The NVIDIA RTX 3080 12GB, while a powerful card, falls short of the VRAM requirements for the FLUX.1 Dev model. FLUX.1 Dev, with its 12 billion parameters, demands 24GB of VRAM when running in FP16 (half-precision floating point). The RTX 3080 12GB only offers 12GB of VRAM, creating a significant 12GB deficit. This VRAM limitation means the entire model cannot be loaded onto the GPU, preventing successful inference without employing specific optimization techniques.
Beyond VRAM, the RTX 3080's memory bandwidth of 0.91 TB/s is substantial, but insufficient VRAM is the primary bottleneck here. Even with adequate memory bandwidth, the inability to load the full model into VRAM negates any potential performance gains. The Ampere architecture and its 8960 CUDA cores and 280 Tensor Cores would normally contribute to fast inference, but they cannot be fully utilized in this scenario. Without sufficient VRAM, the model would have to rely on system RAM, dramatically slowing down the process, or simply fail to run.
Due to the VRAM shortfall, running FLUX.1 Dev directly on the RTX 3080 12GB without modifications is not feasible. Consider using quantization techniques, such as converting the model to INT8 or even lower precision. This reduces the memory footprint, potentially bringing it within the RTX 3080's 12GB VRAM capacity. Alternatively, explore offloading some layers to the system RAM (CPU), but be aware that this will significantly degrade performance. Distributed inference across multiple GPUs is another option, but requires more complex setup and resources.
If quantization or offloading proves insufficient, consider using a smaller model with fewer parameters that fits within the 12GB VRAM limit. Fine-tuning a smaller model on a relevant dataset might offer a more practical solution for your specific needs. Cloud-based inference services offer another alternative, where you can leverage GPUs with larger VRAM capacities without needing to invest in new hardware.