The NVIDIA RTX 4080 SUPER, with its 16GB of GDDR6X VRAM, falls short of the 24GB VRAM required to run the FLUX.1 Schnell diffusion model in FP16 precision. This 8GB deficit means the model, in its native FP16 format, cannot be fully loaded onto the GPU's memory. Consequently, you'll encounter out-of-memory errors during inference. While the RTX 4080 SUPER boasts a memory bandwidth of 0.74 TB/s and 10240 CUDA cores, these specifications become secondary when the model exceeds available memory. The Ada Lovelace architecture's Tensor Cores would normally accelerate computations, but their potential is bottlenecked by the VRAM limitation.
Due to the VRAM constraint, directly running FLUX.1 Schnell on the RTX 4080 SUPER in FP16 is not feasible. The model's context length of 77 tokens is irrelevant in this scenario, as the primary issue is the inability to load the model itself. Performance metrics like tokens/second and batch size cannot be accurately estimated without addressing the VRAM shortfall. The model's 12 billion parameters necessitate significant memory allocation, and without sufficient VRAM, the RTX 4080 SUPER cannot effectively process the model's computational demands.
To run FLUX.1 Schnell on the RTX 4080 SUPER, you'll need to significantly reduce its memory footprint. The most effective approach is to use quantization techniques. Consider quantizing the model to INT8 or even INT4 precision using libraries like `bitsandbytes` or `AutoGPTQ`. This will drastically reduce the VRAM requirement, potentially bringing it within the 4080 SUPER's 16GB limit. Experiment with different quantization levels to find a balance between memory usage and output quality.
Alternatively, explore offloading some model layers to system RAM. However, this approach will severely impact performance due to the slower transfer speeds between system RAM and the GPU. If quantization proves insufficient or degrades output quality unacceptably, consider using a cloud-based GPU with more VRAM, or splitting the model across multiple GPUs using techniques like tensor parallelism (though this is more complex to set up).