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Can the Intel Core Ultra 200H (Arc 140T) (32 GB) run FLUX.1 [dev]?

74Great quality (A) · 74 of 100: how capable the model is at NF4, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly

Slow. At NF4 it needs about 14.4 GB of 18.2 GB usable and takes about 2.1 min per image.

Best precision
NF4
Memory mode
Everything on GPU
Time per image
2.1 min
Output
1024×1024, 28 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 FLUX.1 [dev]

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/FLUX.1-dev-gguf (opens in a new tab)

Original weights · Hugging Face

Full-precision files from Black Forest Labs, for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/black-forest-labs/FLUX.1-dev (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 Black Forest Labs. The weights themselves download from Hugging Face.

github.com/black-forest-labs/flux (opens in a new tab)

git clone https://github.com/black-forest-labs/flux

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

How it’s rated

Quality 74Great quality (A) · 74 of 100: how capable the model is at NF4, whatever it runs on. Match score 77/100 (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 Slow
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.33.5 / 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.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.3 / 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 score77.8 / 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 11.0 GB 18.2 GB 2.4 min Sequential CPU offload Very slowCPU offload 80Great quality (A) · 80 of 100: how capable the model is at FP16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
FP8 14.7 GB 18.2 GB 1.8 min Text encoder offloaded Slowencoder unloaded after use 78Great quality (A) · 78 of 100: how capable the model is at FP8, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
Q8 GGUF 15.5 GB 18.2 GB 2.3 min Text encoder offloaded Very slowtight fit · encoder unloaded after use 79Great quality (A) · 79 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4 GGUF 14.8 GB 18.2 GB 2.2 min Everything on GPU Very slow 74Great quality (A) · 74 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
NF4 14.4 GB 18.2 GB 2.1 min Everything on GPU Slow 74Great quality (A) · 74 of 100: how capable the model is at NF4, whatever it runs on. Match score 77/100 (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 FLUX.1 [dev] need on the Intel Core Ultra 200H (Arc 140T) 32GB?

At NF4 it needs about 14.4 GB. The Intel Core Ultra 200H (Arc 140T) 32GB has 18.2 GB usable.

How long does FLUX.1 [dev] take on the Intel Core Ultra 200H (Arc 140T) 32GB?

About 2.1 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.

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