Can the Apple M5 Max (128 GB) run FLUX.2 [dev]?

83 pts

Yes, it runs well

Runs well. At Q8 GGUF it needs about 61.9 GB of 96.0 GB usable, with an estimated speed of 4.0 min.

Best precision: Q8 GGUF
Memory mode: Everything on GPU
Time per image: 4.0 min
Output: 1024×1024, 28 steps

Download FLUX.2 [dev]

Recommended for the Apple M5 Max (128 GB): q8_gguf. Every other precision has its own download link in the table below.

Quantized files · GGUF for ComfyUI

Smaller GGUF versions for ComfyUI (with the ComfyUI-GGUF node). No sign-in needed; the model’s license still applies.

Download the q8_gguf (Q8_0) file (opens in a new tab)

huggingface.co/unsloth/FLUX.2-dev-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Black Forest Labs, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/black-forest-labs/FLUX.2-dev (opens in a new tab)

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

Official repo · GitHub

Reference code, inference scripts and docs from Black Forest Labs. The weights themselves download from Hugging Face.

github.com/black-forest-labs/flux2 (opens in a new tab)

git clone https://github.com/black-forest-labs/flux2

Links checked 5 days 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.	41.0 / 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.	3.1 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	14.9 / 15
Total	83.9 / 100	83 ptsGreat (A) · 83 of 100 points: Model quality 41.0/45, Memory fit 25.0/25, Speed 3.1/15, Memory use 14.9/15

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

Every precision on the Apple M5 Max (128 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	67.9 GB	96.0 GB	3.6 min	Text encoder offloaded	Runs well	79 ptsGreat (A) · 79 of 100 points: Model quality 41.4/45, Memory fit 20.0/25, Speed 3.2/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	59.7 GB	96.0 GB	3.1 min	Everything on GPU	Runs well	83 ptsGreat (A) · 83 of 100 points: Model quality 40.6/45, Memory fit 25.0/25, Speed 3.5/15, Memory use 14.3/15	Original weights (opens in a new tab)
`Q8 GGUF`	61.9 GB	96.0 GB	4.0 min	Everything on GPU	Runs well	83 ptsGreat (A) · 83 of 100 points: Model quality 41.0/45, Memory fit 25.0/25, Speed 3.1/15, Memory use 14.9/15	GGUF (opens in a new tab)
`Q4 GGUF`	46.8 GB	96.0 GB	4.1 min	Everything on GPU	Runs well	77 ptsGreat (A) · 77 of 100 points: Model quality 38.5/45, Memory fit 25.0/25, Speed 3.0/15, Memory use 11.2/15	GGUF (opens in a new tab)

Other hardware that runs it

89 ptsExcellent (S) · 89 of 100 points: Model quality 40.6/45, Memory fit 25.0/25, Speed 9.7/15, Memory use 14.3/15 NVIDIA RTX PRO 6000 Blackwell FP8 · 24 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 40.6/45, Memory fit 25.0/25, Speed 6.0/15, Memory use 14.3/15 AMD Ryzen AI Max+ 395 (128 GB) FP8 · 62 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 41.4/45, Memory fit 25.0/25, Speed 3.8/15, Memory use 15.0/15 Apple M2 Ultra (192 GB) FP16 · 2.6 min
84 ptsGreat (A) · 84 of 100 points: Model quality 41.4/45, Memory fit 25.0/25, Speed 3.8/15, Memory use 13.9/15 Apple M3 Ultra (256 GB) FP16 · 2.5 min
83 ptsGreat (A) · 83 of 100 points: Model quality 41.0/45, Memory fit 25.0/25, Speed 3.0/15, Memory use 14.9/15 Apple M4 Max (128 GB) Q8 GGUF · 4.0 min
83 ptsGreat (A) · 83 of 100 points: Model quality 40.6/45, Memory fit 25.0/25, Speed 5.8/15, Memory use 12.0/15 NVIDIA GB10 (128 GB) FP8 · 68 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.