The NVIDIA RTX 4070, with its 12GB of GDDR6X VRAM, falls short of the 24GB VRAM requirement for running the FLUX.1 Schnell model in FP16 precision. This VRAM deficit of 12GB means the model, in its current form, cannot be loaded entirely onto the GPU for inference. The Ada Lovelace architecture of the RTX 4070 offers benefits like Tensor Cores for accelerating certain operations, but these advantages are irrelevant if the model cannot fit in memory. Furthermore, the 500 GB/s memory bandwidth of the RTX 4070 would likely become a bottleneck if workarounds like offloading layers to system RAM are attempted, severely impacting performance. The 5888 CUDA cores and 184 Tensor cores would be underutilized due to the VRAM limitation.
To run FLUX.1 Schnell on an RTX 4070, you'll need to significantly reduce the model's memory footprint. Consider using quantization techniques like 4-bit or 8-bit quantization (e.g., using bitsandbytes or llama.cpp) which can drastically reduce VRAM usage. Another option is to explore CPU offloading, where some layers of the model are processed on the CPU. However, this will introduce significant performance overhead due to the slower data transfer between system RAM and GPU VRAM. If these optimizations are insufficient, consider using a GPU with more VRAM or exploring cloud-based inference solutions.