The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, technically meets the minimum VRAM requirement of 24GB for the FLUX.1 Dev diffusion model (12B parameters) when using FP16 precision. However, this compatibility is marginal. The absence of VRAM headroom means that even minor increases in context length or batch size, or the presence of other processes utilizing the GPU, could easily lead to out-of-memory errors. The RTX 3090's 0.94 TB/s memory bandwidth will be a significant factor in the model's performance, influencing the speed at which data can be transferred between the GPU and its memory. The estimated 28 tokens/sec indicates a relatively constrained performance, primarily due to memory limitations and the model's size relative to the available VRAM.
Given the marginal compatibility, careful optimization is crucial. Start by using the lowest practical context length and a batch size of 1 to minimize VRAM usage. Employ quantization techniques such as Q4_K_M or even lower precision if supported to reduce the model's memory footprint, freeing up some VRAM headroom. If possible, offload some layers to CPU to further reduce VRAM usage, though this will impact inference speed. If performance remains unsatisfactory or you encounter frequent out-of-memory errors, consider using a more powerful GPU with more VRAM or exploring distributed inference across multiple GPUs.