---
title: SGLang installation with NPUs support
description: "Complete installation guide for SGLang on Ascend NPUs, including component version mapping, environment setup, and launching inference services."
---
You can install SGLang using any of the methods below. Please go through `System Settings` section to ensure the clusters are operating at optimal performance. Feel free to leave an issue [here at sglang](https://github.com/sgl-project/sglang/issues) if you encounter any issues or have any problems.

## Component Version Mapping For SGLang

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "34%"}} />
    <col style={{width: "33%"}} />
    <col style={{width: "33%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Component</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Version</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Obtain Way</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>HDK</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>25.5.2</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><a href="https://www.hiascend.com/hardware/firmware-drivers/community?product=1&model=30&cann=9.0.0&driver=Ascend+HDK+25.5.2">link</a></td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>CANN</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>9.0.0</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><a href="#obtain-cann-image">Obtain Images</a></td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TorchNPU</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>26.0.0</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><a href="https://gitcode.com/Ascend/pytorch/releases">link</a></td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MemFabric</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1.0.8</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`pip install memfabric-hybrid==1.0.8`</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Triton</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>3.2.1.dev20260530</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`pip install triton-ascend==3.2.1.dev20260530 \`<br/>`--extra-index-url=https://mirrors.huaweicloud.com/ascend/repos/pypi/nightly \`<br/>`--trusted-host mirrors.huaweicloud.com`</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SGLang NPU Kernel</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>2026.05.01.post3</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><a href="https://github.com/sgl-project/sgl-kernel-npu/releases/tag/2026.05.01.post3">link</a></td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>MemFabric-zbal</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1.1.1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`pip install memfabric-zbal==1.1.1`</td>
    </tr>
  </tbody>
</table>

### Obtain CANN Image

<Warning>
Ensure sufficient disk space before pulling images. Each Docker image requires at least **30GB** of free space.
</Warning>

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

<Tabs>
  <Tab title="Atlas 800I A3">

```bash Command
docker pull quay.io/ascend/cann:9.0.0-a3-ubuntu22.04-py3.11
```

  </Tab>
  <Tab title="Atlas 800I A2">

```bash Command
docker pull quay.io/ascend/cann:9.0.0-910b-ubuntu22.04-py3.11
```

  </Tab>
</Tabs>

## 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](https://github.com/conda/conda).

```bash Command
conda create --name sglang_npu python=3.11
conda activate sglang_npu
```

Note on Anaconda repository restrictions
If you encounter an error like “Terms of Service have not been accepted” during the conda create step, the default Anaconda repository is blocking package downloads. To resolve this, configure a mirror (e.g., Tsinghua Open Source Mirror):

```bash Command
# Add Tsinghua mirrors
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
conda config --set show_channel_urls yes
conda config --remove channels defaults
```

Edit the system-level conda config to remove any hardcoded defaults, e.g., vi ~/miniconda3/.condarc
Then remove the failed environment and recreate it:

```bash Command
conda clean -i
conda env remove -n sglang_npu
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 9.0.0, check the [installation guide](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/900/softwareinst/instg/instg_0008.html?OS=openEuler&InstallType=local)

#### 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.

```bash Command
pip install memfabric-hybrid==1.0.8
```

#### MemFabric-zbal

MemFabric-zbal is a Zero Buffer Acceleration Library of high-performance operators for LLM inference and training on Ascend,
accelerating computation by eliminating intermediate memory buffers; it is required only on **aarch64** clusters and is
installed in addition to MemFabric-Hybrid.

```bash Command
# Only needed on aarch64 (arm64) hosts
pip install memfabric-zbal==1.1.1
```

#### PyTorch and PyTorch Framework Adaptor on Ascend

```bash Command
PYTORCH_VERSION=2.10.0
TORCHVISION_VERSION=0.25.0
TORCH_NPU_VERSION=2.10.0
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](https://github.com/Ascend/pytorch/blob/master/README.md)

#### Triton on Ascend

We provide our own implementation of Triton for Ascend.

```bash Command
pip install triton-ascend==3.2.1.dev20260530 \
  --extra-index-url=https://mirrors.huaweicloud.com/ascend/repos/pypi/nightly \
  --trusted-host mirrors.huaweicloud.com
```

For installation of Triton on Ascend nightly builds or from sources, follow [installation guide](https://gitcode.com/Ascend/triton-ascend/blob/master/docs/sources/getting-started/installation.md)

#### SGLang Kernels NPU

We provide SGL kernels for Ascend NPU, check [installation guide](https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/sgl_kernel_npu/README.md).

#### DeepEP-compatible Library

We provide a DeepEP-compatible Library as a drop-in replacement of deepseek-ai's DeepEP library, check the [installation guide](https://github.com/sgl-project/sgl-kernel-npu/blob/main/python/deep_ep/README.md).

#### Some other dependencies

```bash Command
# libGL
apt update
apt install libgl1 libglib2.0-0

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

#### Installing SGLang from source

```bash 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.

<Warning>
Ensure sufficient disk space before pulling images. Each Docker image requires at least **30GB** of free space. If you need to download model weights, check the model size at [ModelScope](https://www.modelscope.cn/models) to reserve enough space.
</Warning>

1. Download SGLang image

<Tip>
We publish both **stable releases** and **daily builds**. Choose a stable release tag (e.g., `v0.5.13.post1-cann9.0.0-a3`) if you prefer a validated version, or a daily build tag (e.g., `main-cann9.0.0-a3`) if you need the latest development changes.
</Tip>

<Tabs>
  <Tab title="Atlas 800I A3">

```bash Command
# Stable release
docker pull quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-a3

# Daily build
docker pull quay.io/ascend/sglang:main-cann9.0.0-a3
```

  </Tab>
  <Tab title="Atlas 800I A2">

```bash Command
# Stable release
docker pull quay.io/ascend/sglang:v0.5.13.post1-cann9.0.0-910b

# Daily build
docker pull quay.io/ascend/sglang:main-cann9.0.0-910b
```

  </Tab>
</Tabs>

2. Build an image based on Dockerfile

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

# Build the docker image
# Replace <arch_tag> with the target architecture, e.g., amd64, arm64.
# Optional build arguments:
#   --build-arg DEVICE_TYPE=910b          # Required for Atlas 800I A2
#   --build-arg APTMIRROR=<mirror_url>    # Use a custom APT mirror to improve download speed
# If there are network errors, please modify the Dockerfile to add ARG HTTP_PROXY/HTTPS_PROXY and set them as ENV.
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.

<Tabs>
  <Tab title="Atlas 800I A3">

```bash Command
# Create a shortcut 'drun' to launch a privileged Docker 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.
# The container runs with the '--rm' flag, so it will be automatically removed after the command finishes (including Ctrl+C)
drun --env "HF_TOKEN=<secret>" \
    <image_name> \
    python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --attention-backend ascend
```

  </Tab>
  <Tab title="Atlas 800I A2">

```bash Command
# Create a shortcut 'drun' to launch a privileged Docker 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/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.
# The container runs with the '--rm' flag, so it will be automatically removed after the command finishes (including Ctrl+C)
drun --env "HF_TOKEN=<secret>" \
    <image_name> \
    python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --attention-backend ascend
```

  </Tab>
</Tabs>

<Tip>
SGLang will serve on `http://127.0.0.1:30000` by default. You can change the host and port by `--host` and `--port` parameters.
</Tip>

## 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.

```bash 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

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

### Prevent swapping out system memory

```bash 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

```bash 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 \
  --host 127.0.0.1 \
  --port 30000
```

#### PD Disaggregation Scene

1. Launch Prefill Server

<Tabs>
  <Tab title="Atlas 800I A3">

```bash Command
# Enabling CPU Affinity
export SGLANG_SET_CPU_AFFINITY=1

# PREFILL_IP: IP address of the first Prefill Server
# FREE_PORT: any available port
# all SGLang servers need to be configured with the same PREFILL_IP and FREE_PORT
export ASCEND_MF_STORE_URL="tcp://PREFILL_IP:FREE_PORT"
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 30001
```

  </Tab>
  <Tab title="Atlas 800I A2">

```bash Command
# Enabling CPU Affinity
export SGLANG_SET_CPU_AFFINITY=1

# PREFILL_IP: IP address of the first Prefill Server
# FREE_PORT: any available port
# all SGLang servers need to be configured with the same PREFILL_IP and FREE_PORT
export ASCEND_MF_STORE_URL="tcp://PREFILL_IP:FREE_PORT"
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 30001
```

  </Tab>
</Tabs>

2. Launch Decode Server

<Tabs>
  <Tab title="Atlas 800I A3">

```bash Command
# PREFILL_IP: IP address of the first Prefill Server
# FREE_PORT: any available port
# all SGLang servers need to be configured with the same PREFILL_IP and FREE_PORT
export ASCEND_MF_STORE_URL="tcp://PREFILL_IP:FREE_PORT"
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 30002
```

  </Tab>
  <Tab title="Atlas 800I A2">

```bash Command
# PREFILL_IP: IP address of the first Prefill Server
# FREE_PORT: any available port
# all SGLang servers need to be configured with the same PREFILL_IP and FREE_PORT
export ASCEND_MF_STORE_URL="tcp://PREFILL_IP:FREE_PORT"
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 30002
```

  </Tab>
</Tabs>

3. Launch Router

```bash Command
python3 -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://127.0.0.1:30001 8995 \
    --decode http://127.0.0.1:30002 \
    --host 127.0.0.1 \
    --port 30000
```

<Note>
The `8995` in command script is the disaggregation bootstrap port. It must match the
`--disaggregation-bootstrap-port` value set on the prefill server in step 1.
</Note>

### Running Service For Multimodal Language Models

#### PD Mixed Scene

```bash Command
python3 -m sglang.launch_server \
    --model-path Qwen/Qwen3-VL-30B-A3B-Instruct \
    --host 127.0.0.1 \
    --port 30000 \
    --tp 4 \
    --device npu \
    --attention-backend ascend \
    --mm-attention-backend ascend_attn \
    --disable-radix-cache \
    --trust-remote-code \
    --enable-multimodal \
    --sampling-backend ascend
```

## Testing the Service

Once the server prints `The server is fired up and ready to roll!` in the logs, it is ready to accept requests.

### Which port to send requests to

The port you use depends on your deployment mode:

| Scenario | Where to send requests |
|---|---|
| Non-PD (single server) | The server's `--port` (e.g., `30000` in the examples above) |
| Non-PD (multi-node) | The primary node's (`--node-rank 0`) `--port`; do **not** send requests to worker nodes |
| PD disaggregation | The router's `--port` (e.g., `30000` in the examples above); do **not** send requests directly to prefill or decode servers |

<Tip>
SGLang defaults to port `30000` when `--port` is not specified. The examples in this guide use explicit ports for clarity.
</Tip>

### Health Check

```bash Command
curl http://127.0.0.1:30000/health
```

A successful response returns HTTP 200 with an empty body.

### Generate (Native Endpoint)

```bash Command
curl http://127.0.0.1:30000/generate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "What is the capital of France?",
    "sampling_params": {"temperature": 0, "max_new_tokens": 128}
  }'
```

The expected output should contain "Paris".

### Chat Completions (OpenAI-Compatible)

```bash Command
curl http://127.0.0.1:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Llama-3.1-8B-Instruct",
    "messages": [{"role": "user", "content": "What is the capital of France?"}]
  }'
```

Some models return responses accompanied with thinking process content. To disable this output, configure parameters as follows:

```bash Command
curl http://127.0.0.1:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Eco-Tech/Qwen3.5-27B-w8a8-mtp",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "chat_template_kwargs": {"enable_thinking": false}
  }'
```

The expected output should contain "Paris".

### Multimodal Chat Completions

<Note>
The image URL in the example below references an external resource (`raw.githubusercontent.com`). Make sure the server has internet access so the image can be downloaded at inference time. Alternatively, you can use a locally accessible URL or base64-encoded image data.
</Note>

```bash Command
curl http://127.0.0.1:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen3-VL-30B-A3B-Instruct",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/examples/assets/example_image.png"}},
        {"type": "text", "text": "Describe this image."}
      ]
    }]
  }'
```
