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[Doc] Update CPU doc (#20676)

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Signed-off-by: jiang1.li <jiang1.li@intel.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Li, Jiang
2025-07-10 01:28:30 +08:00
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105
docs/getting_started/installation/cpu.md
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@ -76,78 +76,56 @@ Currently, there are no pre-built CPU wheels.
### Build image from source
??? console "Commands"
=== "Intel/AMD x86"
```bash
docker build -f docker/Dockerfile.cpu \
--tag vllm-cpu-env \
--target vllm-openai .
--8<-- "docs/getting_started/installation/cpu/x86.inc.md:build-image-from-source"
# Launching OpenAI server
docker run --rm \
--privileged=true \
--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 \
--dtype=bfloat16 \
other vLLM OpenAI server arguments
```
=== "ARM AArch64"
!!! tip
For ARM or Apple silicon, use `docker/Dockerfile.arm`
--8<-- "docs/getting_started/installation/cpu/arm.inc.md:build-image-from-source"
!!! tip
For IBM Z (s390x), use `docker/Dockerfile.s390x` and in `docker run` use flag `--dtype float`
=== "Apple silicon"
## Supported features
--8<-- "docs/getting_started/installation/cpu/arm.inc.md:build-image-from-source"
vLLM CPU backend supports the following vLLM features:
- Tensor Parallel
- Model Quantization (`INT8 W8A8, AWQ, GPTQ`)
- Chunked-prefill
- Prefix-caching
- FP8-E5M2 KV cache
=== "IBM Z (S390X)"
--8<-- "docs/getting_started/installation/cpu/s390x.inc.md:build-image-from-source"
## Related runtime environment variables
- `VLLM_CPU_KVCACHE_SPACE`: specify the KV Cache size (e.g, `VLLM_CPU_KVCACHE_SPACE=40` means 40 GiB space for KV cache), larger setting will allow vLLM running more requests in parallel. This parameter should be set based on the hardware configuration and memory management pattern of users. Default value is `0`.
- `VLLM_CPU_OMP_THREADS_BIND`: specify the CPU cores dedicated to the OpenMP threads. For example, `VLLM_CPU_OMP_THREADS_BIND=0-31` means there will be 32 OpenMP threads bound on 0-31 CPU cores. `VLLM_CPU_OMP_THREADS_BIND=0-31|32-63` means there will be 2 tensor parallel processes, 32 OpenMP threads of rank0 are bound on 0-31 CPU cores, and the OpenMP threads of rank1 are bound on 32-63 CPU cores. By setting to `auto`, the OpenMP threads of each rank are bound to the CPU cores in each NUMA node. By setting to `all`, the OpenMP threads of each rank uses all CPU cores available on the system. Default value is `auto`.
- `VLLM_CPU_NUM_OF_RESERVED_CPU`: specify the number of CPU cores which are not dedicated to the OpenMP threads for each rank. The variable only takes effect when VLLM_CPU_OMP_THREADS_BIND is set to `auto`. Default value is `0`.
- `VLLM_CPU_MOE_PREPACK`: whether to use prepack for MoE layer. This will be passed to `ipex.llm.modules.GatedMLPMOE`. Default is `1` (True). On unsupported CPUs, you might need to set this to `0` (False).
- `VLLM_CPU_SGL_KERNEL` (Experimental): whether to use small-batch optimized kernels for linear layer and MoE layer, especially for low-latency requirements like online serving. The kernels require AMX instruction set, BFloat16 weight type and weight shapes divisible by 32. Default is `0` (False).
- `VLLM_CPU_MOE_PREPACK` (x86 only): whether to use prepack for MoE layer. This will be passed to `ipex.llm.modules.GatedMLPMOE`. Default is `1` (True). On unsupported CPUs, you might need to set this to `0` (False).
- `VLLM_CPU_SGL_KERNEL` (x86 only, Experimental): whether to use small-batch optimized kernels for linear layer and MoE layer, especially for low-latency requirements like online serving. The kernels require AMX instruction set, BFloat16 weight type and weight shapes divisible by 32. Default is `0` (False).
## Performance tips
## FAQ
- We highly recommend to use TCMalloc for high performance memory allocation and better cache locality. For example, on Ubuntu 22.4, you can run:
### Which `dtype` should be used?
```bash
sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
find / -name *libtcmalloc* # find the dynamic link library path
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
python examples/offline_inference/basic/basic.py # run vLLM
```
- Currently vLLM CPU uses model default settings as `dtype`. However, due to unstable float16 support in torch CPU, it is recommended to explicitly set `dtype=bfloat16` if there are any performance or accuracy problem.
- When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
### How to launch a vLLM service on CPU?
- When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 31 for the framework and using CPU 0-30 for inference threads:
```bash
export VLLM_CPU_KVCACHE_SPACE=40
export VLLM_CPU_OMP_THREADS_BIND=0-29
vllm serve facebook/opt-125m
export VLLM_CPU_OMP_THREADS_BIND=0-30
vllm serve facebook/opt-125m --dtype=bfloat16
```
or using default auto thread binding:
```bash
export VLLM_CPU_KVCACHE_SPACE=40
export VLLM_CPU_NUM_OF_RESERVED_CPU=2
vllm serve facebook/opt-125m
export VLLM_CPU_NUM_OF_RESERVED_CPU=1
vllm serve facebook/opt-125m --dtype=bfloat16
```
- If using vLLM CPU backend on a machine with hyper-threading, it is recommended to bind only one OpenMP thread on each physical CPU core using `VLLM_CPU_OMP_THREADS_BIND` or using auto thread binding feature by default. On a hyper-threading enabled platform with 16 logical CPU cores / 8 physical CPU cores:
### How to decide `VLLM_CPU_OMP_THREADS_BIND`?
- Bind each OpenMP thread to a dedicated physical CPU core respectively, or use auto thread binding feature by default. On a hyper-threading enabled platform with 16 logical CPU cores / 8 physical CPU cores:
??? console "Commands"
@ -178,34 +156,21 @@ vllm serve facebook/opt-125m
$ python examples/offline_inference/basic/basic.py
```
- If using vLLM CPU backend on a multi-socket machine with NUMA, be aware to set CPU cores using `VLLM_CPU_OMP_THREADS_BIND` to avoid cross NUMA node memory access.
- When deploy vLLM CPU backend on a multi-socket machine with NUMA and enable tensor parallel or pipeline parallel, each NUMA node is treated as a TP/PP rank. So be aware to set CPU cores of a single rank on a same NUMA node to avoid cross NUMA node memory access.
## Other considerations
### How to decide `VLLM_CPU_KVCACHE_SPACE`?
- The CPU backend significantly differs from the GPU backend since the vLLM architecture was originally optimized for GPU use. A number of optimizations are needed to enhance its performance.
- This value is 4GB by default. Larger space can support more concurrent requests, longer context length. However, users should take care of memory capacity of each NUMA node. The memory usage of each TP rank is the sum of `weight shard size` and `VLLM_CPU_KVCACHE_SPACE`, if it exceeds the capacity of a single NUMA node, the TP worker will be killed with `exitcode 9` due to out-of-memory.
- Decouple the HTTP serving components from the inference components. In a GPU backend configuration, the HTTP serving and tokenization tasks operate on the CPU, while inference runs on the GPU, which typically does not pose a problem. However, in a CPU-based setup, the HTTP serving and tokenization can cause significant context switching and reduced cache efficiency. Therefore, it is strongly recommended to segregate these two components for improved performance.
### Which quantization configs does vLLM CPU support?
- On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the [topology](https://github.com/intel/intel-extension-for-pytorch/blob/main/docs/tutorials/performance_tuning/tuning_guide.md#non-uniform-memory-access-numa). For NUMA architecture, Tensor Parallel is a option for better performance.
- vLLM CPU supports quantizations:
- AWQ (x86 only)
- GPTQ (x86 only)
- compressed-tensor INT8 W8A8 (x86, s390x)
- Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving:
### (x86 only) What is the purpose of `VLLM_CPU_MOE_PREPACK` and `VLLM_CPU_SGL_KERNEL`?
```bash
VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" \
vllm serve meta-llama/Llama-2-7b-chat-hf \
-tp=2 \
--distributed-executor-backend mp
```
or using default auto thread binding:
```bash
VLLM_CPU_KVCACHE_SPACE=40 \
vllm serve meta-llama/Llama-2-7b-chat-hf \
-tp=2 \
--distributed-executor-backend mp
```
- For each thread id list in `VLLM_CPU_OMP_THREADS_BIND`, users should guarantee threads in the list belong to a same NUMA node.
- Meanwhile, users should also take care of memory capacity of each NUMA node. The memory usage of each TP rank is the sum of `weight shard size` and `VLLM_CPU_KVCACHE_SPACE`, if it exceeds the capacity of a single NUMA node, TP worker will be killed due to out-of-memory.
- Both of them requires `amx` CPU flag.
- `VLLM_CPU_MOE_PREPACK` can provides better performance for MoE models
- `VLLM_CPU_SGL_KERNEL` can provides better performance for MoE models and small-batch scenarios.

15
docs/getting_started/installation/cpu/arm.inc.md
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@ -32,7 +32,22 @@ Testing has been conducted on AWS Graviton3 instances for compatibility.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
```bash
docker build -f docker/Dockerfile.arm \
--tag vllm-cpu-env .
# Launching OpenAI server
docker run --rm \
--privileged=true \
--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 \
--dtype=bfloat16 \
other vLLM OpenAI server arguments
```
# --8<-- [end:build-image-from-source]
# --8<-- [start:extra-information]
# --8<-- [end:extra-information]

7
docs/getting_started/installation/cpu/build.inc.md
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@ -2,7 +2,7 @@ First, install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as
```bash
sudo apt-get update -y
sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev
sudo apt-get install -y --no-install-recommends ccache git curl wget ca-certificates gcc-12 g++-12 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 jq lsof
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
```
@ -17,7 +17,7 @@ Third, install Python packages for vLLM CPU backend building:
```bash
pip install --upgrade pip
pip install "cmake>=3.26.1" wheel packaging ninja "setuptools-scm>=8" numpy
pip install -v -r requirements/cpu-build.txt --extra-index-url https://download.pytorch.org/whl/cpu
pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
```
@ -33,4 +33,7 @@ If you want to develop vllm, install it in editable mode instead.
VLLM_TARGET_DEVICE=cpu python setup.py develop
```
!!! note
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:extra-information]

17
docs/getting_started/installation/cpu/s390x.inc.md
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@ -61,6 +61,23 @@ Execute the following commands to build and install vLLM from the source.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
```bash
docker build -f docker/Dockerfile.s390x \
--tag vllm-cpu-env .
# Launching OpenAI server
docker run --rm \
--privileged=true \
--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 \
--dtype=float \
other vLLM OpenAI server arguments
```
# --8<-- [end:build-image-from-source]
# --8<-- [start:extra-information]
# --8<-- [end:extra-information]

39
docs/getting_started/installation/cpu/x86.inc.md
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@ -1,19 +1,15 @@
# --8<-- [start:installation]
vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.
!!! warning
There are no pre-built wheels or images for this device, so you must build vLLM from source.
vLLM supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.
# --8<-- [end:installation]
# --8<-- [start:requirements]
- OS: Linux
- Compiler: `gcc/g++ >= 12.3.0` (optional, recommended)
- Instruction Set Architecture (ISA): AVX512 (optional, recommended)
- CPU flags: `avx512f`, `avx512_bf16` (Optional), `avx512_vnni` (Optional)
!!! tip
[Intel Extension for PyTorch (IPEX)](https://github.com/intel/intel-extension-for-pytorch) extends PyTorch with up-to-date features optimizations for an extra performance boost on Intel hardware.
Use `lscpu` to check the CPU flags.
# --8<-- [end:requirements]
# --8<-- [start:set-up-using-python]
@ -26,18 +22,37 @@ vLLM initially supports basic model inferencing and serving on x86 CPU platform,
--8<-- "docs/getting_started/installation/cpu/build.inc.md"
!!! note
- AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, which brings some performance improvement compared with pure AVX512. The CPU backend build script will check the host CPU flags to determine whether to enable AVX512_BF16.
- If you want to force enable AVX512_BF16 for the cross-compilation, please set environment variable `VLLM_CPU_AVX512BF16=1` before the building.
# --8<-- [end:build-wheel-from-source]
# --8<-- [start:pre-built-images]
See [https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo](https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo)
[https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo](https://gallery.ecr.aws/q9t5s3a7/vllm-cpu-release-repo)
!!! warning
If deploying the pre-built images on machines only contain `avx512f`, `Illegal instruction` error may be raised. It is recommended to build images for these machines with `--build-arg VLLM_CPU_AVX512BF16=false` and `--build-arg VLLM_CPU_AVX512VNNI=false`.
# --8<-- [end:pre-built-images]
# --8<-- [start:build-image-from-source]
```bash
docker build -f docker/Dockerfile.cpu \
--build-arg VLLM_CPU_AVX512BF16=false (default)|true \
--build-arg VLLM_CPU_AVX512VNNI=false (default)|true \
--tag vllm-cpu-env \
--target vllm-openai .
# Launching OpenAI server
docker run --rm \
--privileged=true \
--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 \
--dtype=bfloat16 \
other vLLM OpenAI server arguments
```
# --8<-- [end:build-image-from-source]
# --8<-- [start:extra-information]
# --8<-- [end:extra-information]