The primary limiting factor in running large language models like LLaVA 1.6 13B is VRAM (Video RAM). LLaVA 1.6 13B, when operating in FP16 (half-precision floating point), requires approximately 26GB of VRAM to load the model and perform inference. The NVIDIA RTX 3080 Ti, while a powerful GPU, is equipped with 12GB of GDDR6X VRAM. This significant shortfall of 14GB means the model cannot be loaded entirely onto the GPU, leading to a 'FAIL' compatibility verdict. The high memory bandwidth of the RTX 3080 Ti (0.91 TB/s) would otherwise contribute to fast tensor operations, but this potential is bottlenecked by the insufficient VRAM. Without sufficient VRAM, the system would likely attempt to offload parts of the model to system RAM, resulting in extremely slow performance due to the significantly lower bandwidth of system RAM compared to GDDR6X.
Given the VRAM limitations, running LLaVA 1.6 13B on the RTX 3080 Ti in its native FP16 format is not feasible. To make it work, you'll need to significantly reduce the model's memory footprint through quantization. Consider using 4-bit or 8-bit quantization techniques. Frameworks like `llama.cpp` and `vLLM` are well-suited for this. Furthermore, explore techniques like CPU offloading, but be aware that this will drastically reduce inference speed. If possible, consider using cloud-based GPU instances with more VRAM or splitting the model across multiple GPUs, if your setup allows.