The primary limiting factor for running the FLUX.1 Schnell model (12B parameters) on an NVIDIA RTX 3060 Ti is the VRAM. FLUX.1 Schnell, in FP16 precision, requires 24GB of VRAM to load the model weights and perform inference. The RTX 3060 Ti is equipped with only 8GB of VRAM. This means that the model, in its full FP16 precision, cannot fit entirely within the GPU's memory. Consequently, a direct attempt to load and run the model will result in an out-of-memory error. Memory bandwidth, although important for performance, is secondary to the initial constraint of fitting the model within the available VRAM.
Even with techniques like CPU offloading, the performance would be severely degraded. CPU offloading involves moving parts of the model or intermediate computations to system RAM, which is significantly slower than VRAM. This introduces substantial latency and reduces the throughput (tokens/second) to a point where interactive or real-time applications become impractical. Without substantial quantization or other memory-saving techniques, running FLUX.1 Schnell on an RTX 3060 Ti is not feasible.
To run FLUX.1 Schnell on an RTX 3060 Ti, you'll need to significantly reduce its memory footprint. The most effective approach is to apply aggressive quantization techniques. Experiment with 4-bit or even 3-bit quantization using libraries like `llama.cpp` or `AutoGPTQ`. These methods drastically reduce the VRAM requirements, potentially bringing the model within the 8GB limit. However, be aware that extreme quantization can impact the model's accuracy and generation quality.
Alternatively, consider using cloud-based GPU services that offer instances with sufficient VRAM (e.g., an NVIDIA A10, A100, or similar). This eliminates the hardware limitations and allows you to run the model without extensive optimization. If local execution is a must, explore smaller models with fewer parameters that fit within the RTX 3060 Ti's VRAM. Be prepared to trade off model size and capabilities for compatibility.