[Doc] [1/N] Reorganize Getting Started section (#11645)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/getting_started/installation/xpu.md

@@ -0,0 +1,74 @@
+(installation-xpu)=
+
+# Installation for XPUs
+
+vLLM initially supports basic model inferencing and serving on Intel GPU platform.
+
+Table of contents:
+
+1. [Requirements](#xpu-backend-requirements)
+2. [Quick start using Dockerfile](#xpu-backend-quick-start-dockerfile)
+3. [Build from source](#build-xpu-backend-from-source)
+
+(xpu-backend-requirements)=
+
+## Requirements
+
+- OS: Linux
+- Supported Hardware: Intel Data Center GPU, Intel ARC GPU
+- OneAPI requirements: oneAPI 2024.2
+
+(xpu-backend-quick-start-dockerfile)=
+
+## Quick start using Dockerfile
+
+```console
+$ 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
+```
+
+(build-xpu-backend-from-source)=
+
+## Build from source
+
+- First, install required driver and intel OneAPI 2024.2 or later.
+- Second, install Python packages for vLLM XPU backend building:
+
+```console
+$ source /opt/intel/oneapi/setvars.sh
+$ pip install --upgrade pip
+$ pip install -v -r requirements-xpu.txt
+```
+
+- Finally, 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 will be supported in the future.
+```
+
+## Distributed inference and serving
+
+XPU platform supports tensor-parallel inference/serving and also supports pipeline parallel as a beta feature for online serving. We requires Ray as the distributed runtime backend. For example, a reference execution likes following:
+
+```console
+$ 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 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/run_cluster.sh> helper script.