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	25.2.1	link
CANN	8.3.rc2	Obtain Images
Pytorch Adapter	7.3.0	link
MemFabric	0.1.0	link
Triton	3.2.0.dev2025112116	link
Bisheng	20251121	link
SGLang NPU Kernel	NA	link

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:8.3.rc2-a3-ubuntu22.04-py3.11
# for Atlas 800I A2 and Ubuntu OS
docker pull quay.io/ascend/cann:8.3.rc2-910b-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.

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 Adaptor#

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

pip install mf-adapter==1.0.0

Pytorch and Pytorch Framework Adaptor on Ascend#

At the moment NPUGraph optimizations are supported only in torch_npu==2.6.0.post3 that requires ‘torch==2.6.0’.

TODO: NPUGraph optimizations will be supported in future releases of ‘torch_npu’ 2.7.1, 2.8.0 and 2.9.0

PYTORCH_VERSION=2.6.0
TORCHVISION_VERSION=0.21.0
TORCH_NPU_VERSION=2.6.0.post3
pip install torch==$PYTORCH_VERSION torchvision==$TORCHVISION_VERSION --index-url https://download.pytorch.org/whl/cpu
pip install torch_npu==$TORCH_NPU_VERSION

While there is no released versions of ‘torch_npu’ for ‘torch==2.7.1’ and ‘torch==2.8.0’ we provide custom builds of ‘torch_npu’. PLATFORM can be ‘aarch64’ or ‘x86_64’

PLATFORM="aarch64"
PYTORCH_VERSION=2.8.0
TORCHVISION_VERSION=0.23.0
pip install torch==$PYTORCH_VERSION torchvision==$TORCHVISION_VERSION --index-url https://download.pytorch.org/whl/cpu
wget https://sglang-ascend.obs.cn-east-3.myhuaweicloud.com/sglang/torch_npu/torch_npu-${PYTORCH_VERSION}.post2.dev20251120-cp311-cp311-manylinux_2_28_${PLATFORM}.whl
pip install torch_npu-${PYTORCH_VERSION}.post2.dev20251120-cp311-cp311-manylinux_2_28_${PLATFORM}.whl

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==3.2.0rc4

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.

CustomOps#

TODO: to be removed once merged into sgl-kernel-npu. Additional package with custom operations. DEVICE_TYPE can be “a3” for Atlas A3 server or “910b” for Atlas A2 server.

DEVICE_TYPE="a3"
wget https://sglang-ascend.obs.cn-east-3.myhuaweicloud.com/ops/CANN-custom_ops-8.2.0.0-$DEVICE_TYPE-linux.aarch64.run
chmod a+x ./CANN-custom_ops-8.2.0.0-$DEVICE_TYPE-linux.aarch64.run
./CANN-custom_ops-8.2.0.0-$DEVICE_TYPE-linux.aarch64.run --quiet --install-path=/usr/local/Ascend/ascend-toolkit/latest/opp
wget https://sglang-ascend.obs.cn-east-3.myhuaweicloud.com/ops/custom_ops-1.0.$DEVICE_TYPE-cp311-cp311-linux_aarch64.whl
pip install ./custom_ops-1.0.$DEVICE_TYPE-cp311-cp311-linux_aarch64.whl

Installing SGLang from source#

# Use the last release branch
git clone -b v0.5.6.post2 https://github.com/sgl-project/sglang.git
cd sglang
mv python/pyproject_other.toml python/pyproject.toml
pip install -e python[srt_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}-cann8.3.rc2-a3
Atlas 800I A2: {main}-cann8.3.rc2-910b

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
docker build -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.

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#

Running 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#

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 \

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

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

Running Service 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

SGLang installation with NPUs support

Contents

SGLang installation with NPUs support#

Component Version Mapping For SGLang#

Obtain CANN Image#

Preparing the Running Environment#

Method 1: Installing from source with prerequisites#

Python Version#

CANN#

MemFabric Adaptor#

Pytorch and Pytorch Framework Adaptor on Ascend#

Triton on Ascend#

SGLang Kernels NPU#

DeepEP-compatible Library#

CustomOps#

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 Separation Scene#

Running Service For Multimodal Language Models#

PD Mixed Scene#