Qwen-Image 2512 on Intel Core Ultra 200H (Arc 140T) (96 GB)

Can the Intel Core Ultra 200H (Arc 140T) (96 GB) run Qwen-Image 2512?

81 pts

Yes, it runs well

Runs well. At Q8 GGUF it needs about 36.5 GB of 54.7 GB usable, with an estimated speed of 16.1 min.

Best precision: Q8 GGUF
Memory mode: Everything on GPU
Time per image: 16.1 min
Output: 1328×1328, 50 steps

Download Qwen-Image 2512

Recommended for the Intel Core Ultra 200H (Arc 140T) (96 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/Qwen-Image-2512-GGUF (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen-Image-2512 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/QwenLM/Qwen-Image (opens in a new tab)

git clone https://github.com/QwenLM/Qwen-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.	40.1 / 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.	1.5 / 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	81.6 / 100	81 ptsGreat (A) · 81 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 1.5/15, 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 Core Ultra 200H (Arc 140T) (96 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	47.2 GB	54.7 GB	14.7 min	Text encoder offloaded	Tight fit	75 ptsGreat (A) · 75 of 100 points: Model quality 40.5/45, Memory fit 18.0/25, Speed 1.6/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	35.1 GB	54.7 GB	12.6 min	Everything on GPU	Runs well	81 ptsGreat (A) · 81 of 100 points: Model quality 39.7/45, Memory fit 25.0/25, Speed 1.7/15, Memory use 14.8/15	Original weights (opens in a new tab)
`Q8 GGUF`	36.5 GB	54.7 GB	16.1 min	Everything on GPU	Runs well	81 ptsGreat (A) · 81 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 1.5/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q4 GGUF`	26.9 GB	54.7 GB	16.8 min	Everything on GPU	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 37.7/45, Memory fit 25.0/25, Speed 1.5/15, Memory use 11.4/15	GGUF (opens in a new tab)

Other hardware that runs it

86 ptsExcellent (S) · 86 of 100 points: Model quality 40.5/45, Memory fit 25.0/25, Speed 5.8/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell FP16 · 66 s
84 ptsGreat (A) · 84 of 100 points: Model quality 40.5/45, Memory fit 25.0/25, Speed 3.6/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) FP16 · 2.9 min
82 ptsGreat (A) · 82 of 100 points: Model quality 37.7/45, Memory fit 25.0/25, Speed 5.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q4 GGUF · 1.5 min
82 ptsGreat (A) · 82 of 100 points: Model quality 40.5/45, Memory fit 25.0/25, Speed 2.1/15, Memory use 15.0/15 Apple M4 Max (128 GB) FP16 · 8.8 min
82 ptsGreat (A) · 82 of 100 points: Model quality 40.1/45, Memory fit 25.0/25, Speed 1.9/15, Memory use 15.0/15 Apple M4 Max (64 GB) Q8 GGUF · 10.1 min
81 ptsGreat (A) · 81 of 100 points: Model quality 40.5/45, Memory fit 25.0/25, Speed 3.5/15, Memory use 12.8/15 NVIDIA GB10 (128 GB) FP16 · 3.1 min

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.