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Can the Apple M3 Ultra (512 GB) run Mistral Small 4 (119B-A6B)?

83 ptsGreat (A) · 83 of 100 points: Model quality 35.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15

Yes, it runs well

Runs well. At Q6_K it needs about 129 GB of 384 GB usable, with an estimated speed of 50.1 tok/s.

Best quant
Q6_K
Generation speed
50.1 tok/s
Prompt processing
2,935 tok/s
First token (1K prompt)
0.3 s
Largest safe context
1,024K

Download Mistral Small 4 (119B-A6B)

Recommended for the Apple M3 Ultra (512 GB): Q6_K. Every other quantization has its own download link in the table below.

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/unsloth/Mistral-Small-4-119B-2603-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Small-4-119B-2603 (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.

Links checked 9 hours 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.35.9 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.7.8 / 15
Total83.7 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 35.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15

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

Every quantization on the Apple M3 Ultra (512 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 79.3 GB 384 GB 118 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 25.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 90.0 GB 384 GB 93.3 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 30.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.4/15 GGUF · folder (opens in a new tab)
Q4_K_M 104 GB 384 GB 72.1 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 34.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.3/15 GGUF · folder (opens in a new tab)
Q5_K_M 116 GB 384 GB 59.6 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 35.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF · folder (opens in a new tab)
Q6_K 129 GB 384 GB 50.1 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 35.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF · folder (opens in a new tab)
Q8_0 158 GB 384 GB 36.7 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 36.3/45, Memory fit 25.0/25, Speed 14.4/15, Memory use 9.5/15 GGUF · folder (opens in a new tab)
F16 270 GB 384 GB 16.9 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 36.3/45, Memory fit 25.0/25, Speed 9.8/15, Memory use 15.0/15 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.

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.