[Doc][CPU] Update CPU doc (#30765)

Signed-off-by: jiang1.li <jiang1.li@intel.com>
Signed-off-by: Li, Jiang <bigpyj64@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

docker/Dockerfile.cpu

@@ -17,7 +17,7 @@
 #   VLLM_CPU_DISABLE_AVX512=false (default)|true
 #   VLLM_CPU_AVX512BF16=false (default)|true
 #   VLLM_CPU_AVX512VNNI=false (default)|true
-#   VLLM_CPU_AMXBF16=false (default)|true
+#   VLLM_CPU_AMXBF16=false |true (default)
 #
 
 ######################### COMMON BASE IMAGE #########################
@@ -95,7 +95,7 @@ ENV VLLM_CPU_AVX512BF16=${VLLM_CPU_AVX512BF16}
 ARG VLLM_CPU_AVX512VNNI=0
 ENV VLLM_CPU_AVX512VNNI=${VLLM_CPU_AVX512VNNI}
 # Support for building with AMXBF16 ISA: docker build --build-arg VLLM_CPU_AMXBF16="true" ...
-ARG VLLM_CPU_AMXBF16=0
+ARG VLLM_CPU_AMXBF16=1
 ENV VLLM_CPU_AMXBF16=${VLLM_CPU_AMXBF16}
 
 WORKDIR /workspace/vllm

docs/getting_started/installation/cpu.arm.inc.md

@@ -19,7 +19,7 @@ Pre-built vLLM wheels for Arm are available since version 0.11.2. These wheels c
 
 ```bash
 export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
-uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu --index-strategy first-index
 ```
 
 ??? console "pip"
@@ -27,6 +27,20 @@ uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu
     pip install vllm==${VLLM_VERSION}+cpu --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu
     ```
 
+!!! warning "set `LD_PRELOAD`"
+    Before use vLLM CPU installed via wheels, make sure TCMalloc is installed and added to `LD_PRELOAD`:
+    ```bash
+    # install TCMalloc
+    sudo apt-get install -y --no-install-recommends libtcmalloc-minimal4
+
+    # manually find the path
+    sudo find / -iname *libtcmalloc_minimal.so.4
+    TC_PATH=...
+
+    # add them to LD_PRELOAD
+    export LD_PRELOAD="$TC_PATH:$LD_PRELOAD"
+    ```
+
 The `uv` approach works for vLLM `v0.6.6` and later. A unique feature of `uv` is that packages in `--extra-index-url` have [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes). If the latest public release is `v0.6.6.post1`, `uv`'s behavior allows installing a commit before `v0.6.6.post1` by specifying the `--extra-index-url`. In contrast, `pip` combines packages from `--extra-index-url` and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.
 
 **Install the latest code**
@@ -37,7 +51,7 @@ LLM inference is a fast-evolving field, and the latest code may contain bug fixe
 
 To install from nightly index, run:
 ```bash
-uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu --index-strategy first-index
 ```
 
 ??? console "pip (there's a caveat)"
@@ -56,7 +70,7 @@ If you want to access the wheels for previous commits (e.g. to bisect the behavi
 
 ```bash
 export VLLM_COMMIT=730bd35378bf2a5b56b6d3a45be28b3092d26519 # use full commit hash from the main branch
-uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}/cpu
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}/cpu --index-strategy first-index
 ```
 
 # --8<-- [end:pre-built-wheels]
@@ -105,6 +119,20 @@ VLLM_TARGET_DEVICE=cpu uv pip install -e . --no-build-isolation
 
 Testing has been conducted on AWS Graviton3 instances for compatibility.
 
+!!! warning "set `LD_PRELOAD`"
+    Before use vLLM CPU installed via wheels, make sure TCMalloc is installed and added to `LD_PRELOAD`:
+    ```bash
+    # install TCMalloc
+    sudo apt-get install -y --no-install-recommends libtcmalloc-minimal4
+
+    # manually find the path
+    sudo find / -iname *libtcmalloc_minimal.so.4
+    TC_PATH=...
+
+    # add them to LD_PRELOAD
+    export LD_PRELOAD="$TC_PATH:$LD_PRELOAD"
+    ```
+
 # --8<-- [end:build-wheel-from-source]
 # --8<-- [start:pre-built-images]
 

docs/getting_started/installation/cpu.md

@@ -18,6 +18,12 @@ vLLM is a Python library that supports the following CPU variants. Select your C
 
     --8<-- "docs/getting_started/installation/cpu.s390x.inc.md:installation"
 
+## Technical Discussions
+
+The main discussions happen in the `#sig-cpu` channel of [vLLM Slack](https://slack.vllm.ai/).
+
+When open a Github issue about the CPU backend, please add `[CPU Backend]` in the title and it will be labeled with `cpu` for better awareness.
+
 ## Requirements
 
 - Python: 3.10 -- 3.13
@@ -258,11 +264,6 @@ vLLM CPU supports data parallel (DP), tensor parallel (TP) and pipeline parallel
     - GPTQ (x86 only)
     - compressed-tensor INT8 W8A8 (x86, s390x)
 
-### (x86 only) What is the purpose of `VLLM_CPU_SGL_KERNEL`?
-
-- Both of them require `amx` CPU flag.
-    - `VLLM_CPU_SGL_KERNEL` can provide better performance for MoE models and small-batch scenarios.
-
 ### Why do I see `get_mempolicy: Operation not permitted` when running in Docker?
 
 In some container environments (like Docker), NUMA-related syscalls used by vLLM (e.g., `get_mempolicy`, `migrate_pages`) are blocked/denied in the runtime's default seccomp/capabilities settings. This may lead to warnings like `get_mempolicy: Operation not permitted`. Functionality is not affected, but NUMA memory binding/migration optimizations may not take effect and performance can be suboptimal.

docs/getting_started/installation/cpu.x86.inc.md

@@ -17,7 +17,51 @@ vLLM supports basic model inferencing and serving on x86 CPU platform, with data
 # --8<-- [end:set-up-using-python]
 # --8<-- [start:pre-built-wheels]
 
-Currently, there are no pre-built x86 CPU wheels.
+Pre-built vLLM wheels for x86 with AVX512 are available since version 0.13.0. To install release wheels:
+
+```bash
+export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
+
+# use uv
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu --index-strategy first-index --torch-backend cpu
+```
+??? console "pip"
+    ```bash
+    # use pip
+    pip install vllm==${VLLM_VERSION}+cpu --extra-index-url https://wheels.vllm.ai/${VLLM_VERSION}/cpu --extra-index-url https://download.pytorch.org/whl/cpu
+    ```
+!!! warning "set `LD_PRELOAD`"
+    Before use vLLM CPU installed via wheels, make sure TCMalloc and Intel OpenMP are installed and added to `LD_PRELOAD`:
+    ```bash
+    # install TCMalloc, Intel OpenMP is installed with vLLM CPU
+    sudo apt-get install -y --no-install-recommends libtcmalloc-minimal4
+
+    # manually find the path
+    sudo find / -iname *libtcmalloc_minimal.so.4
+    sudo find / -iname *libiomp5.so
+    TC_PATH=...
+    IOMP_PATH=...
+
+    # add them to LD_PRELOAD
+    export LD_PRELOAD="$TC_PATH:$IOMP_PATH:$LD_PRELOAD"
+    ```
+
+**Install the latest code**
+
+To install the wheel built from the latest main branch:
+
+```bash
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu --index-strategy first-index --torch-backend cpu
+```
+
+**Install specific revisions**
+
+If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:
+
+```bash
+export VLLM_COMMIT=730bd35378bf2a5b56b6d3a45be28b3092d26519 # use full commit hash from the main branch
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}/cpu --index-strategy first-index --torch-backend cpu
+```
 
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]
@@ -26,10 +70,12 @@ Install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the def
 
 ```bash
 sudo apt-get update -y
-sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev
+sudo apt-get install -y gcc-12 g++-12 libnuma-dev
 sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
 ```
 
+--8<-- "docs/getting_started/installation/python_env_setup.inc.md"
+
 Clone the vLLM project:
 
 ```bash
@@ -82,6 +128,22 @@ uv pip install dist/*.whl
     pip install dist/*.whl
     ```
 
+!!! warning "set `LD_PRELOAD`"
+    Before use vLLM CPU installed via wheels, make sure TCMalloc and Intel OpenMP are installed and added to `LD_PRELOAD`:
+    ```bash
+    # install TCMalloc, Intel OpenMP is installed with vLLM CPU
+    sudo apt-get install -y --no-install-recommends libtcmalloc-minimal4
+
+    # manually find the path
+    sudo find / -iname *libtcmalloc_minimal.so.4
+    sudo find / -iname *libiomp5.so
+    TC_PATH=...
+    IOMP_PATH=...
+
+    # add them to LD_PRELOAD
+    export LD_PRELOAD="$TC_PATH:$IOMP_PATH:$LD_PRELOAD"
+    ```
+
 !!! example "Troubleshooting"
     - **NumPy ≥2.0 error**: Downgrade using `pip install "numpy<2.0"`.
     - **CMake picks up CUDA**: Add `CMAKE_DISABLE_FIND_PACKAGE_CUDA=ON` to prevent CUDA detection during CPU builds, even if CUDA is installed.
@@ -95,7 +157,6 @@ uv pip install dist/*.whl
       "torch==X.Y.Z+cpu"   # <-------
     ]
     ```
-    - If you are building vLLM from source and not using the pre-built images, remember to set `LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD"` on x86 machines before running vLLM.
 
 # --8<-- [end:build-wheel-from-source]
 # --8<-- [start:pre-built-images]
@@ -112,6 +173,7 @@ uv pip install dist/*.whl
 docker build -f docker/Dockerfile.cpu \
         --build-arg VLLM_CPU_AVX512BF16=false (default)|true \
         --build-arg VLLM_CPU_AVX512VNNI=false (default)|true \
+        --build-arg VLLM_CPU_AMXBF16=false|true (default) \
         --build-arg VLLM_CPU_DISABLE_AVX512=false (default)|true \ 
         --tag vllm-cpu-env \
         --target vllm-openai .
@@ -123,9 +185,8 @@ docker run --rm \
             --shm-size=4g \
             -p 8000:8000 \
             -e VLLM_CPU_KVCACHE_SPACE=<KV cache space> \
-            -e VLLM_CPU_OMP_THREADS_BIND=<CPU cores for inference> \
             vllm-cpu-env \
-            --model=meta-llama/Llama-3.2-1B-Instruct \
+            meta-llama/Llama-3.2-1B-Instruct \
             --dtype=bfloat16 \
             other vLLM OpenAI server arguments
 ```

docs/getting_started/installation/python_env_setup.inc.md

-On NVIDIA CUDA only, it's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
+It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following commands:
 
 ```bash
 uv venv --python 3.12 --seed