Mistral Medium 3.5 (128B) on Apple M5 Max (64 GB)

Can the Apple M5 Max (64 GB) run Mistral Medium 3.5 (128B)?

50 pts

Barely: most of the model spills into system RAM

Heavy offload. At Q2_K it needs about 58.5 GB of 48.0 GB usable, with an estimated speed of 7.1 tok/s.

Best quant: Q2_K
Generation speed: 7.1 tok/s
Prompt processing: 91.8 tok/s
First token (1K prompt): 11.2 s
Largest safe context: —

Download Mistral Medium 3.5 (128B)

Recommended for the Apple M5 Max (64 GB): Q2_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-medium-3.5:128b

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 Q2_K file (opens in a new tab)

huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Medium-3.5-128B-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Medium-3.5-128B (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 7 days ago against Hugging Face, Ollama and GitHub.

How the C 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.	25.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	6.3 / 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
Total	50.6 / 100	50 ptsUsable (C) · 50 of 100 points: Model quality 25.3/45, Memory fit 4.0/25, Speed 6.3/15, 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 M5 Max (64 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	58.5 GB	48.0 GB	7.1 tok/s	Heavy offload	50 ptsUsable (C) · 50 of 100 points: Model quality 25.3/45, Memory fit 4.0/25, Speed 6.3/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q3_K_M`	70.0 GB	48.0 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q4_K_M`	85.3 GB	48.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q5_K_M`	98.1 GB	48.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q6_K`	112 GB	48.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q8_0`	143 GB	48.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`F16`	263 GB	48.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, 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.

Other hardware that runs it

83 ptsGreat (A) · 83 of 100 points: Model quality 34.4/45, Memory fit 24.0/25, Speed 9.8/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 17.2 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 35.8/45, Memory fit 25.0/25, Speed 5.1/15, Memory use 15.0/15 Apple M2 Ultra (192 GB) Q6_K · 4.6 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 35.8/45, Memory fit 25.0/25, Speed 5.1/15, Memory use 14.9/15 Apple M3 Ultra (256 GB) Q6_K · 4.7 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 34.4/45, Memory fit 24.0/25, Speed 5.0/15, Memory use 15.0/15 Apple M4 Max (128 GB) Q4_K_M · 4.5 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 34.4/45, Memory fit 25.0/25, Speed 3.8/15, Memory use 15.0/15 NVIDIA GB10 (128 GB) Q4_K_M · 2.6 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 34.4/45, Memory fit 24.0/25, Speed 3.5/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) Q4_K_M · 2.1 tok/s

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.