CPU Servers

The document addresses how to set up the SGLang environment and run LLM inference on CPU servers. SGLang is enabled and optimized on the CPUs equipped with Intel® AMX® Instructions, which are 4th generation or newer Intel® Xeon® Scalable Processors.

Optimized Model List

A list of popular LLMs are optimized and run efficiently on CPU, including the most notable open-source models like Llama series, Qwen series, and DeepSeek series like DeepSeek-R1 and DeepSeek-V3.1-Terminus.

Model Name	BF16	W8A8_INT8	FP8
DeepSeek-R1	—	DeepSeek-R1-Channel-INT8	DeepSeek-R1
DeepSeek-V3.1-Terminus	—	DeepSeek-V3.1-Terminus-Channel-int8	DeepSeek-V3.1-Terminus
Llama-3.2-3B	Llama-3.2-3B-Instruct	Llama-3.2-3B-quantized.w8a8	—
Llama-3.1-8B	Llama-3.1-8B-Instruct	Llama-3.1-8B-quantized.w8a8	—
QwQ-32B	—	QwQ-32B-quantized.w8a8	—
DeepSeek-Distilled-Llama	—	DeepSeek-R1-Distill-Llama-70B-quantized.w8a8	—
Qwen3-235B	—	—	Qwen3-235B-A22B-FP8

Note: The model identifiers listed in the table above have been verified on 6th Gen Intel® Xeon® P-core platforms.

Installation

Docker (Recommended)

It is recommended to use Docker for setting up the SGLang environment. A Dockerfile is provided to facilitate the installation.

Note: Replace <secret> below with your HuggingFace access token.

Clone, Build and Run

# Clone the SGLang repository
git clone https://github.com/sgl-project/sglang.git
cd sglang/docker

# Build the docker image
docker build -t sglang-cpu:latest -f xeon.Dockerfile .

# Initiate a docker container
docker run \
    -it \
    --privileged \
    --ipc=host \
    --network=host \
    -v /dev/shm:/dev/shm \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    -p 30000:30000 \
    -e "HF_TOKEN=<secret>" \
    sglang-cpu:latest /bin/bash

From Source

If you prefer to install SGLang in a bare metal environment, the setup process is as follows.

Please install the required packages and libraries beforehand if they are not already present on your system. You can refer to the Ubuntu-based installation commands in the Dockerfile for guidance.

1. Install uv and create a virtual environment

   Create Virtual Environment

   # Taking '/opt' as the example uv env folder, feel free to change it as needed
   cd /opt
   curl -LsSf https://astral.sh/uv/install.sh | sh
   source $HOME/.local/bin/env
   uv venv --python 3.12
   source .venv/bin/activate
   

2. Create a config file for torch package indexes Create the uv.toml config file:

   Open Config File

   vim .venv/uv.toml
   

   Press a to enter insert mode in vim, then paste the following content:

   [[index]]
   name = "torch"
   url = "https://download.pytorch.org/whl/cpu"
   
   [[index]]
   name = "torchvision"
   url = "https://download.pytorch.org/whl/cpu"
   
   [[index]]
   name = "torchaudio"
   url = "https://download.pytorch.org/whl/cpu"
   
   [[index]]
   name = "triton"
   url = "https://download.pytorch.org/whl/cpu"
   

   Save the file (press Esc, then type :x and hit Enter), then set it as the default uv config:

   Set Config Path

   export UV_CONFIG_FILE=/opt/.venv/uv.toml
   

3. Clone SGLang and build packages

   Build SGLang

   # Clone the SGLang code
   git clone https://github.com/sgl-project/sglang.git
   cd sglang
   git checkout <YOUR-DESIRED-VERSION>
   
   # Use dedicated toml file
   cd python
   cp pyproject_cpu.toml pyproject.toml
   # Install SGLang dependent libs, and build SGLang main package
   uv pip install --upgrade pip setuptools
   uv pip install .
   
   # Build the CPU backend kernels
   cd ../sgl-kernel
   cp pyproject_cpu.toml pyproject.toml
   uv pip install .
   

4. Set required environment variables

   Set Environment Variables

   export SGLANG_USE_CPU_ENGINE=1
   
   # Set 'LD_LIBRARY_PATH' and 'LD_PRELOAD' to ensure the libs can be loaded by sglang processes
   export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu
   export LD_PRELOAD=${LD_PRELOAD}:/opt/.venv/lib/libiomp5.so:${LD_LIBRARY_PATH}/libtcmalloc.so.4:${LD_LIBRARY_PATH}/libtbbmalloc.so.2
   

   Note: The environment variable SGLANG_USE_CPU_ENGINE=1 is required to enable the SGLang service with the CPU engine.

   Note: If you encounter code compilation issues during the sgl-kernel building process, please check your gcc and g++ versions and upgrade them if they are outdated. It is recommended to use gcc-13 and g++-13 as they have been verified in the official Docker container.

   Note: The system library path is typically located in one of the following directories: ~/.local/lib/, /usr/local/lib/, /usr/local/lib64/, /usr/lib/, /usr/lib64/, and /usr/lib/x86_64-linux-gnu/. In the above example commands, /usr/lib/x86_64-linux-gnu is used. Please adjust the path according to your server configuration.

   It is recommended to add the following to your ~/.bashrc file to avoid setting these variables every time you open a new terminal:

   Persist in ~/.bashrc

   source .venv/bin/activate
   export SGLANG_USE_CPU_ENGINE=1
   export LD_LIBRARY_PATH=<YOUR-SYSTEM-LIBRARY-FOLDER>
   export LD_PRELOAD=<YOUR-LIBS-PATHS>
   

Launch of the Serving Engine

Example command to launch SGLang serving:

Launch Server

python -m sglang.launch_server   \
    --model <MODEL_ID_OR_PATH>   \
    --trust-remote-code          \
    --disable-overlap-schedule   \
    --device cpu                 \
    --host 0.0.0.0               \
    --tp 6

Note: For running W8A8 quantized models, please add the flag --quantization w8a8_int8.

Note: The flag --tp 6 specifies that tensor parallelism will be applied using 6 ranks (TP6). On a CPU platform, a TP rank means a sub-NUMA cluster (SNC). You can get the SNC count using lscpu. If the specified TP rank number differs from the total SNC count, the system will automatically utilize the first n SNCs — but n cannot exceed the total SNC number.

To specify the cores to be used, set the environment variable SGLANG_CPU_OMP_THREADS_BIND. For example, to use the first 40 cores of each SNC on a Xeon® 6980P server (which has 43-43-42 cores on the 3 SNCs of a socket):

Set Thread Binding

export SGLANG_CPU_OMP_THREADS_BIND="0-39|43-82|86-125|128-167|171-210|214-253"

Please beware that with SGLANG_CPU_OMP_THREADS_BIND set, the available memory amounts of the ranks may not be determined in advance. You may need to set --max-total-tokens to avoid out-of-memory errors.

Note: For optimizing decoding with torch.compile, add the flag --enable-torch-compile. To specify the maximum batch size, set --torch-compile-max-bs. For example, --enable-torch-compile --torch-compile-max-bs 4 uses torch.compile with a maximum batch size of 4. The maximum applicable batch size is 16.

Note: A warmup step is automatically triggered when the service is started. The server is ready when you see the log The server is fired up and ready to roll!.

Benchmarking with Requests

You can benchmark the performance via the bench_serving script. Run the command in another terminal. An example command would be:

Run Benchmark

python -m sglang.bench_serving   \
    --dataset-name random        \
    --random-input-len 1024      \
    --random-output-len 1024     \
    --num-prompts 1              \
    --request-rate inf           \
    --random-range-ratio 1.0

Detailed parameter descriptions are available via the command:

Benchmark Help

python -m sglang.bench_serving -h

Additionally, requests can be formatted using the OpenAI Completions API and sent via the command line (e.g., using curl) or through your own scripts.

Example Usage Commands

Large Language Models can range from fewer than 1 billion to several hundred billion parameters. Dense models larger than 20B are expected to run on flagship 6th Gen Intel® Xeon® processors with dual sockets and a total of 6 sub-NUMA clusters. Dense models of approximately 10B parameters or fewer, or MoE (Mixture of Experts) models with fewer than 10B activated parameters, can run on more common 4th generation or newer Intel® Xeon® processors, or utilize a single socket of the flagship 6th Gen Intel® Xeon® processors.

Example: Running DeepSeek-V3.1-Terminus

W8A8_INT8

python -m sglang.launch_server                                 \
    --model IntervitensInc/DeepSeek-V3.1-Terminus-Channel-int8 \
    --trust-remote-code                                        \
    --disable-overlap-schedule                                 \
    --device cpu                                               \
    --quantization w8a8_int8                                   \
    --host 0.0.0.0                                             \
    --enable-torch-compile                                     \
    --torch-compile-max-bs 4                                   \
    --tp 6

Note: Please set --torch-compile-max-bs to the maximum desired batch size for your deployment, which can be up to 16. The value 4 in the examples is illustrative.

Example: Running Llama-3.2-3B

BF16

python -m sglang.launch_server                     \
    --model meta-llama/Llama-3.2-3B-Instruct       \
    --trust-remote-code                            \
    --disable-overlap-schedule                     \
    --device cpu                                   \
    --host 0.0.0.0                                 \
    --enable-torch-compile                         \
    --torch-compile-max-bs 16                      \
    --tp 2

Note: The --torch-compile-max-bs and --tp settings are examples that should be adjusted for your setup. For instance, use --tp 3 to utilize 1 socket with 3 sub-NUMA clusters on an Intel® Xeon® 6980P server.

Once the server has been launched, you can test it using the bench_serving command or create your own commands or scripts following the benchmarking example.