MiMo-V2-Pro — Xiaomi 的本地 AI 模型
作者： Jakub Rusinowski · 最后更新：
Xiaomi's 1T+ MoE model originally launched anonymously as 'Hunter Alpha' in March 2026 before being revealed as MiMo-V2-Pro. Led by ex-DeepSeek engineer Luo Fuli. Was the largest model on OpenRouter at launch. Strong on PinchBench and ClawEval agentic benchmarks with ~42B active parameters per token. Available via API through Xiaomi and OpenRouter.
变体
MiMo-V2-Pro 最小的变体在 Q4_K_M 下约需 605 GB 显存——量化权重加框架开销，不含 KV 缓存。
| 模型 | Q4 下显存 | 显存 | 上下文 | 运行 |
|---|---|---|---|---|
| MiMo-V2-Pro 1T+ → 1T+ | ~604.6 GB | 1,000,000 | ollama run mimo-v2-pro |
显存为 Q4_K_M 下的量化权重加开销，与 GPU 与显存检测器使用同一引擎计算。
如何在本地运行 MiMo-V2-Pro
安装 Ollama，然后拉取标签。
ollama run mimo-v2-pro在上方选择一个尺寸，查看它自己的显存、速度估算和安装命令。
许可证
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
适用于: MiMo-V2-Pro 1T+我的 GPU 能运行 MiMo-V2-Pro 吗？
MiMo-V2-Pro — 常见问题
How much VRAM does MiMo-V2-Pro need?
MiMo-V2-Pro needs about 605 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: MiMo-V2-Pro 1T+ (605 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run MiMo-V2-Pro on an RTX 4090 (24 GB)?
MiMo-V2-Pro's smallest variant needs about 605 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.
What quantization should I use for MiMo-V2-Pro?
Q4_K_M is the best balance of quality and VRAM for MiMo-V2-Pro in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run MiMo-V2-Pro with Ollama?
MiMo-V2-Pro has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.