The NVIDIA RTX 4060 Ti 16GB, while a capable mid-range GPU based on the Ada Lovelace architecture, falls short of the VRAM requirements for the FLUX.1 Dev diffusion model. FLUX.1 Dev, with its 12 billion parameters, necessitates approximately 24GB of VRAM when using FP16 (half-precision floating-point) for storing the model weights and intermediate activations during inference. The RTX 4060 Ti 16GB offers only 16GB of GDDR6 VRAM, resulting in an 8GB shortfall. This VRAM deficit will prevent the model from loading and running without significant modifications.
Beyond VRAM, the RTX 4060 Ti's memory bandwidth of 288 GB/s will also influence performance. While sufficient for many tasks, it could become a bottleneck when processing large diffusion models like FLUX.1 Dev, especially with larger batch sizes or longer context lengths. The 4352 CUDA cores and 136 Tensor cores will contribute to the computational throughput, but the limited VRAM is the primary constraint. Expect extremely slow or non-functional performance without employing aggressive optimization techniques.
Due to the significant VRAM deficit, running FLUX.1 Dev on the RTX 4060 Ti 16GB in its standard FP16 configuration is not feasible. To potentially make it work, you would need to explore aggressive quantization techniques. Consider using 4-bit or even 3-bit quantization (e.g., using bitsandbytes or GPTQ). This will drastically reduce the model's memory footprint. However, expect a significant reduction in output quality.
Alternatively, explore using CPU offloading or splitting the model across the GPU and system RAM. This will severely impact performance, making it impractical for real-time or interactive applications. If the model's output quality is paramount, consider using a cloud-based GPU service with sufficient VRAM (e.g., NVIDIA A100, H100) or upgrading to a GPU with at least 24GB of VRAM (e.g., RTX 3090, RTX 4080, RTX 4090).