The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, technically meets the minimum VRAM requirement of 24GB for the FLUX.1 Schnell model when running in FP16 precision. However, this leaves virtually no headroom for the operating system, other processes, or even slight variations in model size after loading. The 3090's memory bandwidth of 0.94 TB/s is substantial, but the limited VRAM headroom will likely be the primary performance bottleneck. The Ampere architecture and its 10496 CUDA cores and 328 Tensor Cores will contribute to processing speed, but the model's performance will be significantly constrained by memory swapping if VRAM usage exceeds the available 24GB.
Given the marginal VRAM situation, running FLUX.1 Schnell on the RTX 3090 will likely require careful optimization. Start by closing all unnecessary applications to free up as much VRAM as possible. Consider using a framework like `llama.cpp` that supports aggressive quantization techniques (e.g., Q4_K_M or even lower) to reduce the model's memory footprint. If performance is still unsatisfactory, explore alternatives such as offloading some layers to system RAM (which will dramatically slow down inference) or using a different model with a smaller parameter count. If possible, consider upgrading to a GPU with more VRAM for a smoother experience.