Stable Diffusion 3.5 Large Turbo on Apple M5 Max (64 GB)

Can the Apple M5 Max (64 GB) run Stable Diffusion 3.5 Large Turbo?

82 pts

Yes, it runs well

Runs well. At FP16 it needs about 30.0 GB of 48.0 GB usable, with an estimated speed of 9.2 s.

Best precision: FP16
Memory mode: Everything on GPU
Time per image: 9.2 s
Output: 1024×1024, 4 steps

Download Stable Diffusion 3.5 Large Turbo

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/stable-diffusion-3.5-large-turbo-gguf (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Stability AI, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo (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 Stability AI. The weights themselves download from Hugging Face.

github.com/Stability-AI/sd3.5 (opens in a new tab)

git clone https://github.com/Stability-AI/sd3.5

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.	27.9 / 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.	14.4 / 15
Total	82.3 / 100	82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15

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

Every precision on the Apple M5 Max (64 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	30.0 GB	48.0 GB	9.2 s	Everything on GPU	Runs well	82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15	Original weights (opens in a new tab)
`FP8`	16.3 GB	48.0 GB	8.3 s	Everything on GPU	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 27.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15	Original weights (opens in a new tab)
`Q8 GGUF`	16.8 GB	48.0 GB	11 s	Everything on GPU	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 14.6/15, Memory use 8.1/15	GGUF (opens in a new tab)
`Q4 GGUF`	13.0 GB	48.0 GB	11 s	Everything on GPU	Runs well	71 ptsGood (B) · 71 of 100 points: Model quality 25.9/45, Memory fit 25.0/25, Speed 14.3/15, Memory use 6.3/15	GGUF (opens in a new tab)

Other hardware that runs it

82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q8 GGUF · 2.0 s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q8 GGUF · 3.5 s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q8 GGUF · 1.8 s
82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.4/15 Apple M4 Max (64 GB) FP16 · 9.4 s
80 ptsGreat (A) · 80 of 100 points: Model quality 25.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q4 GGUF · 2.5 s
80 ptsGreat (A) · 80 of 100 points: Model quality 25.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060 Ti 16GB Q4 GGUF · 5.2 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.