Can the Apple M2 Pro (32 GB) run Z-Image Turbo?

84 pts

Yes, it runs well

Runs well. At FP8 it needs about 12.6 GB of 21.4 GB usable, with an estimated speed of 28 s.

Best precision: FP8
Memory mode: Everything on GPU
Time per image: 28 s
Output: 1024×1024, 8 steps

Download Z-Image 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/unsloth/Z-Image-Turbo-GGUF (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Alibaba (Tongyi), for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/Tongyi-MAI/Z-Image-Turbo (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba (Tongyi). The weights themselves download from Hugging Face.

github.com/Tongyi-MAI/Z-Image (opens in a new tab)

git clone https://github.com/Tongyi-MAI/Z-Image

Links checked 6 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.	37.0 / 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.	9.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	13.5 / 15
Total	84.6 / 100	84 ptsGreat (A) · 84 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 9.0/15, Memory use 13.5/15

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

Every precision on the Apple M2 Pro (32 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	14.7 GB	21.4 GB	32 s	Text encoder offloaded	Runs well	81 ptsGreat (A) · 81 of 100 points: Model quality 37.8/45, Memory fit 20.0/25, Speed 8.3/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	12.6 GB	21.4 GB	28 s	Everything on GPU	Runs well	84 ptsGreat (A) · 84 of 100 points: Model quality 37.0/45, Memory fit 25.0/25, Speed 9.0/15, Memory use 13.5/15	Original weights (opens in a new tab)
`Q8 GGUF`	13.0 GB	21.4 GB	36 s	Everything on GPU	Runs well	84 ptsGreat (A) · 84 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 8.0/15, Memory use 14.0/15	GGUF (opens in a new tab)
`Q4 GGUF`	10.1 GB	21.4 GB	37 s	Everything on GPU	Runs well	78 ptsGreat (A) · 78 of 100 points: Model quality 35.2/45, Memory fit 25.0/25, Speed 7.8/15, Memory use 10.9/15	GGUF (opens in a new tab)

Other hardware that runs it

92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 FP16 · 2.1 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q8 GGUF · 3.8 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060 Ti 16GB Q8 GGUF · 7.7 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER Q8 GGUF · 4.6 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q8 GGUF · 4.1 s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5060 Ti 16GB Q8 GGUF · 7.3 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.