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	25.3.RC1
CANN	8.5.0	Obtain Images
Pytorch Adapter	7.3.0
MemFabric	1.0.5	pip install memfabric-hybrid==1.0.5
Triton	3.2.0	pip install triton-ascend
Bisheng	20251121
SGLang NPU Kernel	NA

Obtain CANN Image

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

# 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

Source

Python Version

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

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 8.3.RC2 or higher, 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.

pip install memfabric-hybrid==1.0.5

Pytorch and Pytorch Framework Adaptor on Ascend

PYTORCH_VERSION=2.8.0
TORCHVISION_VERSION=0.23.0
TORCH_NPU_VERSION=2.8.0
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.

BISHENG_NAME="Ascend-BiSheng-toolkit_aarch64_20251121.run"
BISHENG_URL="https://sglang-ascend.obs.cn-east-3.myhuaweicloud.com/sglang/triton_ascend/${BISHENG_NAME}"
wget -O "${BISHENG_NAME}" "${BISHENG_URL}" && chmod a+x "${BISHENG_NAME}" && "./${BISHENG_NAME}" --install && rm "${BISHENG_NAME}"

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.

Installing SGLang from source

# 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]

Docker

Obtain Image

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

1. 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

2. Build an image based on Dockerfile

# 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
docker build -t <image_name> -f npu.Dockerfile .

Create Docker

--privileged and --network=host are required by RDMA, which is typically needed by Ascend NPU clusters.

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.

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.

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

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

Prevent swapping out system memory

sudo sysctl -w vm.swappiness=10

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

Running SGLang Service

For Large Language Models

PD Mixed Scene

# 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 Separation Scene

1. Launch Prefill Server

# 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

2. Launch Decode Server

# 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

3. Launch Router

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

For Multimodal Language Models

PD Mixed Scene

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