Choosing the Right Flux Model for Your GPU: A Comprehensive Guide + Workflows

How To Choose The Right Flux Model For Your GPU - A Guide

If you’re just getting started with AI image generation because you’ve seen the amazing stuff Flux can create, you’re probably feeling pretty lost looking at all the different models available.


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Trust me, I get it – I’ve been working with AI art for years now and even I was overwhelmed trying to figure out which version would work best with my video card and VRAM.

The good news is that picking the right Flux model really comes down to one main thing: your hardware.

In this guide, I’ll help you figure out exactly which Flux model to use based on your GPU’s VRAM, so you can find that sweet spot between image quality and performance.

Quick Reference Guide

VRAM Model Notes
24GB+ FP16 Best quality, ideal for RTX 3090/4090
12-16GB GGUF-Q8 Excellent balance, great for RTX 3060-12GB/3080
6-12GB GGUF-Q4 or BNB nf4 Good for RTX 3060/2070 Super

Detailed Breakdown by VRAM Capacity

High-End GPUs (24GB+ VRAM)

Recommended: Flux Dev FP16

  • Perfect for: RTX 3090, 4090, or professional GPUs
  • Advantages:
    • Highest possible image quality
    • Seems to produce the most legible text rendering
    • No compression artifacts
  • Considerations:
    • Requires ~22GB VRAM
    • May spill into system RAM, affecting speed

Mid-Range GPUs (12-16GB VRAM)

Recommended: Flux Dev GGUF-Q8

  • Perfect for: RTX 3070/3080/3060 Ti
  • Advantages:
    • Nearly identical quality to FP16
    • Uses half the VRAM of FP16
    • Better quality than BNB nf4
  • Considerations:
    • Slightly slower due to decompression
    • Excellent balance of quality and resource usage
    • The “sweet spot” for most users

Budget GPUs (6-12GB VRAM)

Primary Recommendation: GGUF-Q4

  • Perfect for: RTX 3060/2070 Super
  • Advantages:
    • Better quality than BNB nf4
    • Strong Lora support
    • Efficient VRAM usage
  • Considerations:
    • Some quality loss compared to Q8
    • Smaller chunk sizes for more precise computation

Alternative: BNB nf4

  • Advantages:
    • Fastest render speeds
  • Considerations:
    • Noticeable quality loss
    • Generally considered outdated compared to GGUF options

The Power of Quantization

One of the most exciting developments in making Flux accessible to more users is quantization. Here’s why it matters:

  • Quantization reduces model size by using fewer bits (8-bit and 4-bit precision), allowing these powerful models to run on consumer-grade hardware
  • The BitsAndBytes library provides PyTorch support for k-bit quantization, significantly reducing memory consumption for both inference and training
  • Thanks to these advances, you can now run Flux models on GPUs with as little as 6GB of VRAM, making AI image generation accessible to a much wider range of users

This is why we have options like GGUF-Q8 and GGUF-Q4 – they’re the result of these quantization techniques being applied to make Flux more efficient while maintaining as much quality as possible.

Comparison – My Personal Testing

I thought it might be helpful to include the performance and results that I got with these models using my setup.

I’m running a Nvidia 3060 GPU with 12GB VRAM on a PC with 64GB RAM.

These tests were all run using the ComfyUI Flux Workflows that I’ve included down below.

Prompt: endless lake with a still, mirror-like surface, perfectly reflecting the pink and orange hues of a sunset; in the foreground, small clusters of floating lanterns glow with soft, golden light, their reflections perfectly aligned with their real counterparts; further back, the lanterns seem to drift off the water and float upward into the sky, blurring the boundary between lake and clouds as if they’re merging into a distant celestial sea

Flux Dev FP16

Speed: 4.25s/it

flux dev comparison fp16 results using a 3060 12gb vram 64gb ram

Flux Dev GGUF-Q8

Speed: 2.90s/it

flux dev comparison GGUF-Q8 results using a 3060 12gb vram 64gb ram

Flux Dev GGUF-Q4

Speed: 3.02s/it

flux dev comparison GGUF-Q4 results using a 3060 12gb vram 64gb ram

Flux Dev BNB-NF4

Speed: 2.15s/it

flux dev comparison BNB-NF4 results using a 3060 12gb vram 64gb ram

From my testing above you can see some interesting results. Firstly, for whatever reason, the GGUF-Q4 image seems to look the best, at least to my eyes.

Another thing of note is that on my 12GB VRAM card, the GGUF-Q4 and the GGUF-Q8 were about equal in speed with each other.

The BNB-NF4 version was clearly the quickest, by close to 1s/it. Honestly, the image quality on that one doesn’t even look worse than the others, just slightly different.

In different prompts, the impact might be more obvious, but take from this what you will.

If you had different results, I’d love to hear about them in the comments!

Understanding Flux Model Variants

Flux comes in several different versions, each serving different needs:

  • Flux-Schnell: An open-source, distilled version of the model that’s freely available to the community
  • Flux-Dev: An open model that comes with a restrictive license
  • Flux-Pro: A closed-source version that’s accessible through APIs

Conclusion & Getting Started Workflows

The ideal Flux model choice depends heavily on your available VRAM and specific needs:

  • FP16 for uncompromising quality (24GB+ VRAM)
  • GGUF-Q8 for the best balance (12-16GB VRAM)
  • GGUF-Q4 for limited VRAM systems (6-12GB VRAM)

Now that you know which model variant matches your hardware, you can begin creating with Flux.

I use ComfyUI primarily to generate with Flux but there are also others such as ForgeUI.

Workflows

If you choose to use ComfyUI, here are a couple of simple workflows that I made which you can load into ComfyUI to get generating quickly.

Just download the appropriate workflow and load the file into ComfyUI. Make sure to select the models on your computer.

If you encounter a nodes missing message, click on Manager in the ComfyUI menu, and then click on Install Missing Custom Nodes and install the ones that are missing and restart.

Remember to check your GPU’s VRAM capacity and choose the appropriate model version from the guide above.

If you found this article helpful, share it on social media or let me know what you think in the comments below.

Happy generating!

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