The NVIDIA RTX 3060 12GB, with its Ampere architecture, provides a decent entry point for AI model experimentation. However, the FLUX.1 Schnell model, a diffusion model with 12 billion parameters, presents a significant challenge due to its substantial VRAM requirement. Specifically, running FLUX.1 Schnell in FP16 precision necessitates 24GB of VRAM. The RTX 3060's 12GB of GDDR6 VRAM falls significantly short of this requirement, resulting in a VRAM headroom deficit of 12GB. This discrepancy will prevent the model from loading and running without memory optimizations.
Furthermore, even if aggressive quantization techniques are applied to reduce the model's memory footprint, the RTX 3060's memory bandwidth of 0.36 TB/s could become a bottleneck, particularly during inference. While the 3584 CUDA cores and 112 Tensor cores offer reasonable computational power, the limited VRAM is the primary constraint. The context length of 77 tokens is relatively small and shouldn't pose an immediate problem, but it highlights the model's design for specific, potentially memory-intensive tasks. Without sufficient VRAM, estimating tokens per second or achievable batch sizes is impossible.
Due to the VRAM limitations, running FLUX.1 Schnell on an RTX 3060 12GB is impractical without significant concessions. Consider exploring quantization techniques like 4-bit or even lower precision to drastically reduce the model's VRAM footprint. Even with quantization, performance will likely be significantly degraded compared to running the model in FP16 on a GPU with sufficient VRAM.
Alternatively, explore cloud-based GPU solutions or consider upgrading to a GPU with at least 24GB of VRAM, such as an RTX 3090, RTX 4080, or similar, to run FLUX.1 Schnell effectively. If upgrading isn't feasible, look for smaller diffusion models with fewer parameters that can fit within the RTX 3060's VRAM capacity.