The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, falls short of the 24GB VRAM requirement for running the FLUX.1 Schnell model in FP16 precision. This memory deficit means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors or reliance on slower system RAM. While the RTX 4070 Ti boasts a memory bandwidth of 0.5 TB/s and 7680 CUDA cores, these specifications are irrelevant if the model doesn't fit within the available VRAM. The Ada Lovelace architecture's Tensor Cores would typically accelerate the model's computations, but their potential is bottlenecked by the VRAM limitation. Memory bandwidth would become a factor if offloading parts of the model to system memory.
Due to the significant VRAM shortfall, running FLUX.1 Schnell on the RTX 4070 Ti at FP16 precision is not feasible. Consider using quantization techniques like Q4_K_M or even lower precisions, which can drastically reduce the model's memory footprint, potentially bringing it within the 12GB VRAM limit. Alternatively, explore using CPU offloading with frameworks like llama.cpp, but expect a substantial performance decrease. As a last resort, consider cloud-based solutions or upgrading to a GPU with more VRAM (e.g., RTX 3090, RTX 4080 with 16GB, or an RTX 4090).