Can the Intel Arc B580 run Z-Image?

79 pts

Yes, it runs well

Runs well. At Q4 GGUF it needs about 10.1 GB of 12.0 GB usable, with an estimated speed of 84 s.

Best precision: Q4 GGUF
Memory mode: Everything on GPU
Time per image: 84 s
Output: 1024×1024, 50 steps

Download Z-Image

Recommended for the Intel Arc B580: q4_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 q4_gguf (Q4_K_S) file (opens in a new tab)

huggingface.co/unsloth/Z-Image-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 (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 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.	34.3 / 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.	5.2 / 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	79.5 / 100	79 ptsGreat (A) · 79 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 15.0/15

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

Every precision on the Intel Arc B580

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	6.5 GB	12.0 GB	1.5 min	Sequential CPU offload	Runs well	66 ptsGood (B) · 66 of 100 points: Model quality 36.9/45, Memory fit 12.0/25, Speed 5.0/15, Memory use 12.5/15	Original weights (opens in a new tab)
`FP8`	8.5 GB	12.0 GB	66 s	Text encoder offloaded	Runs well	76 ptsGreat (A) · 76 of 100 points: Model quality 36.2/45, Memory fit 20.0/25, Speed 5.8/15, Memory use 15.0/15	Original weights (opens in a new tab)
`Q8 GGUF`	9.0 GB	12.0 GB	85 s	Text encoder offloaded	Runs well	76 ptsGreat (A) · 76 of 100 points: Model quality 36.5/45, Memory fit 20.0/25, Speed 5.2/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q4 GGUF`	10.1 GB	12.0 GB	84 s	Everything on GPU	Runs well	79 ptsGreat (A) · 79 of 100 points: Model quality 34.3/45, Memory fit 25.0/25, Speed 5.2/15, Memory use 15.0/15	GGUF (opens in a new tab)

Other hardware that runs it

89 ptsExcellent (S) · 89 of 100 points: Model quality 36.9/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 FP16 · 14 s
86 ptsExcellent (S) · 86 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 15.0/15 AMD Radeon RX 9070 XT FP8 · 20 s
86 ptsExcellent (S) · 86 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080 FP8 · 20 s
86 ptsExcellent (S) · 86 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 10.2/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER FP8 · 22 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER FP8 · 24 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 36.2/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti FP8 · 25 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.