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Can the Apple M3 Ultra (256 GB) run Mistral Large 2.1 (2411)?

80 ptsGreat (A) · 80 of 100 points: Model quality 35.6/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 14.4/15

Yes, it runs well

Runs well. At Q6_K it needs about 119 GB of 192 GB usable, with an estimated speed of 4.9 tok/s.

Best quant
Q6_K
Generation speed
4.9 tok/s
Prompt processing
144 tok/s
First token (1K prompt)
7.1 s
Largest safe context
128K

Download Mistral Large 2.1 (2411)

Recommended for the Apple M3 Ultra (256 GB): Q6_K. 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 mistral-large:123b-instruct-2411-q6_K

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 Q6_K files (opens in a new tab)

huggingface.co/bartowski/Mistral-Large-Instruct-2411-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Mistral-Large-Instruct-2411-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Large-Instruct-2411 (opens in a new tab)

No sign-in needed.

Links checked 1 hour ago against Hugging Face, Ollama and GitHub.

How the A 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.35.6 / 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.5.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.14.4 / 15
Total80.2 / 10080 ptsGreat (A) · 80 of 100 points: Model quality 35.6/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 14.4/15

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

Every quantization on the Apple M3 Ultra (256 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 68.0 GB 192 GB 11.6 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 8.1/15, Memory use 8.2/15 GGUF (opens in a new tab)
Q3_K_M 79.0 GB 192 GB 9.1 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 30.2/45, Memory fit 25.0/25, Speed 7.2/15, Memory use 9.5/15 GGUF · folder (opens in a new tab)
Q4_K_M 93.7 GB 192 GB 7.1 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 6.3/15, Memory use 11.3/15 GGUF · folder (opens in a new tab)
Q5_K_M 106 GB 192 GB 5.8 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 34.9/45, Memory fit 25.0/25, Speed 5.7/15, Memory use 12.7/15 GGUF · folder (opens in a new tab)
Q6_K 119 GB 192 GB 4.9 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 35.6/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 14.4/15 GGUF · folder (opens in a new tab)
Q8_0 149 GB 192 GB 3.6 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 36.0/45, Memory fit 25.0/25, Speed 4.5/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
F16 264 GB 192 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, 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.