FireRed-Image-Edit 1.1 on NVIDIA H100 NVL

Can the NVIDIA H100 NVL run FireRed-Image-Edit 1.1?

Yes, but slowly

Slow. At FP16 it needs about 61.2 GB of 94.0 GB usable, with an estimated speed of 60 s.

Best precision: FP16
Memory mode: Everything on GPU
Time per image: 60 s
Output: 1024×1024, 40 steps

Best app to run it on the NVIDIA H100 NVL

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

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

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 FireRed-Image-Edit 1.1

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/FireRedTeam/FireRed-Image-Edit-1.1-ComfyUI (opens in a new tab)

Original weights · Hugging Face

Full-precision files from rednote (FireRed Team), for fine-tuning, vLLM, Transformers, Diffusers or ComfyUI.

huggingface.co/FireRedTeam/FireRed-Image-Edit-1.1 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from rednote (FireRed Team). The weights themselves download from Hugging Face.

github.com/FireRedTeam/FireRed-Image-Edit (opens in a new tab)

git clone https://github.com/FireRedTeam/FireRed-Image-Edit

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 87Excellent quality (A+) · 87 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at FP16, 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 NVIDIA H100 NVL: 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.	39.2 / 45
Memory fitFull marks when everything sits in GPU 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.	6.1 / 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 score	85.3 / 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 NVIDIA H100 NVL

Quant	Memory needed	Available	Speed	Memory mode	Runs	Quality	Download
`FP16`	61.2 GB	94.0 GB	60 s	Everything on GPU	Slow	87Excellent quality (A+) · 87 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`FP8`	32.5 GB	94.0 GB	54 s	Everything on GPU	Slow	85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`Q8 GGUF`	33.9 GB	94.0 GB	70 s	Everything on GPU	Slow	86Excellent quality (A+) · 86 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4 GGUF`	24.3 GB	94.0 GB	73 s	Everything on GPU	Slow	80Great quality (A) · 80 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)

Other hardware that runs it

87Excellent quality (A+) · 87 of 100: how capable the model is at FP16, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell FP16 · 59 s · Slow
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q4 GGUF · 80 s · Slow
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q4 GGUF · 1.7 min · Slow (encoder in RAM)
80Great quality (A) · 80 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX Q4 GGUF · 1.9 min · Slow (encoder in RAM)
85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT FP8 · 2.1 min · Slow (CPU offload)
85Excellent quality (A+) · 85 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 FP8 · 2.1 min · Slow (CPU offload)

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.