The NVIDIA RTX 4070 Ti SUPER, while a capable card with its Ada Lovelace architecture, 8448 CUDA cores, and 16GB of GDDR6X VRAM, falls short of the VRAM requirements for the FLUX.1 Dev model. FLUX.1 Dev, a 12 billion parameter diffusion model, requires 24GB of VRAM for FP16 (half-precision floating point) inference. The 8GB VRAM deficit means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors or requiring offloading parts of the model to system RAM, which significantly slows down performance. While the 4070 Ti SUPER's 670 GB/s memory bandwidth is substantial, it cannot compensate for the fundamental lack of sufficient on-card VRAM to hold the model in its entirety.
Furthermore, the context length of 77 tokens is relatively small for modern language models. The limited context window may restrict the model's ability to generate coherent and contextually relevant outputs, especially for tasks requiring long-range dependencies. The combination of insufficient VRAM and a short context length presents a significant challenge for effectively utilizing the FLUX.1 Dev model on the RTX 4070 Ti SUPER.
Due to the VRAM limitations, running FLUX.1 Dev on the RTX 4070 Ti SUPER without modifications is not feasible. Consider quantization techniques like Q4 or even lower to reduce the model's memory footprint. This will significantly degrade the quality of the model, but it might be the only way to run it. Alternatively, explore using cloud-based GPU instances with sufficient VRAM (e.g., AWS, Google Cloud, or Paperspace). If local execution is essential, consider splitting the model across multiple GPUs if possible, although this requires specialized software and setup.