The NVIDIA RTX 4080, with its 16GB of GDDR6X VRAM, falls short of the 24GB VRAM requirement for the FLUX.1 Dev model when using FP16 precision. This memory shortfall means the entire model cannot be loaded onto the GPU simultaneously. The RTX 4080's memory bandwidth of 0.72 TB/s is substantial, but insufficient VRAM is the primary bottleneck, not memory bandwidth. The Ada Lovelace architecture and 9728 CUDA cores would otherwise provide significant computational power for inference.
Without sufficient VRAM, the model will either fail to load or require offloading parts of the model to system RAM (CPU). Offloading to system RAM significantly slows down inference speed, making real-time or interactive applications impractical. While the RTX 4080's 304 Tensor Cores would accelerate FP16 operations if the model fit entirely in VRAM, the VRAM limitation negates this advantage.
To run FLUX.1 Dev on the RTX 4080, you'll need to employ aggressive quantization techniques. Consider using 8-bit integer quantization (INT8) or even 4-bit quantization (GPTQ or bitsandbytes) to significantly reduce the model's memory footprint. These techniques will reduce the model's VRAM requirements, potentially bringing it within the RTX 4080's 16GB limit. However, quantization can impact model accuracy, so it's crucial to evaluate the trade-off between memory usage and output quality.
Alternatively, explore using model parallelism across multiple GPUs if available, but this requires more advanced setup. If neither quantization nor model parallelism is feasible, consider using a GPU with at least 24GB of VRAM, such as an RTX 3090, RTX 4090, or a professional-grade NVIDIA A-series card. Cloud-based GPU instances are another option for accessing more powerful hardware.