The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, technically meets the minimum VRAM requirement of 24GB for running the FLUX.1 Schnell diffusion model in FP16 precision. However, this compatibility is marginal due to the complete lack of VRAM headroom. The RTX 3090 Ti's memory bandwidth of 1.01 TB/s is substantial, but with all VRAM utilized, performance bottlenecks are likely. The Ampere architecture, with its 10752 CUDA cores and 336 Tensor cores, should provide adequate compute capability for the model's operations, but the VRAM limitation will significantly impact achievable throughput. The model's context length of 77 tokens is relatively short, which may alleviate some memory pressure, but it also limits the model's ability to generate coherent longer outputs.
Given the marginal VRAM headroom, achieving acceptable performance with FLUX.1 Schnell on the RTX 3090 Ti will require careful optimization. Start by using a memory-efficient inference framework such as `text-generation-inference` which is designed to minimize memory footprint. Explore quantization techniques like Q4_K_S or Q5_K_M to reduce the model's memory footprint, potentially freeing up VRAM for larger batch sizes or longer context lengths. If performance remains unsatisfactory, consider splitting the model across multiple GPUs, if possible, or exploring alternative diffusion models with smaller parameter sizes. You should also monitor GPU utilization and VRAM usage closely to identify potential bottlenecks.