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Can the NVIDIA GeForce RTX 4070 Ti SUPER run FLUX.1 [dev]?

74Great quality (A) · 74 of 100: how capable the model is at NF4, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At NF4 it needs about 14.4 GB of 16.0 GB usable and takes about 21 s per image.

Best precision
NF4
Memory mode
Everything on GPU
Time per image
21 s
Output
1024×1024, 28 steps

Best app to run it on the NVIDIA GeForce RTX 4070 Ti SUPER

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 81/100 (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 Runs welltight fit
How well it runs on the NVIDIA GeForce RTX 4070 Ti SUPER: 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 everything sits in GPU memory; fewer when part spills to system RAM.23.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.10.3 / 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 score81.8 / 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 NVIDIA GeForce RTX 4070 Ti SUPER

Quant Memory needed Available Speed Memory mode Runs Quality Download
FP16 11.0 GB 16.0 GB 25 s Sequential CPU offload Runs wellCPU offload 80Great quality (A) · 80 of 100: how capable the model is at FP16, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
FP8 14.7 GB 16.0 GB 18 s Text encoder offloaded Runs welltight fit · encoder in RAM 78Great quality (A) · 78 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)
Q8 GGUF 7.1 GB 16.0 GB 29 s Sequential CPU offload Runs wellCPU offload 79Great quality (A) · 79 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4 GGUF 14.8 GB 16.0 GB 23 s Everything on GPU Runs welltight fit 74Great quality (A) · 74 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. GGUF (opens in a new tab)
NF4 14.4 GB 16.0 GB 21 s Everything on GPU Runs welltight fit 74Great quality (A) · 74 of 100: how capable the model is at NF4, whatever it runs on. Match score 81/100 (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 RTX 4070 Ti SUPER 16GB?

At NF4 it needs about 14.4 GB. The RTX 4070 Ti SUPER 16GB has 16.0 GB usable.

How long does FLUX.1 [dev] take on the RTX 4070 Ti SUPER 16GB?

About 21 s 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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