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Can the Apple M2 Pro (32 GB) run MiMo-VL 7B RL?

71 ptsGood (B) · 71 of 100 points: Model quality 24.8/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 9.2/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 8.6 GB of 21.4 GB usable, with an estimated speed of 25.4 tok/s.

Best quant
Q4_K_M
Generation speed
25.4 tok/s
Prompt processing
507 tok/s
First token (1K prompt)
2.0 s
Largest safe context
93K

Download MiMo-VL 7B RL

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/XiaomiMiMo/MiMo-VL-7B-RL-GGUF (opens in a new tab)

ollama run hf.co/XiaomiMiMo/MiMo-VL-7B-RL-GGUF

Original weights · Hugging Face

Full-precision files from Xiaomi, for fine-tuning, vLLM, Transformers.

huggingface.co/XiaomiMiMo/MiMo-VL-7B-RL (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.24.8 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.9.2 / 15
Total71.0 / 10071 ptsGood (B) · 71 of 100 points: Model quality 24.8/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 9.2/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.8 GB 21.4 GB 41.8 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 18.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.4/15 GGUF files (opens in a new tab)
Q3_K_M 7.6 GB 21.4 GB 32.9 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 21.9/45, Memory fit 25.0/25, Speed 13.6/15, Memory use 8.2/15 GGUF files (opens in a new tab)
Q4_K_M 8.6 GB 21.4 GB 25.4 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 24.8/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 9.2/15 GGUF files (opens in a new tab)
Q5_K_M 9.4 GB 21.4 GB 21.0 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 25.3/45, Memory fit 25.0/25, Speed 10.9/15, Memory use 10.1/15 GGUF files (opens in a new tab)
Q6_K 10.3 GB 21.4 GB 17.7 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 25.8/45, Memory fit 25.0/25, Speed 10.0/15, Memory use 11.1/15 GGUF files (opens in a new tab)
Q8_0 12.3 GB 21.4 GB 13.0 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 8.5/15, Memory use 13.3/15 GGUF files (opens in a new tab)
F16 20.1 GB 21.4 GB 6.0 tok/s Tight fit 70 ptsGood (B) · 70 of 100 points: Model quality 26.1/45, Memory fit 24.0/25, Speed 5.8/15, Memory use 15.0/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.