The primary bottleneck in running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 4060 Ti 8GB is the insufficient VRAM. FLUX.1 Dev, a diffusion model, requires approximately 24GB of VRAM when using FP16 (half-precision floating point). The RTX 4060 Ti only provides 8GB, resulting in a significant deficit of 16GB. This VRAM shortage will prevent the model from loading and running effectively, leading to out-of-memory errors. While the RTX 4060 Ti's Ada Lovelace architecture offers benefits such as Tensor Cores for accelerated AI computations, these advantages are negated by the inability to load the entire model into memory.
Furthermore, even if aggressive quantization techniques were applied to reduce the model's memory footprint, the 8GB VRAM limitation would still pose a considerable challenge. The memory bandwidth of 0.29 TB/s on the RTX 4060 Ti, while adequate for many tasks, will likely become a secondary bottleneck if the model could somehow be squeezed into the available VRAM. The limited context length of 77 tokens specified for the model is not a limiting factor, as diffusion models do not typically operate with long context windows like Large Language Models (LLMs).
Due to the severe VRAM limitation, directly running the FLUX.1 Dev model on the RTX 4060 Ti 8GB is not feasible. Consider using cloud-based services or renting a GPU with sufficient VRAM (at least 24GB) to run the model without modification. Alternatively, explore model distillation techniques to create a smaller, less demanding version of FLUX.1 Dev that can fit within the 8GB VRAM. This approach involves training a smaller "student" model to mimic the behavior of the larger "teacher" model. If distillation isn't an option, focus on running smaller diffusion models that are designed to operate within the constraints of your hardware.
Another approach is to investigate offloading layers to system RAM, but this will significantly impact performance and is generally not recommended for demanding models like FLUX.1 Dev. If you are determined to run FLUX.1 Dev locally, consider upgrading to a GPU with significantly more VRAM, such as an RTX 3090 or an RTX 4090, or professional GPUs like the A4000 or A5000.