MiniMax-01
Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention
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Decision brief
MiniMax-01 optimizes Linear Attention for large-language and vision-language models.
Good fit when
- When high throughput performance is required for model serving
- For efficient memory management and batch request handling
Avoid when
- If deep customization of attention mechanisms aside from Linear Attention is needed
- In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Not a fork · Organization account
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- No lockfile
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- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install MiniMax-01How it fits your stack(7)
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Overview
Offers deployment guides for production use via vLLM or Transformers for miniaturized large language and vision-language models.
Capability facts
- Languages
- python
Source: github.language · Aug 18, 2026
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README
5. Deployment Guide
For production deployment, we recommend using vLLM to serve MiniMax-Text-01 and MiniMax-VL-01. vLLM provides excellent performance for serving large language models with the following features:
- 🔥 Outstanding service throughput performance
- ⚡ Efficient and intelligent memory management
- 📦 Powerful batch request processing capability
- ⚙️ Deeply optimized underlying performance
For detailed vLLM deployment instructions, please refer to our vLLM Deployment Guide.
Alternatively, you can also deploy using Transformers directly. For detailed Transformers deployment instructions, you can see our MiniMax-Text-01 Transformers Deployment Guide.
For agents
This page has a .md twin and JSON over the API.