The primary bottleneck in running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 4060 is the VRAM limitation. FLUX.1 Dev, in FP16 precision, requires approximately 24GB of VRAM to load the model and perform inference. The RTX 4060 is equipped with only 8GB of VRAM. This 16GB deficit means the model cannot be loaded entirely onto the GPU, leading to a compatibility failure. Memory bandwidth, while important, is secondary to the VRAM constraint in this scenario; the RTX 4060's 0.27 TB/s memory bandwidth would be sufficient if the model fit in VRAM. CUDA and Tensor core counts are also not the limiting factor here; they would contribute to processing speed if the model was loaded.
Due to the significant VRAM shortfall, running FLUX.1 Dev on the RTX 4060 in its native FP16 format is not feasible. To potentially run the model, you would need to explore aggressive quantization techniques. Consider using Q4_K_M or similar quantization methods available in llama.cpp or other inference frameworks. This drastically reduces the VRAM footprint, but comes at the cost of some accuracy. Offloading layers to system RAM is another option, but will severely impact performance. As an alternative, explore smaller diffusion models that fit within the 8GB VRAM of the RTX 4060, or consider upgrading to a GPU with significantly more VRAM (16GB or more is recommended for comfortable operation).