The primary limiting factor for running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 3060 12GB is the available VRAM. FLUX.1 Dev, when using FP16 (half-precision floating point) for weights, requires approximately 24GB of VRAM. The RTX 3060 only provides 12GB, resulting in a 12GB deficit. This means the model's weights and intermediate activations cannot fully reside on the GPU's memory, leading to out-of-memory errors or forcing the system to rely on significantly slower system RAM, severely impacting performance.
While the RTX 3060's memory bandwidth of 0.36 TB/s is reasonable for many tasks, the limited VRAM becomes the bottleneck in this scenario. The 3584 CUDA cores and 112 Tensor Cores would be capable of accelerating computations if sufficient memory were available. Due to the architecture being Ampere, it supports various acceleration techniques such as Tensor Cores for mixed-precision operations, but these cannot be fully utilized with insufficient VRAM. The large parameter size of the FLUX.1 Dev model exacerbates the VRAM constraint.
Due to the significant VRAM shortfall, running FLUX.1 Dev on the RTX 3060 12GB in its native FP16 precision is not feasible. To potentially run the model, aggressive quantization techniques are necessary. Consider using 4-bit quantization (Q4) or even lower precisions if available in compatible inference frameworks. This reduces the memory footprint of the model, potentially bringing it within the RTX 3060's 12GB limit.
Alternatively, explore cloud-based solutions or consider upgrading to a GPU with significantly more VRAM (24GB or greater). Cloud platforms offer access to GPUs like the A100 or H100, which have the necessary memory to run FLUX.1 Dev without significant performance compromises. If local execution is a must, investigate distributed inference techniques to split the model across multiple GPUs, although this adds significant complexity.