on

Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run HiDream-I1 Full?

78Great quality (A) · 78 of 100: how capable the model is at NF4, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (encoder unloaded after use)

Very slow (encoder unloaded after use). At NF4 it needs about 12.6 GB of 18.2 GB usable and takes about 8.1 min per image.

Best precision
NF4
Memory mode
Text encoder offloaded
Time per image
8.1 min
Output
1024×1024, 50 steps

Best app to run it on the Intel Core Ultra 200H (Arc 140T) (32 GB)

On Windows

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

  • Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

On Linux

ComfyUI

Desktop app · runs on PyTorch (ComfyUI), GGUF via ComfyUI-GGUF

  • Runs this model with built-in or popular custom nodes; GGUF files load with ComfyUI-GGUF.

Get ComfyUI

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download HiDream-I1 Full

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/HiDream-I1-Full-gguf (opens in a new tab)

Original weights · Hugging Face

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

huggingface.co/HiDream-ai/HiDream-I1-Full (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/HiDream-ai/HiDream-I1 (opens in a new tab)

git clone https://github.com/HiDream-ai/HiDream-I1

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 78Great quality (A) · 78 of 100: how capable the model is at NF4, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at NF4, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowencoder unloaded after use
How well it runs on the Intel Core Ultra 200H (Arc 140T) (32 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.35.2 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.20.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.2.2 / 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
Match score72.3 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every precision on the Intel Core Ultra 200H (Arc 140T) (32 GB)

Quant Memory needed Available Speed Memory mode Runs Quality Download
FP16 14.8 GB 18.2 GB 9.1 min Sequential CPU offload Very slowCPU offload 84Great quality (A) · 84 of 100: how capable the model is at FP16, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
FP8 8.8 GB 18.2 GB 8.2 min Sequential CPU offload Very slowCPU offload 82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
Q8 GGUF 9.3 GB 18.2 GB 10.5 min Sequential CPU offload Very slowCPU offload 83Great quality (A) · 83 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4 GGUF 13.3 GB 18.2 GB 8.8 min Text encoder offloaded Very slowencoder unloaded after use 78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
NF4 12.6 GB 18.2 GB 8.1 min Text encoder offloaded Very slowencoder unloaded after use 78Great quality (A) · 78 of 100: how capable the model is at NF4, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Other hardware that runs it

Quick answers

How much memory does HiDream-I1 Full need on the Intel Core Ultra 200H (Arc 140T) 32GB?

At NF4 it needs about 12.6 GB. The Intel Core Ultra 200H (Arc 140T) 32GB has 18.2 GB usable.

How long does HiDream-I1 Full take on the Intel Core Ultra 200H (Arc 140T) 32GB?

About 8.1 min per image at NF4 and default settings, an estimate scaled from a reference timing by this hardware’s compute speed.

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.

Was this page helpful?