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# MiniMax M2 Model SGLang Deployment Guide
[English Version](./sglang_deploy_guide.md) | [Chinese Version](./sglang_deploy_guide_cn.md)
We recommend using [SGLang](https://github.com/sgl-project/sglang) to deploy the [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) model. SGLang is a high-performance inference engine with excellent serving throughput, efficient and intelligent memory management, powerful batch request processing capabilities, and deeply optimized underlying performance. We recommend reviewing SGLang's official documentation to check hardware compatibility before deployment.
## Applicable Models
This document applies to the following models. You only need to change the model name during deployment.
- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
The deployment process is illustrated below using MiniMax-M2 as an example.
## System Requirements
- OS: Linux
- Python: 3.9 - 3.12
- GPU:
- compute capability 7.0 or higher
- Memory requirements: 220 GB for weights, 240 GB per 1M context tokens
The following are recommended configurations; actual requirements should be adjusted based on your use case:
- 4x 96GB GPUs: Supported context length of up to 400K tokens.
- 8x 144GB GPUs: Supported context length of up to 3M tokens.
## Deployment with Python
It is recommended to use a virtual environment (such as **venv**, **conda**, or **uv**) to avoid dependency conflicts.
We recommend installing SGLang in a fresh Python environment:
```bash
git clone -b v0.5.4.post1 https://github.com/sgl-project/sglang.git
cd sglang
# Install the python packages
pip install --upgrade pip
pip install -e "python"
```
Run the following command to start the SGLang server. SGLang will automatically download and cache the MiniMax-M2 model from Hugging Face.
4-GPU deployment command:
```bash
python -m sglang.launch_server \
--model-path MiniMaxAI/MiniMax-M2 \
--tp-size 4 \
--tool-call-parser minimax-m2 \
--reasoning-parser minimax-append-think \
--host 0.0.0.0 \
--trust-remote-code \
--port 8000 \
--mem-fraction-static 0.85
```
8-GPU deployment command:
```bash
python -m sglang.launch_server \
--model-path MiniMaxAI/MiniMax-M2 \
--tp-size 8 \
--ep-size 8 \
--tool-call-parser minimax-m2 \
--trust-remote-code \
--host 0.0.0.0 \
--reasoning-parser minimax-append-think \
--port 8000 \
--mem-fraction-static 0.85
```
## Testing Deployment
After startup, you can test the SGLang OpenAI-compatible API with the following command:
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "MiniMaxAI/MiniMax-M2",
"messages": [
{"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
{"role": "user", "content": [{"type": "text", "text": "Who won the world series in 2020?"}]}
]
}'
```
## Common Issues
### Hugging Face Network Issues
If you encounter network issues, you can set up a proxy before pulling the model.
```bash
export HF_ENDPOINT=https://hf-mirror.com
```
### MiniMax-M2 model is not currently supported
Please upgrade to the latest stable version, >= v0.5.4.post3.
## Getting Support
If you encounter any issues while deploying the MiniMax model:
- Contact our technical support team through official channels such as email at [model@minimax.io](mailto:model@minimax.io)
- Submit an issue on our [GitHub](https://github.com/MiniMax-AI) repository
We continuously optimize the deployment experience for our models. Feedback is welcome!
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