The NVIDIA RTX 3080 12GB, while a powerful GPU, falls short of the VRAM requirements for the FLUX.1 Schnell model. FLUX.1 Schnell, with its 12 billion parameters, demands 24GB of VRAM when running in FP16 (half-precision floating point). The RTX 3080 12GB only provides 12GB of VRAM, leaving a deficit of 12GB. This discrepancy means the entire model cannot be loaded onto the GPU simultaneously, leading to inevitable out-of-memory (OOM) errors.
Furthermore, even if some clever memory management techniques were employed (like offloading layers to system RAM), the performance would be severely impacted. The RTX 3080's memory bandwidth of 0.91 TB/s, while substantial, would be a bottleneck when constantly transferring data between system RAM and the GPU. The Ampere architecture's 8960 CUDA cores and 280 Tensor cores would be underutilized due to the memory constraints. Consequently, the token generation rate would be significantly lower compared to a GPU with sufficient VRAM, rendering the model impractical for real-time or interactive applications.
Unfortunately, running FLUX.1 Schnell in FP16 on an RTX 3080 12GB is not feasible without significant compromises. The primary limitation is the insufficient VRAM. To use this model, consider exploring quantization techniques like Q4 or Q5 which significantly reduce the model's memory footprint, potentially bringing it within the RTX 3080's VRAM capacity. Alternatively, you could explore cloud-based GPU solutions or renting time on a machine equipped with a GPU that has at least 24GB of VRAM, such as an RTX 3090, RTX 4080/4090, or an A40/A100.