MiniMaxAI · minimax_m2
MiniMax-M2.7
MiniMax-M2.7 by MiniMaxAI: a 228.7B-parameter mixture-of-experts open-weight model under the other license.
1,461,696 downloads · 1,247 likes · Model card · synced 2026-09-19
Specifications
- Parameters
- 228.7B (7.1B active)
- Architecture
- moe · minimax_m2
- Layers
- 62
- Hidden size
- 3,072
- KV heads · head dim
- 8 · 128
- Vocabulary
- 200,064
- Native dtype
- —
- Context window
- 204,800 tokens
- Released
- 2026-04-09
- Last modified on Hub
- 2026-04-20
Features & licensing
- License
- other
- Commercial use
- No
- Modalities
- text
- Tool / function calling
- No
- Reasoning mode
- No
- Languages
- EN
- Quantised variants on Hub
- GGUF, bitsandbytes
- Pipeline
- text-generation
Task fit
Editorial scores (0–100) used by the recommendation engine.
- Chat70
- RAG / Q&A70
- Code70
- Summarisation70
- Extraction70
- Agents70
Hardware to run MiniMax-M2.7
Weights need about 503 GB at FP16, 264 GB at INT8 and 151 GB at INT4. The KV cache adds roughly 254 MB per 1,000 tokens per request. For a reference workload of 5 requests per second with 1,500 input and 300 output tokens, total GPU memory is around 243.7 GB, which fits on 4 × NVIDIA A100 (80 GB).
| Configuration | VRAM | Utilisation | Est. first token | Cloud / month |
|---|---|---|---|---|
| 6 × NVIDIA L40S (48 GB) | 288 GB | 85 % | ~0.2 s | $8,322 |
| 4 × NVIDIA A100 (80 GB)recommended | 320 GB | 76 % | ~0.1 s | $9,344 |
| 4 × NVIDIA H100 (80 GB) | 320 GB | 76 % | ~0.0 s | $13,140 |
Estimates only. Run an assessment for your own traffic.
Auto-imported from Hugging Face. Architecture, license and feature data are synced from the model repository; the task-fit scores below are provisional defaults until a curator reviews them.