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Can the Apple M2 (8 GB) run Mistral Large 3 (675B)?

8 ptsWon't run (F) · 8 of 100 points: Model quality 8.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

No, it doesn’t fit

No. Mistral Large 3 (675B) needs about 272 GB even at Q2_K; the Apple M2 (8 GB) has 5.4 GB usable.

Download Mistral Large 3 (675B)

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Ollama

Ollama offers this only as a cloud model (it runs on Ollama’s servers, not your machine). To run it locally, use the GGUF files below.

Quantized files · GGUF

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

Download the Q2_K files (opens in a new tab)

huggingface.co/unsloth/Mistral-Large-3-675B-Instruct-2512-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Large-3-675B-Instruct-2512-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Mistral AI. The weights themselves download from Hugging Face.

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

How the F 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.8.5 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.0.0 / 15
Total8.5 / 1008 ptsWon't run (F) · 8 of 100 points: Model quality 8.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

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 272 GB 5.4 GB — Won't run 8 ptsWon't run (F) · 8 of 100 points: Model quality 8.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q3_K_M 332 GB 5.4 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q4_K_M 413 GB 5.4 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q5_K_M 481 GB 5.4 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q6_K 555 GB 5.4 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
Q8_0 717 GB 5.4 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 1,352 GB 5.4 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (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.

Hardware that can run it

Models that do run on the Apple M2 (8 GB)

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.