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

# Deploy MiniMax-M2.7

> Deploy MiniMax-M2.7 with the SGLang Cookbook and verify inference, reasoning, and tool calls through an OpenAI-compatible endpoint.

MiniMax-M2.7 is a language model for agents, software engineering, and complex productivity tasks. For a new project that needs native multimodal input or a 1M-token context, use [MiniMax-M3](/docs/guides/local-deploy-m3). Continue with this page for an existing M2.7 workflow.

## Open release and license

| Resource         | Description                                                                             |
| :--------------- | :-------------------------------------------------------------------------------------- |
| Model repository | [`MiniMaxAI/MiniMax-M2.7`](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)               |
| License          | [MiniMax-M2.7 License](https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE) |
| SGLang Cookbook  | [MiniMax-M2.7](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M2.7)     |

The MiniMax-M2.7 License permits the personal, research, and educational uses listed in the license. Commercial use includes attribution and prior-authorization requirements. Read the complete license before production or commercial use.

## Hardware and images

The current SGLang Cookbook lists these reference combinations:

| Platform | Reference hardware        | SGLang image                                  |
| :------- | :------------------------ | :-------------------------------------------- |
| NVIDIA   | A100 / H100 / H200 / B200 | `lmsysorg/sglang:v0.5.10.post1`               |
| NVIDIA   | B300 / GB300              | `lmsysorg/sglang:v0.5.10.post1-cu130`         |
| AMD      | MI300X / MI325X           | `lmsysorg/sglang:v0.5.10.post1-rocm720-mi30x` |
| AMD      | MI355X                    | `lmsysorg/sglang:v0.5.10.post1-rocm720-mi35x` |

Common NVIDIA reference topologies use four high-memory GPUs with TP 4 or eight GPUs with TP 8 and EP 8. Other hardware supports different two-, four-, or eight-GPU combinations. Use the [SGLang configurator](https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M2.7) to obtain the matching command.

## Reference deployment: four high-memory NVIDIA GPUs

### Start the server

```bash theme={null}
docker run --gpus all \
  --shm-size 32g \
  -p 30000:30000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --ipc=host \
  lmsysorg/sglang:v0.5.10.post1 \
  sglang serve \
  --model-path MiniMaxAI/MiniMax-M2.7 \
  --tp 4 \
  --tool-call-parser minimax-m2 \
  --reasoning-parser minimax-append-think \
  --trust-remote-code \
  --mem-fraction-static 0.85 \
  --host 0.0.0.0 \
  --port 30000
```

On the first start, SGLang downloads the model from Hugging Face. When authentication is required, add this argument to `docker run`:

```bash theme={null}
--env "HF_TOKEN=<your-hf-token>"
```

### Verify the deployment

```bash theme={null}
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MiniMaxAI/MiniMax-M2.7",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Write a Python function that tests whether a number is prime."}
    ],
    "max_tokens": 2048
  }'
```

You can also use the OpenAI SDK:

```python theme={null}
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="MiniMaxAI/MiniMax-M2.7",
    messages=[{"role": "user", "content": "Explain mixture-of-experts models."}],
    max_tokens=2048,
)

print(response.choices[0].message.content)
```

## Key launch parameters

| Parameter               | Recommended value       | Purpose                                                                   |
| :---------------------- | :---------------------- | :------------------------------------------------------------------------ |
| `--tool-call-parser`    | `minimax-m2`            | Converts native tool calls to an OpenAI-compatible format                 |
| `--reasoning-parser`    | `minimax-append-think`  | Processes model reasoning content                                         |
| `--trust-remote-code`   | Enabled                 | Loads the implementation from the model repository                        |
| `--mem-fraction-static` | `0.85`                  | Allocates the static GPU memory fraction for model execution and KV cache |
| `--tp`                  | `2`, `4`, or `8`        | Sets tensor parallelism for the hardware                                  |
| `--ep`                  | Set by the configurator | Expert parallelism is available for eight-GPU NVIDIA or AMD deployments   |

<Warning>
  Parallelism, KV cache capacity, and available context length are interdependent. Do not change only the GPU count. Select a complete configuration from the SGLang configurator whenever the hardware changes.
</Warning>

## Reasoning and tool calls

MiniMax-M2.7 emits reasoning content. With `minimax-append-think`, read or parse the trace according to the response format of your SGLang version. With `minimax-m2`, functions can be supplied through the OpenAI-compatible `tools` parameter.

Before production integration, validate:

* Non-streaming and streaming text generation
* Your policy for displaying or hiding reasoning content
* Single, parallel, and nested tool arguments
* GPU memory headroom at the required context length

## Community quantizations and Mac deployment

Community-converted MLX and GGUF versions are available on Hugging Face, but they are maintained by their respective publishers and are outside the MiniMax + SGLang reference deployment on this page. When using a community build, separately verify the conversion method, quantization error, license, and runtime compatibility.

## Resources

<CardGroup cols={2}>
  <Card title="SGLang MiniMax-M2.7 Cookbook" icon="book-open" href="https://docs.sglang.io/cookbook/autoregressive/MiniMax/MiniMax-M2.7">
    Open the complete hardware combinations, configuration parameters, and benchmarks.
  </Card>

  <Card title="MiniMax-M2.7 model repository" icon="file-text" href="https://huggingface.co/MiniMaxAI/MiniMax-M2.7">
    Open the model card, weights, and license.
  </Card>
</CardGroup>