which might conflict with XPU

# Clone the SGLang code
git clone https://github.com/sgl-project/sglang.git
cd sglang
git checkout <YOUR-DESIRED-VERSION>

# Use dedicated toml file
cd python
cp pyproject_xpu.toml pyproject.toml
# Install SGLang dependent libs, and build SGLang main package
pip install --upgrade pip setuptools
pip install -v . --extra-index-url https://download.pytorch.org/whl/xpu
```

### Install Using Docker

The docker for XPU is under active development. Please stay tuned.

## Launch of the Serving Engine

Example command to launch SGLang serving:

```bash
python -m sglang.launch_server       \
    --model <MODEL_ID_OR_PATH>       \
    --trust-remote-code              \
    --disable-overlap-schedule       \
    --device xpu                     \
    --host 0.0.0.0                   \
    --tp 2                           \   # using multi GPUs
    --attention-backend intel_xpu    \   # using intel optimized XPU attention backend
    --page-size                      \   # intel_xpu attention backend supports [32, 64, 128]
```

## Benchmarking with Requests

You can benchmark the performance via the `bench_serving` script.
Run the command in another terminal.

```bash
python -m sglang.bench_serving   \
    --dataset-name random        \
    --random-input-len 1024      \
    --random-output-len 1024     \
    --num-prompts 1              \
    --request-rate inf           \
    --random-range-ratio 1.0
```

The detail explanations of the parameters can be looked up by the command:

```bash
python -m sglang.bench_serving -h
```

Additionally, the requests can be formed with
[OpenAI Completions API](https://docs.sglang.io/basic_usage/openai_api_completions.html)
and sent via the command line (e.g. using `curl`) or via your own script.

## Prefill-Decode (P/D) Disaggregation on Intel XPU [Experimental]

SGLang supports prefill-decode disaggregation on Intel XPU using the [NIXL](https://github.com/ai-dynamo/nixl) KV-transfer backend.

**Tested models:**

| Model | Notes |
|:---:|:---:|
| [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) | Used in integration tests; verified on Intel XPU with homogeneous P/D (XPU prefill + XPU decode) |
| [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | Verified on Intel XPU with homogeneous P/D (XPU prefill + XPU decode) |

**Prerequisites:** `pip install nixl sglang-router`

**Start the prefill server (GPU 0):**

```bash
ZE_AFFINITY_MASK=0 UCX_POSIX_USE_PROC_LINK=n python -m sglang.launch_server \
    --model-path Qwen/Qwen3-0.6B --trust-remote-code --device xpu \
    --disaggregation-mode prefill --disaggregation-transfer-backend nixl \
    --disaggregation-bootstrap-port 12335 --host 0.0.0.0 --port 30000
```

**Start the decode server (GPU 1):**

```bash
ZE_AFFINITY_MASK=1 UCX_POSIX_USE_PROC_LINK=n python -m sglang.launch_server \
    --model-path Qwen/Qwen3-0.6B --trust-remote-code --device xpu \
    --disaggregation-mode decode --disaggregation-transfer-backend nixl \
    --disaggregation-bootstrap-port 12335 --host 0.0.0.0 --port 30001
```

**Start the router:**

```bash
python -m sglang_router.launch_router \
    --pd-disaggregation \
    --prefill http://127.0.0.1:30000 \
    --decode  http://127.0.0.1:30001 \
    --host 0.0.0.0 --port 8000
```

**Send a request:**

```bash
curl http://127.0.0.1:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{"model": "Qwen/Qwen3-0.6B", "prompt": "The capital of France is", "max_tokens": 32}'
```

> **Note:** `UCX_POSIX_USE_PROC_LINK=n` is required on Intel XPU to avoid UCX shared-memory transport issues.