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[Docs] Sync docs_new with legacy docs and update migration redirects (#23337)

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Co-authored-by: Mingyi <wisclmy0611@gmail.com>
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---
title: "MiniMax M2.1/M2 Usage"
title: "MiniMax M2.5/M2.1/M2 Usage"
metatags:
description: "Deploy MiniMax M2.1/M2 with SGLang: 230B MoE model (10B active), up to 3M context, optimized for coding and agentic tasks, tool use support."
description: "Deploy MiniMax M2.5/M2.1/M2 with SGLang: 230B MoE model (10B active), up to 3M context, optimized for coding and agentic tasks, tool use support."
---
[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/).
[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/).
MiniMax-M2 series redefines efficiency for agents. It's a compact, fast, and cost-effective MoE model (230 billion total parameters with 10 billion active parameters) built for elite performance in coding and agentic tasks, all while maintaining powerful general intelligence. With just 10 billion activated parameters, MiniMax-M2 provides the 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.
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)
@@ -52,6 +53,24 @@ python -m sglang.launch_server \
--mem-fraction-static 0.85
```
### AMD GPUs (MI300X/MI325X/MI355X)
8-GPU deployment command:
```bash Command
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: