The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is a powerful GPU suitable for many AI tasks. However, the FLUX.1 Schnell diffusion model, with its 12 billion parameters, requires precisely 24GB of VRAM when using FP16 precision. This creates a marginal compatibility scenario. The RTX 4090 technically meets the minimum VRAM requirement, but leaves absolutely no headroom for other processes, operating system overhead, or potential VRAM fragmentation. This lack of headroom can lead to out-of-memory errors or severely degraded performance due to constant swapping between VRAM and system RAM. The estimated 28 tokens/second suggests the model will run, but not at optimal speeds due to potential memory constraints and the relatively short context length of 77 tokens, which might limit the model's ability to capture long-range dependencies within the data.
Given the marginal VRAM situation, running FLUX.1 Schnell on the RTX 4090 will require careful optimization. First, close all unnecessary applications to free up as much VRAM as possible. Experiment with quantization techniques such as Q4_K_M or even lower precision to reduce the model's VRAM footprint. Consider using a framework optimized for low-VRAM usage, such as llama.cpp with appropriate flags, or text-generation-inference with memory-efficient attention mechanisms. If you continue to experience issues, explore offloading some layers to system RAM, although this will significantly reduce performance. If these optimizations are insufficient, consider using a GPU with more VRAM or distributing the model across multiple GPUs.