FLUX.1 [schnell] on Apple M3 Ultra (256 GB)

Can the Apple M3 Ultra (256 GB) run FLUX.1 [schnell]?

75 pts

Yes, it runs well

Runs well. At FP16 it needs about 36.4 GB of 192 GB usable, with an estimated speed of 8.5 s.

Best precision: FP16
Memory mode: Everything on GPU
Time per image: 8.5 s
Output: 1024×1024, 4 steps

Download FLUX.1 [schnell]

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.

huggingface.co/city96/FLUX.1-schnell-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.1-schnell (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/flux (opens in a new tab)

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

Links checked 7 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.	31.5 / 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.	4.4 / 15
Total	75.9 / 100	75 ptsGreat (A) · 75 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15

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

Every precision on the Apple M3 Ultra (256 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	36.4 GB	192 GB	8.5 s	Everything on GPU	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15	Original weights (opens in a new tab)
`FP8`	19.6 GB	192 GB	7.6 s	Everything on GPU	Runs well	73 ptsGreat (A) · 73 of 100 points: Model quality 30.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.4/15	Original weights (opens in a new tab)
`Q8 GGUF`	20.4 GB	192 GB	9.7 s	Everything on GPU	Runs well	73 ptsGreat (A) · 73 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.5/15	GGUF (opens in a new tab)
`Q4 GGUF`	14.8 GB	192 GB	10 s	Everything on GPU	Runs well	70 ptsGood (B) · 70 of 100 points: Model quality 29.3/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 1.8/15	GGUF (opens in a new tab)
`NF4`	14.4 GB	192 GB	9.3 s	Everything on GPU	Runs well	71 ptsGood (B) · 71 of 100 points: Model quality 29.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 1.7/15	Original weights (opens in a new tab)

Other hardware that runs it

85 ptsExcellent (S) · 85 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15 NVIDIA GeForce RTX 5090 Q8 GGUF · 1.9 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 30.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX FP8 · 2.0 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 30.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 FP8 · 3.4 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 30.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 FP8 · 1.8 s
82 ptsGreat (A) · 82 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 11.2/15, Memory use 15.0/15 Apple M1 Max (64 GB) FP16 · 18 s
82 ptsGreat (A) · 82 of 100 points: Model quality 29.3/45, Memory fit 23.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q4 GGUF · 3.2 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.