The NVIDIA RTX 4070 Ti SUPER, while a powerful card, falls short of the VRAM requirements for the FLUX.1 Schnell diffusion model. FLUX.1 Schnell, with its 12 billion parameters, necessitates 24GB of VRAM when using FP16 (half-precision floating point) data types. The RTX 4070 Ti SUPER is equipped with 16GB of GDDR6X memory. This 8GB VRAM deficit means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors or reliance on significantly slower system RAM, severely impacting performance. Memory bandwidth, while substantial at 0.67 TB/s, is secondary to the primary limitation of insufficient VRAM in this scenario.
Due to the VRAM limitation, running FLUX.1 Schnell on the RTX 4070 Ti SUPER at FP16 precision is not feasible. Consider using quantization techniques, such as 8-bit integer (INT8) or even 4-bit integer (INT4) quantization, to reduce the model's memory footprint. Frameworks like `llama.cpp` or `text-generation-inference` are well-suited for quantized inference. If quantization is insufficient, explore alternative diffusion models with smaller parameter counts or consider upgrading to a GPU with at least 24GB of VRAM. Cloud-based inference services are also an option.