The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, technically meets the minimum VRAM requirement for the FLUX.1 Dev model (12B parameters) when using FP16 precision. However, this compatibility is marginal, leaving virtually no VRAM headroom. This means that any other processes running on the GPU, or slight increases in model size during operation, could easily lead to out-of-memory errors. The RTX 3090 Ti's memory bandwidth of 1.01 TB/s is substantial and should allow for reasonable data transfer speeds, but the lack of VRAM headroom will be the primary bottleneck.
Given the 12B parameter size of FLUX.1 Dev and the 24GB VRAM, the estimated tokens per second is approximately 28. This performance is constrained by the full VRAM utilization. While the RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores are powerful, their potential is limited by the available memory. Running the model in FP16 without any VRAM headroom is a risky proposition, and optimizations or alternative precision settings will likely be necessary for stable operation.
Due to the extremely tight VRAM situation, running FLUX.1 Dev on the RTX 3090 Ti at FP16 is not recommended for sustained use. Begin by exploring quantization techniques, such as Q4_K_M or even lower, to significantly reduce the model's memory footprint. If quantization is not sufficient, consider alternative models with smaller parameter counts that can comfortably fit within the 24GB VRAM. Monitor VRAM usage closely during operation and be prepared to adjust settings to prevent crashes.
For improved performance and stability, consider using an inference framework like `text-generation-inference` which offers advanced optimization techniques, including quantization and memory management. Experiment with different quantization levels to find a balance between performance and accuracy. If possible, offloading some layers to system RAM might be necessary, but this will drastically reduce inference speed.