The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, technically meets the minimum VRAM requirement of 24GB for running the FLUX.1 Dev model in FP16 precision. However, this compatibility is marginal, leaving effectively no VRAM headroom. This lack of headroom can lead to out-of-memory errors, especially when dealing with larger batch sizes or more complex diffusion tasks. The RTX 4090's impressive 1.01 TB/s memory bandwidth and 16384 CUDA cores will contribute to reasonable performance, but the limited VRAM will be a bottleneck. Expect performance to be limited by memory swapping if the VRAM is exceeded.
Due to the tight VRAM constraints, running FLUX.1 Dev on the RTX 4090 will require careful optimization. Start by using a lower precision, such as INT8 or even INT4 quantization, to significantly reduce the VRAM footprint. Experiment with different inference frameworks like `llama.cpp` (if compatible with diffusion models) or `text-generation-inference` which offer memory-efficient implementations and quantization support. Monitor VRAM usage closely and reduce batch size to the smallest possible value. If these optimizations are insufficient, consider using a machine with more VRAM or exploring model parallelism techniques to distribute the model across multiple GPUs.