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Can the Apple M2 (8 GB) run Command R7B?

65 ptsGood (B) · 65 of 100 points: Model quality 16.6/45, Memory fit 24.0/25, Speed 9.8/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 5.3 GB of 5.4 GB usable, with an estimated speed of 17.0 tok/s.

Best quant
Q3_K_M
Generation speed
17.0 tok/s
Prompt processing
278 tok/s
First token (1K prompt)
3.7 s
Largest safe context
8K

Download Command R7B

Recommended for the Apple M2 (8 GB): Q3_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run command-r7b:7b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

Download the Q3_K_M file (opens in a new tab)

huggingface.co/bartowski/c4ai-command-r7b-12-2024-GGUF (opens in a new tab)

ollama run hf.co/bartowski/c4ai-command-r7b-12-2024-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/CohereLabs/c4ai-command-r7b-12-2024 (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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.16.6 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.9.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total65.4 / 10065 ptsGood (B) · 65 of 100 points: Model quality 16.6/45, Memory fit 24.0/25, Speed 9.8/15, Memory use 15.0/15

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

Every quantization on the Apple M2 (8 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.6 GB 5.4 GB 21.6 tok/s Tight fit 63 ptsGood (B) · 63 of 100 points: Model quality 13.9/45, Memory fit 24.0/25, Speed 11.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 5.3 GB 5.4 GB 17.0 tok/s Tight fit 65 ptsGood (B) · 65 of 100 points: Model quality 16.6/45, Memory fit 24.0/25, Speed 9.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 6.3 GB 5.4 GB 11.2 tok/s Heavy offload 45 ptsUsable (C) · 45 of 100 points: Model quality 18.8/45, Memory fit 4.0/25, Speed 7.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 7.1 GB 5.4 GB — Won't run 5 ptsWon't run (F) · 5 of 100 points: Model quality 5.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q6_K 8.0 GB 5.4 GB — Won't run 5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
Q8_0 9.9 GB 5.4 GB — Won't run 5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
F16 17.5 GB 5.4 GB — Won't run 5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.