The NVIDIA RTX 4060 Ti 16GB, while a capable mid-range GPU based on the Ada Lovelace architecture, falls short when attempting to run the FLUX.1 Schnell diffusion model. The primary bottleneck is VRAM. FLUX.1 Schnell, with its 12 billion parameters, requires approximately 24GB of VRAM when running in FP16 (half-precision floating point). The RTX 4060 Ti 16GB only provides 16GB of VRAM, resulting in an 8GB deficit. This VRAM shortfall means the model and its intermediate computations cannot be fully loaded onto the GPU, leading to either a complete failure to load the model or extremely slow performance as data is constantly swapped between the GPU and system RAM, which is significantly slower. The RTX 4060 Ti's memory bandwidth of 290 GB/s, while decent, would further exacerbate performance issues if VRAM swapping occurred. The 4352 CUDA cores and 136 Tensor cores would be underutilized due to the VRAM limitation.
Unfortunately, running FLUX.1 Schnell on the RTX 4060 Ti 16GB in its full FP16 precision is not feasible. To attempt to run this model, you'll need to explore aggressive quantization techniques. Quantization reduces the memory footprint of the model by using lower precision data types (e.g., INT8 or even INT4). Experiment with quantization tools available in frameworks like PyTorch or TensorFlow, or use libraries like `llama.cpp` which are optimized for running large language models on consumer hardware. Even with aggressive quantization, performance may be significantly impacted and the resulting image quality may be degraded. Consider using cloud-based GPU services with higher VRAM capacity if high performance and image quality are paramount.