Can the NVIDIA A100 80GB SXM run HiDream-I1 Fast?

88 pts

Yes, it runs well

Runs well. At FP16 it needs about 64.4 GB of 80.0 GB usable, with an estimated speed of 10 s.

Best precision: FP16
Memory mode: Everything on GPU
Time per image: 10 s
Output: 1024×1024, 16 steps

Download HiDream-I1 Fast

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-Fast-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-Fast (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 5 days ago against Hugging Face, Ollama and GitHub.

How the S 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.	34.2 / 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.	14.7 / 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	88.9 / 100	88 ptsExcellent (S) · 88 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 14.7/15, 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 NVIDIA A100 80GB SXM

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	64.4 GB	80.0 GB	10 s	Everything on GPU	Runs well	88 ptsExcellent (S) · 88 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 14.7/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	33.7 GB	80.0 GB	9.4 s	Everything on GPU	Runs well	83 ptsGreat (A) · 83 of 100 points: Model quality 33.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.7/15	Original weights (opens in a new tab)
`Q8 GGUF`	34.9 GB	80.0 GB	12 s	Everything on GPU	Runs well	82 ptsGreat (A) · 82 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 13.7/15, Memory use 10.1/15	GGUF (opens in a new tab)
`Q4 GGUF`	26.9 GB	80.0 GB	13 s	Everything on GPU	Runs well	77 ptsGreat (A) · 77 of 100 points: Model quality 31.8/45, Memory fit 25.0/25, Speed 13.4/15, Memory use 7.8/15	GGUF (opens in a new tab)
`NF4`	26.2 GB	80.0 GB	11 s	Everything on GPU	Runs well	78 ptsGreat (A) · 78 of 100 points: Model quality 31.8/45, Memory fit 25.0/25, Speed 14.0/15, Memory use 7.6/15	Original weights (opens in a new tab)

Other hardware that runs it

89 ptsExcellent (S) · 89 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell FP16 · 7.3 s
86 ptsExcellent (S) · 86 of 100 points: Model quality 31.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 NF4 · 9.2 s
85 ptsExcellent (S) · 85 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 10.8/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) FP16 · 19 s
83 ptsGreat (A) · 83 of 100 points: Model quality 33.5/45, Memory fit 20.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 FP8 · 9.5 s
83 ptsGreat (A) · 83 of 100 points: Model quality 33.5/45, Memory fit 20.0/25, Speed 14.6/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX FP8 · 11 s
82 ptsGreat (A) · 82 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 10.4/15, Memory use 12.9/15 NVIDIA GB10 (128 GB) FP16 · 21 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.