CPU

vLLM is a Python library that supports the following CPU variants. Select your CPU type to see vendor specific instructions:

x86

vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.

ARM

vLLM has been adapted to work on ARM64 CPUs with NEON support, leveraging the CPU backend initially developed for the x86 platform.

ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.

Apple silicon

vLLM has experimental support for macOS with Apple silicon. For now, users shall build from the source vLLM to natively run on macOS.

Currently the CPU implementation for macOS supports FP32 and FP16 datatypes.

Requirements

• Python: 3.9 – 3.12

x86

• OS: Linux

• Compiler: gcc/g++ >= 12.3.0 (optional, recommended)

• Instruction Set Architecture (ISA): AVX512 (optional, recommended)

ARM

• OS: Linux

• Compiler: gcc/g++ >= 12.3.0 (optional, recommended)

• Instruction Set Architecture (ISA): NEON support is required

Apple silicon

• OS: macOS Sonoma or later

• SDK: XCode 15.4 or later with Command Line Tools

• Compiler: Apple Clang >= 15.0.0

Set up using Python

Create a new Python environment

You can create a new Python environment using conda:

# (Recommended) Create a new conda environment.
conda create -n myenv python=3.12 -y
conda activate myenv

Note

PyTorch has deprecated the conda release channel. If you use conda, please only use it to create Python environment rather than installing packages.

Or you can create a new Python environment using uv, a very fast Python environment manager. Please follow the documentation to install uv. After installing uv, you can create a new Python environment using the following command:

# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
uv venv myenv --python 3.12 --seed
source myenv/bin/activate

Pre-built wheels

Currently, there are no pre-built CPU wheels.

Build wheel from source

x86

First, install recommended compiler. We recommend to use gcc/g++ >= 12.3.0 as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:

sudo apt-get update  -y
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

Second, install Python packages for vLLM CPU backend building:

pip install --upgrade pip
pip install cmake>=3.26 wheel packaging ninja "setuptools-scm>=8" numpy
pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu

Finally, build and install vLLM CPU backend:

VLLM_TARGET_DEVICE=cpu python setup.py install

Note

• AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, will 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.

ARM

First, install recommended compiler. We recommend to use gcc/g++ >= 12.3.0 as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:

sudo apt-get update  -y
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

Second, install Python packages for vLLM CPU backend building:

pip install --upgrade pip
pip install cmake>=3.26 wheel packaging ninja "setuptools-scm>=8" numpy
pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu

Finally, build and install vLLM CPU backend:

VLLM_TARGET_DEVICE=cpu python setup.py install

Testing has been conducted on AWS Graviton3 instances for compatibility.

Apple silicon

After installation of XCode and the Command Line Tools, which include Apple Clang, execute the following commands to build and install vLLM from the source.

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -r requirements-cpu.txt
pip install -e . 

Note

On macOS the VLLM_TARGET_DEVICE is automatically set to cpu, which currently is the only supported device.

Troubleshooting

If the build has error like the following snippet where standard C++ headers cannot be found, try to remove and reinstall your Command Line Tools for Xcode.

[...] fatal error: 'map' file not found
          1 | #include <map>
            |          ^~~~~
      1 error generated.
      [2/8] Building CXX object CMakeFiles/_C.dir/csrc/cpu/pos_encoding.cpp.o

[...] fatal error: 'cstddef' file not found
         10 | #include <cstddef>
            |          ^~~~~~~~~
      1 error generated.

Set up using Docker

Pre-built images

Currently, there are no pre-build CPU images.

Build image from source

$ docker build -f Dockerfile.cpu -t vllm-cpu-env --shm-size=4g .
$ docker run -it \
             --rm \
             --network=host \
             --cpuset-cpus=<cpu-id-list, optional> \
             --cpuset-mems=<memory-node, optional> \
             vllm-cpu-env

Tip

For ARM or Apple silicon, use Dockerfile.arm

Supported features

vLLM CPU backend supports the following vLLM features:

• Tensor Parallel

• Model Quantization (INT8 W8A8, AWQ, GPTQ)

• Chunked-prefill

• Prefix-caching

• FP8-E5M2 KV-Caching (TODO)

Related runtime environment variables

• VLLM_CPU_KVCACHE_SPACE: specify the KV Cache size (e.g, VLLM_CPU_KVCACHE_SPACE=40 means 40 GB 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.

• 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.

Performance tips

• We highly recommend to use TCMalloc for high performance memory allocation and better cache locality. For example, on Ubuntu 22.4, you can run:

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.py # run vLLM

• 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:

export VLLM_CPU_KVCACHE_SPACE=40
export VLLM_CPU_OMP_THREADS_BIND=0-29
vllm serve facebook/opt-125m

• 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. On a hyper-threading enabled platform with 16 logical CPU cores / 8 physical CPU cores:

$ lscpu -e # check the mapping between logical CPU cores and physical CPU cores

# The "CPU" column means the logical CPU core IDs, and the "CORE" column means the physical core IDs. On this platform, two logical cores are sharing one physical core.
CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE    MAXMHZ   MINMHZ      MHZ
0    0      0    0 0:0:0:0          yes 2401.0000 800.0000  800.000
1    0      0    1 1:1:1:0          yes 2401.0000 800.0000  800.000
2    0      0    2 2:2:2:0          yes 2401.0000 800.0000  800.000
3    0      0    3 3:3:3:0          yes 2401.0000 800.0000  800.000
4    0      0    4 4:4:4:0          yes 2401.0000 800.0000  800.000
5    0      0    5 5:5:5:0          yes 2401.0000 800.0000  800.000
6    0      0    6 6:6:6:0          yes 2401.0000 800.0000  800.000
7    0      0    7 7:7:7:0          yes 2401.0000 800.0000  800.000
8    0      0    0 0:0:0:0          yes 2401.0000 800.0000  800.000
9    0      0    1 1:1:1:0          yes 2401.0000 800.0000  800.000
10   0      0    2 2:2:2:0          yes 2401.0000 800.0000  800.000
11   0      0    3 3:3:3:0          yes 2401.0000 800.0000  800.000
12   0      0    4 4:4:4:0          yes 2401.0000 800.0000  800.000
13   0      0    5 5:5:5:0          yes 2401.0000 800.0000  800.000
14   0      0    6 6:6:6:0          yes 2401.0000 800.0000  800.000
15   0      0    7 7:7:7:0          yes 2401.0000 800.0000  800.000

# On this platform, it is recommend to only bind openMP threads on logical CPU cores 0-7 or 8-15
$ export VLLM_CPU_OMP_THREADS_BIND=0-7
$ python examples/offline_inference/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.

Other considerations

• 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.

• 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.

• On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the topology. For NUMA architecture, two optimizations are to recommended: Tensor Parallel or Data Parallel.

  • Using Tensor Parallel for a latency constraints deployment: following GPU backend design, a Megatron-LM’s parallel algorithm will be used to shard the model, based on the number of NUMA nodes (e.g. TP = 2 for a two NUMA node system). With TP feature on CPU merged, 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:

    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
    

  • Using Data Parallel for maximum throughput: to launch an LLM serving endpoint on each NUMA node along with one additional load balancer to dispatch the requests to those endpoints. Common solutions like Nginx or HAProxy are recommended. Anyscale Ray project provides the feature on LLM serving. Here is the example to setup a scalable LLM serving with Ray Serve.