The primary limiting factor for running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 3060 Ti is the VRAM. FLUX.1 Dev, when using FP16 precision, requires approximately 24GB of VRAM to load the model and perform inference. The RTX 3060 Ti is equipped with only 8GB of VRAM. This 16GB deficit means the model cannot be loaded directly onto the GPU for processing. Memory bandwidth, while important, becomes a secondary concern when the model's size exceeds available VRAM, as offloading to system RAM significantly degrades performance. The Ampere architecture and the presence of Tensor Cores would normally be beneficial for accelerating computations; however, the VRAM constraint prevents these features from being effectively utilized.
Without sufficient VRAM, the system would rely on swapping data between the GPU and system RAM, resulting in extremely slow inference speeds. The 450 GB/s memory bandwidth of the RTX 3060 Ti becomes largely irrelevant as the bottleneck shifts to the significantly slower system RAM. Consequently, achieving usable tokens/second or determining an optimal batch size becomes impossible. The limited context length of 77 tokens specified in the model details further exacerbates the issue, suggesting a model architecture that may not be optimized for memory efficiency.
Given the significant VRAM shortfall, directly running FLUX.1 Dev on the RTX 3060 Ti is not feasible without substantial modifications. Consider exploring quantization techniques, such as 4-bit or even 2-bit quantization, to significantly reduce the model's memory footprint. Using `llama.cpp` with appropriate quantization settings might allow you to load a reduced version of the model. Alternatively, explore cloud-based inference services or invest in a GPU with at least 24GB of VRAM. If quantization is insufficient, consider using CPU-based inference as a last resort, understanding that performance will be significantly lower than GPU-accelerated inference.
If experimenting with quantization, start with Q4_K_M or similar methods in `llama.cpp`. Monitor VRAM usage closely and adjust quantization levels as needed. Be aware that extreme quantization can impact model accuracy. If you choose to use CPU inference, ensure you have sufficient system RAM (32GB or more) and a modern CPU with a high core count to mitigate performance limitations.