作者: Jakub Rusinowski · 最后更新: 2026年9月6日
Model library → MiMo-V2.5 → MiMo-V2.5 310B
310B total, 15B active per token, MIT, 1M context. Every one of the 310B must be resident — 188 GB at Q4_K_M, so a 192 GB Mac Studio or a small multi-GPU box — while per-token bandwidth is that of a 15B model. The standard variant rather than the flagship, and the cheaper of the two to serve.
MiMo-V2.5 310B needs about 188 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.
| Parameters | 310 Billion (15B active) |
| Context window | 1,000,000 |
| Architecture | Mixture-of-Experts |
| Provider | Xiaomi |
| Licence | MIT |
| Specified at | Q4_K_M |
| System RAM | 256 GB |
| Record updated | 2026-09-06 |
MIT — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.
Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.
| Quant | Weights | VRAM needed | Est. speed | Fit on 24 GB |
|---|---|---|---|---|
| Q2_K | 101.9 GB | 102.7 GB | — | Won't fit |
| Q3_K_M | 132.1 GB | 132.9 GB | — | Won't fit |
| Q4_K_M | 187.2 GB | 188.0 GB | — | Won't fit |
| Q5_K_M | 219.7 GB | 220.5 GB | — | Won't fit |
| Q6_K | 254.2 GB | 255.0 GB | — | Won't fit |
| Q8_0 | 329.4 GB | 330.2 GB | — | Won't fit |
| F16 | 620.0 GB | 620.8 GB | — | Won't fit |
Want the memory numbers alone, at every quantization level and your own context length? Use the MiMo-V2.5 310B VRAM calculator.
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The cheapest catalogued GPU that runs MiMo-V2.5 310B is the Apple M2 Ultra (192 GB).
Install Ollama, then run:
ollama run mimo-v2-5
Weights on Hugging Face: XiaomiMiMo/MiMo-V2.5.
Best for: long context, agentic tasks, enterprise, cost efficient.
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