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Can the Apple M2 Pro (32 GB) run RecurrentGemma 9B IT?

59 ptsGood (B) · 59 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 8.9/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 8.3 GB of 21.4 GB usable, with an estimated speed of 22.0 tok/s.

Best quant
Q4_K_M
Generation speed
22.0 tok/s
Prompt processing
438 tok/s
First token (1K prompt)
2.3 s
Largest safe context
8K

Download RecurrentGemma 9B IT

Original weights · Hugging Face

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

huggingface.co/google/recurrentgemma-9b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

Links checked 7 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.14.5 / 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.11.1 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.9 / 15
Total59.6 / 10059 ptsGood (B) · 59 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 8.9/15

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 6.3 GB 21.4 GB 36.1 tok/s Runs well 56 ptsUsable (C) · 56 of 100 points: Model quality 10.7/45, Memory fit 25.0/25, Speed 14.2/15, Memory use 6.8/15 All files (opens in a new tab)
Q3_K_M 7.1 GB 21.4 GB 28.4 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 12.9/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 7.7/15 All files (opens in a new tab)
Q4_K_M 8.3 GB 21.4 GB 22.0 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 8.9/15 All files (opens in a new tab)
Q5_K_M 9.3 GB 21.4 GB 18.1 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 14.8/45, Memory fit 25.0/25, Speed 10.1/15, Memory use 10.0/15 All files (opens in a new tab)
Q6_K 10.3 GB 21.4 GB 15.2 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 9.3/15, Memory use 11.1/15 All files (opens in a new tab)
Q8_0 12.6 GB 21.4 GB 11.2 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 15.3/45, Memory fit 25.0/25, Speed 7.9/15, Memory use 13.6/15 All files (opens in a new tab)
F16 21.7 GB 21.4 GB 4.4 tok/s Runs with offload 45 ptsUsable (C) · 45 of 100 points: Model quality 15.3/45, Memory fit 10.0/25, Speed 5.0/15, Memory use 15.0/15 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.