SGLang installation with NPUs support

You can install SGLang using any of the methods below. Please go through System Settings section to ensure the clusters are roaring at max performance. Feel free to leave an issue here at sglang if you encounter any issues or have any problems.

Component Version Mapping For SGLang

Component	Version	Obtain Way
HDK	25.5.2	link
CANN	8.5.0	Obtain Images
Pytorch Adapter	7.3.0	link
MemFabric	1.0.5	`pip install memfabric-hybrid==1.0.5`
Triton	3.2.0	`pip install triton-ascend`
SGLang NPU Kernel	NA	link

Obtain CANN Image

You can obtain the dependency of a specified version of CANN through an image.

Command

# for Atlas 800I A3 and Ubuntu OS
docker pull quay.io/ascend/cann:8.5.0-a3-ubuntu22.04-py3.11
# for Atlas 800I A2 and Ubuntu OS
docker pull quay.io/ascend/cann:8.5.0-910b-ubuntu22.04-py3.11

Preparing the Running Environment

Method 1: Installing from source with prerequisites

Python Version

Only python==3.11 is supported currently. If you don’t want to break system pre-installed python, try installing with conda.

Command

conda create --name sglang_npu python=3.11
conda activate sglang_npu

CANN

Prior to start work with SGLang on Ascend you need to install CANN Toolkit, Kernels operator package and NNAL version 8.5.0, check the installation guide

MemFabric-Hybrid

If you want to use PD disaggregation mode, you need to install MemFabric-Hybrid. MemFabric-Hybrid is a drop-in replacement of Mooncake Transfer Engine that enables KV cache transfer on Ascend NPU clusters.

Command

pip install memfabric-hybrid==1.0.5

Pytorch and Pytorch Framework Adaptor on Ascend

Command

PYTORCH_VERSION=2.8.0
TORCHVISION_VERSION=0.23.0
TORCH_NPU_VERSION=2.8.0.post2
pip install torch==$PYTORCH_VERSION torchvision==$TORCHVISION_VERSION --index-url https://download.pytorch.org/whl/cpu
pip install torch_npu==$TORCH_NPU_VERSION

If you are using other versions of torch and install torch_npu, check installation guide

Triton on Ascend

We provide our own implementation of Triton for Ascend.

Command

pip install triton-ascend

For installation of Triton on Ascend nightly builds or from sources, follow installation guide

SGLang Kernels NPU

We provide SGL kernels for Ascend NPU, check installation guide.

DeepEP-compatible Library

We provide a DeepEP-compatible Library as a drop-in replacement of deepseek-ai’s DeepEP library, check the installation guide.

Some other dependencies

Command

# libGL
apt update
apt install libgl1 libglib2.0-0

# ensure setuptools contains pkg_resources module
pip install "setuptools<80"

Installing SGLang from source

Command

# Use the last release branch
git clone https://github.com/sgl-project/sglang.git
cd sglang
mv python/pyproject_npu.toml python/pyproject.toml
pip install -e python[all_npu]

Method 2: Using Docker Image

Obtain Image

You can download the SGLang image or build an image based on Dockerfile to obtain the Ascend NPU image.

Download SGLang image

dockerhub: docker.io/lmsysorg/sglang:$tag
# Main-based tag, change main to specific version like v0.5.6,
# you can get image for specific version
Atlas 800I A3 : {main}-cann8.5.0-a3
Atlas 800I A2: {main}-cann8.5.0-910b

Build an image based on Dockerfile

Command

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

# Build the docker image
# If there are network errors, please modify the Dockerfile to use offline dependencies or use a proxy
# <arch_tag> is the target architecture of the image, e.g. amd64, arm64
docker build --build-arg TARGETARCH=<arch_tag> -t <image_name> -f npu.Dockerfile .

Create Docker

Notice: --privileged and --network=host are required by RDMA, which is typically needed by Ascend NPU clusters. Notice: The following docker command is based on Atlas 800I A3 machines. If you are using Atlas 800I A2, make sure only davinci[0-7] are mapped into container.

Command

alias drun='docker run -it --rm --privileged --network=host --ipc=host --shm-size=16g \
    --device=/dev/davinci0 --device=/dev/davinci1 --device=/dev/davinci2 --device=/dev/davinci3 \
    --device=/dev/davinci4 --device=/dev/davinci5 --device=/dev/davinci6 --device=/dev/davinci7 \
    --device=/dev/davinci8 --device=/dev/davinci9 --device=/dev/davinci10 --device=/dev/davinci11 \
    --device=/dev/davinci12 --device=/dev/davinci13 --device=/dev/davinci14 --device=/dev/davinci15 \
    --device=/dev/davinci_manager --device=/dev/hisi_hdc \
    --volume /usr/local/sbin:/usr/local/sbin --volume /usr/local/Ascend/driver:/usr/local/Ascend/driver \
    --volume /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \
    --volume /etc/ascend_install.info:/etc/ascend_install.info \
    --volume /var/queue_schedule:/var/queue_schedule --volume ~/.cache/:/root/.cache/'

# Add HF_TOKEN env for download model by SGLang.
drun --env "HF_TOKEN=<secret>" \
    <image_name> \
    python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --attention-backend ascend

System Settings

CPU performance power scheme

The default power scheme on Ascend hardware is ondemand which could affect performance, changing it to performance is recommended.

Command

echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

# Make sure changes are applied successfully
cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor # shows performance

Disable NUMA balancing

Command

sudo sysctl -w kernel.numa_balancing=0
# Check
cat /proc/sys/kernel/numa_balancing # shows 0

Prevent swapping out system memory

Command

sudo sysctl -w vm.swappiness=10

# Check
cat /proc/sys/vm/swappiness # shows 10

Running SGLang Service

Running Service For Large Language Models

PD Mixed Scene

Command

# Enabling CPU Affinity
export SGLANG_SET_CPU_AFFINITY=1
python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --attention-backend ascend

PD Disaggregation Scene

Launch Prefill Server

Command

# Enabling CPU Affinity
export SGLANG_SET_CPU_AFFINITY=1

# PIP: recommended to config first Prefill Server IP
# PORT: one free port
# all sglang servers need to be config the same PIP and PORT,
export ASCEND_MF_STORE_URL="tcp://PIP:PORT"
# if you are Atlas 800I A2 hardware and use rdma for kv cache transfer, add this parameter
export ASCEND_MF_TRANSFER_PROTOCOL="device_rdma"
python3 -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --disaggregation-mode prefill \
    --disaggregation-transfer-backend ascend \
    --disaggregation-bootstrap-port 8995 \
    --attention-backend ascend \
    --device npu \
    --base-gpu-id 0 \
    --tp-size 1 \
    --host 127.0.0.1 \
    --port 8000

Launch Decode Server

Command

# PIP: recommended to config first Prefill Server IP
# PORT: one free port
# all sglang servers need to be config the same PIP and PORT,
export ASCEND_MF_STORE_URL="tcp://PIP:PORT"
# if you are Atlas 800I A2 hardware and use rdma for kv cache transfer, add this parameter
export ASCEND_MF_TRANSFER_PROTOCOL="device_rdma"
python3 -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --disaggregation-mode decode \
    --disaggregation-transfer-backend ascend \
    --attention-backend ascend \
    --device npu \
    --base-gpu-id 1 \
    --tp-size 1 \
    --host 127.0.0.1 \
    --port 8001

Launch Router

Command

python3 -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://127.0.0.1:8000 8995 \
    --decode http://127.0.0.1:8001 \
    --host 127.0.0.1 \
    --port 6688

Running Service For Multimodal Language Models

PD Mixed Scene

Command

python3 -m sglang.launch_server \
    --model-path Qwen3-VL-30B-A3B-Instruct \
    --host 127.0.0.1 \
    --port 8000 \
    --tp 4 \
    --device npu \
    --attention-backend ascend \
    --mm-attention-backend ascend_attn \
    --disable-radix-cache \
    --trust-remote-code \
    --enable-multimodal \
    --sampling-backend ascend

Hardware Platforms

Documentation Index

​Component Version Mapping For SGLang

​Obtain CANN Image

​Preparing the Running Environment

​Method 1: Installing from source with prerequisites

​Python Version

​CANN

​MemFabric-Hybrid

​Pytorch and Pytorch Framework Adaptor on Ascend

​Triton on Ascend

​SGLang Kernels NPU

​DeepEP-compatible Library

​Some other dependencies

​Installing SGLang from source

​Method 2: Using Docker Image

​Obtain Image

​Create Docker

​System Settings

​CPU performance power scheme

​Disable NUMA balancing

​Prevent swapping out system memory

​Running SGLang Service

​Running Service For Large Language Models

​PD Mixed Scene

​PD Disaggregation Scene

​Running Service For Multimodal Language Models

​PD Mixed Scene

Component Version Mapping For SGLang

Obtain CANN Image

Preparing the Running Environment

Method 1: Installing from source with prerequisites

Python Version

CANN

MemFabric-Hybrid

Pytorch and Pytorch Framework Adaptor on Ascend

Triton on Ascend

SGLang Kernels NPU

DeepEP-compatible Library

Some other dependencies

Installing SGLang from source

Method 2: Using Docker Image

Obtain Image

Create Docker

System Settings

CPU performance power scheme

Disable NUMA balancing

Prevent swapping out system memory

Running SGLang Service

Running Service For Large Language Models

PD Mixed Scene

PD Disaggregation Scene

Running Service For Multimodal Language Models

PD Mixed Scene