Migrate docs from Sphinx to MkDocs (#18145)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

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

@@ -0,0 +1,81 @@
+# --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]