---
title: "MindSpore Models"
---
## Introduction

MindSpore is a high-performance AI framework optimized for Ascend NPUs. This doc guides users to run MindSpore models in SGLang.

## Requirements

MindSpore currently only supports Ascend NPU devices. Users need to first install Ascend CANN 8.5.
The CANN software packages can be downloaded from the [Ascend Official Website](https://www.hiascend.com).

## Supported Models

Currently, the following models are supported:

- **Qwen3**: Dense and MoE models
- **DeepSeek V3/R1**
- *More models coming soon...*

## Installation

> **Note**: Currently, MindSpore models are provided by an independent package `sgl-mindspore`. Support for MindSpore is built upon current SGLang support for Ascend NPU platform. Please first [install SGLang for Ascend NPU](../hardware-platforms/ascend-npus/ascend_npu) and then install `sgl-mindspore`:

```bash Install
git clone https://github.com/mindspore-lab/sgl-mindspore.git
cd sgl-mindspore
pip install -e .
```


## Run Model

Current SGLang-MindSpore supports Qwen3 and DeepSeek V3/R1 models. This doc uses Qwen3-8B as an example.

### Offline inference

Use the following script for offline inference:

```python Offline Infer
import sglang as sgl

# Initialize the engine with MindSpore backend
llm = sgl.Engine(
    model_path="/path/to/your/model",  # Local model path
    device="npu",                      # Use NPU device
    model_impl="mindspore",            # MindSpore implementation
    attention_backend="ascend",        # Attention backend
    tp_size=1,                         # Tensor parallelism size
    dp_size=1                          # Data parallelism size
)

# Generate text
prompts = [
    "Hello, my name is",
    "The capital of France is",
    "The future of AI is"
]

sampling_params = {"temperature": 0, "top_p": 0.9}
outputs = llm.generate(prompts, sampling_params)

for prompt, output in zip(prompts, outputs):
    print(f"Prompt: {prompt}")
    print(f"Generated: {output['text']}")
    print("---")
```

### Start server

Launch a server with MindSpore backend:

```bash Command
# Basic server startup
python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --tp-size 1 \
    --dp-size 1
```

For distributed server with multiple nodes:

```bash Command
# Multi-node distributed server
python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --dist-init-addr 127.0.0.1:29500 \
    --nnodes 2 \
    --node-rank 0 \
    --tp-size 4 \
    --dp-size 2
```

## Troubleshooting

#### Debug Mode

Enable sglang debug logging by log-level argument.

```bash Debug Mode
python3 -m sglang.launch_server \
    --model-path /path/to/your/model \
    --host 0.0.0.0 \
    --device npu \
    --model-impl mindspore \
    --attention-backend ascend \
    --log-level DEBUG
```

Enable mindspore info and debug logging by setting environments.

```bash Command
export GLOG_v=1  # INFO
export GLOG_v=0  # DEBUG
```

#### Explicitly select devices

Use the following environment variable to explicitly select the devices to use.

```bash Command
export ASCEND_RT_VISIBLE_DEVICES=4,5,6,7  # to set device
```

#### Some communication environment issues

In case of some environment with special communication environment, users need set some environment variables.

```bash Disable LCCL
export MS_ENABLE_LCCL=off # current not support LCCL communication mode in SGLang-MindSpore
```

#### Some dependencies of protobuf

In case of some environment with special protobuf version, users need set some environment variables to avoid binary version mismatch.

```bash Command
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python  # to avoid protobuf binary version mismatch
```

## Support
For MindSpore-specific issues:

- Refer to the [MindSpore documentation](https://www.mindspore.cn/)
