The primary limiting factor in running FLUX.1 Schnell (12B parameters) on an NVIDIA RTX 3080 10GB is the insufficient VRAM. FLUX.1 Schnell, a diffusion model, requires approximately 24GB of VRAM when operating in FP16 (half-precision floating point). The RTX 3080 only provides 10GB. This 14GB VRAM shortfall means the entire model and its intermediate computations cannot be loaded onto the GPU simultaneously. Consequently, the system will either refuse to load the model, or it will attempt to offload data to system RAM, leading to a dramatic performance decrease due to the slower transfer speeds between system RAM and the GPU.
While the RTX 3080's memory bandwidth of 0.76 TB/s and its 8704 CUDA cores offer substantial computational power, they become irrelevant when the model cannot reside entirely in VRAM. The Ampere architecture and its 272 Tensor Cores are designed to accelerate deep learning tasks, but again, this potential is bottlenecked by the limited VRAM. Without sufficient VRAM, the model will be forced to rely on system memory, significantly reducing the speed of inference. Therefore, even though the RTX 3080 is a powerful GPU, it's simply not suitable for running FLUX.1 Schnell in its standard FP16 configuration.
Due to the significant VRAM deficit, directly running FLUX.1 Schnell on the RTX 3080 10GB is not feasible without substantial modifications. One potential workaround involves aggressive quantization techniques to reduce the model's memory footprint. Consider using 4-bit quantization (QLORA or similar techniques) which might compress the model enough to fit within the 10GB VRAM, though with a potential trade-off in output quality. Alternatively, explore distributed inference solutions that split the model across multiple GPUs or utilize cloud-based GPU instances with sufficient VRAM.
Another option is to use CPU-based inference, though the performance will be significantly slower compared to GPU acceleration. If you must use the RTX 3080, prioritize inference frameworks that support offloading layers to system RAM (with significant performance degradation) and experiment with different batch sizes and context lengths to minimize memory usage. However, for practical and reasonable performance, consider using a GPU with at least 24GB of VRAM or exploring smaller diffusion models that fit within the RTX 3080's memory capacity.