See What GPU to Use with FLUX.1 AI Image Model

As AI enthusiasts and developers, we’ve been exploring the new  FLUX.1 model, a powerful diffusion image generation model that has taken the community by storm. A question we frequently encounter is, “What GPU do I need to run FLUX.1?” After testing across different setups, we’re ready to share what we found. This guide breaks down the GPU requirements for different versions of FLUX.1, focusing on GPUs that can load the entire model into VRAM.

Understanding VRAM Requirements for FLUX.1 Versions

When selecting a GPU for FLUX.1, VRAM (Video RAM) is the key factor to consider. Both Dev and Schnell FLUX.1 models comes in several quantization versions, each with different VRAM needs depending on whether you’re using the full-size model or a quantized version. And because FLUX.1 is a transformer-based diffusion model, it can now be quantized to the GGUF file format, allowing for lower quantization levels (i.e., reduced VRAM requirements) while maintaining high image generation quality.

Here’s a quick overview:

• Official FP16 Models: The most accurate model. Require about 24 GB of VRAM.
• Q8, FP8 and NF4 Quantized Versions : These versions reduce the VRAM requirement to around 13 GB. The Q8 GGUF model is the best option – comes quite close to the FP16 version.
• Lower Quantized Versions: Depending on the level of quantization (Q2, Q3, Q4, Q5 and Q6), these models need between 6 GB and 16 GB of VRAM.
• While some frontends allow you to offload parts of the model to system RAM, for optimal performance, we recommend using GPUs that can contain the entire FLUX.1 model in VRAM.

24 GB+ VRAM: Official FP16 Models

For those seeking the highest quality with FLUX.1, use Dev and Schnell at FP16. These models require GPUs with at least 24 GB of VRAM to run efficiently.

Recommended GPUs:

• NVIDIA RTX 4090: This 24 GB GPU delivers outstanding performance.
• NVIDIA RTX 3090 / 3090 Ti: Both provide 24 GB of VRAM, making them suitable for running the full-size FLUX.1 models without a hitch.

In our tests, the RTX 4090 stood out with great speed and efficiency, though the RTX 3090 remains a the best option, especially if you can find it at good price as second hand.

16GB VRAM: Q8, FP8 and NF4 Quantized Versions

For users who need to balance performance with hardware accessibility, Q8 (GGUF), FP8 and NF4 quantized versions of FLUX.1 are excellent choices. These versions significantly reduce the VRAM requirement between to 16 GB.

Recommended GPUs:

• NVIDIA RTX 4070 Ti Super: A well-rounded choice with 16 GB of VRAM, providing excellent performance with Q8 quantized FLUX.1 versions.
• NVIDIA RTX 4060 Ti :Best price to performance ratio it this category. Great card for both gaming and AI.
• AMD Radeon RX 6800: This GPU comes with 16 GB of VRAM, offering plenty of headroom for running the models models.

In our experience, the RTX 4060 Ti provided an optimal blend of price, power efficiency and performance, making it a standout option for those working with this level of quantization.

12GB VRAM: Q6 and Q5 Quantization

For those seeking an optimal balance between price and performance, GPUs with 12 GB of VRAM are an excellent choice for running FLUX.1. The Q6 and Q5 quantized versions offer image quality that is remarkably close to the original, while reducing VRAM requirements to a more accessible range of 12 GB. This makes them an ideal solution for users who want high-quality output without the need for high-end hardware.

Recommended GPUs:

• NVIDIA RTX 4070: The fastest performer in this segment, offering exceptional speed and efficiency for running Q6 and Q5 quantizations.
• NVIDIA RTX 3060: With 12 GB of VRAM, this GPU comfortably handles most Q6 and Q5 quantized versions, making it a highly affordable and effective option for AI projects.

In our experience, the RTX 4070 delivers an outstanding blend of price, power efficiency, and performance, making it a standout choice for users working with these quantized versions of FLUX.1.

8 GB VRAM: GGUF Quantized Versions

For those with more budget-conscious setups or older hardware, the lower GGUF quantized versions of FLUX.1 (Q2, Q3, Q4) offer a path forward. These versions are designed to fit into GPUs with as little as 6 GB, depending on the specific quantization.

Recommended GPUs:

• NVIDIA RTX 4060: The fastest performer
• NVIDIA RTX 3050: Another cheap 8 GB option, suitable for lower FLUX.1 quantization.
• AMD Radeon RX 6600 XT: This 8 GB GPU offers good performance for running the smaller GGUF quantized models.

In our testing, the RTX 4060 proved to be a pleasant surprise, handling the GGUF versions with efficiency, making it a great choice for those just starting with FLUX.1

NVIDIA vs. AMD: A Performance Comparison

While both NVIDIA and AMD GPUs can run FLUX.1, NVIDIA generally leads in AI performance due to better support for CUDA and optimized drivers. In our tests, NVIDIA cards consistently outpaced their AMD counterparts, especially in terms of inference speed and stability.

That said, AMD GPUs remain a viable option, particularly if you already own one or are looking to save on costs. The GGUF quantized versions of FLUX.1 run well on AMD hardware, though you might experience longer generation times compared to NVIDIA GPUs.

Conclusion

The best GPU for FLUX.1 largely depends on the specific model version you plan to use and your available budget. For those who prioritize quality and have the budget, a high-end NVIDIA GPU with at least 24 GB of VRAM is the optimal choice. However, if you’re working with quantized models, GPUs with 12 GB or even 8 GB of VRAM can still deliver excellent results.