The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls short of the 24GB VRAM requirement of the FLUX.1 Dev model when using FP16 precision. This discrepancy means that the model, in its default configuration, cannot be loaded and run on the RTX 3080 Ti without encountering out-of-memory (OOM) errors. The 3080 Ti's memory bandwidth of 0.91 TB/s is substantial, but it doesn't compensate for the insufficient VRAM. While the Ampere architecture and its 10240 CUDA cores and 320 Tensor cores would contribute to reasonable processing speed if the model fit into memory, the VRAM limitation is the primary bottleneck. The context length of 77 tokens is relatively small, but it doesn't significantly alleviate the VRAM pressure in this case.
To run FLUX.1 Dev on an RTX 3080 Ti, you'll need to employ aggressive quantization techniques. Consider using a framework like `llama.cpp` or `text-generation-inference` to load and run the model with lower precision, such as 4-bit quantization (Q4). This will significantly reduce the VRAM footprint, potentially bringing it within the 12GB limit. Be aware that quantization will likely impact the model's output quality and inference speed. Experiment with different quantization levels to find a balance between VRAM usage and performance. Another strategy is to offload some layers to system RAM, but this will drastically slow down inference.