> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# MiniMax M2.5/M2.1/M2 Usage

[MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5), [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1), and [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) are advanced large language models created by [MiniMax](https://www.minimax.io/).

The MiniMax-M2 series redefines efficiency for agents. These compact, fast, and cost-effective MoE models (230 billion total parameters with 10 billion active parameters) are built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, the MiniMax-M2 series provides sophisticated, end-to-end tool use performance expected from today's leading models, but in a streamlined form factor that makes deployment and scaling easier than ever.

## Supported Models

This guide applies to the following models. You only need to update the model name during deployment. The following examples use **MiniMax-M2**:

* [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
* [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
* [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)

## System Requirements

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

4-GPU deployment command:

```bash Command theme={null}
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 Command theme={null}
python -m sglang.launch_server \
    --model-path MiniMaxAI/MiniMax-M2 \
    --tp-size 8 \
    --ep-size 8 \
    --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
```

### AMD GPUs (MI300X/MI325X/MI355X)

8-GPU deployment command:

```bash Command theme={null}
SGLANG_USE_AITER=1 python -m sglang.launch_server \
    --model-path MiniMaxAI/MiniMax-M2.5 \
    --tp-size 8 \
    --ep-size 8 \
    --attention-backend aiter \
    --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
```

## Testing Deployment

After startup, you can test the SGLang OpenAI-compatible API with the following command:

```bash Command theme={null}
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?"}]}
        ]
    }'
```