The NVIDIA RTX 4060 Ti 8GB falls short of the VRAM requirements for the FLUX.1 Schnell diffusion model. FLUX.1 Schnell, with its 12 billion parameters, necessitates 24GB of VRAM when using FP16 (half-precision floating point) for inference. The RTX 4060 Ti only provides 8GB, resulting in a significant 16GB VRAM deficit. This discrepancy means the entire model cannot reside on the GPU's memory simultaneously, leading to out-of-memory errors or severely degraded performance due to constant data swapping between system RAM and the GPU. Memory bandwidth, while decent at 0.29 TB/s for the 4060 Ti, becomes a bottleneck in such scenarios as data transfer becomes the limiting factor.
Furthermore, the context length of 77 tokens is relatively small for modern diffusion models. While this reduces the memory footprint slightly, it doesn't compensate for the massive VRAM requirement of the model itself. The 4352 CUDA cores and 136 Tensor cores of the RTX 4060 Ti are capable processing units, but their potential is unrealized when the model exceeds available VRAM. The Ada Lovelace architecture offers some performance benefits, but these are overshadowed by the memory limitations. Consequently, running FLUX.1 Schnell on this GPU without significant modifications is not feasible.
Due to the substantial VRAM shortfall, directly running FLUX.1 Schnell on the RTX 4060 Ti 8GB is impractical. Consider exploring alternative diffusion models with smaller parameter counts and lower VRAM requirements that better align with your hardware capabilities. If using FLUX.1 Schnell is essential, investigate quantization techniques, such as 4-bit or even 2-bit quantization, to drastically reduce the model's memory footprint. However, be aware that extreme quantization can impact output quality. Cloud-based inference or using a system with a GPU that meets the 24GB VRAM requirement are other viable options.
If you are set on using this GPU, you can experiment with CPU offloading, but this will significantly slow down the model. Be sure to monitor VRAM usage closely using tools like `nvidia-smi` to understand the memory allocation and identify any potential bottlenecks. Experiment with smaller batch sizes and context lengths if possible, though the context length is already quite small. You should also consider a different diffusion model, as this is not the ideal configuration.