The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the 68GB VRAM required to run LLaVA 1.6 34B in FP16 precision. This massive discrepancy means the entire model and its intermediate computations cannot fit into the GPU's memory simultaneously, leading to a 'FAIL' verdict. While the RTX 3080 Ti boasts a respectable memory bandwidth of 0.91 TB/s and a substantial number of CUDA and Tensor cores, these specifications are rendered largely irrelevant when the primary constraint is VRAM capacity. Attempting to run the model without addressing the VRAM issue will likely result in out-of-memory errors, preventing successful inference.
To run LLaVA 1.6 34B on an RTX 3080 Ti, you must significantly reduce the model's memory footprint. The most practical approach is quantization. Experiment with 4-bit quantization using libraries like `llama.cpp` or `AutoGPTQ`. This can compress the model substantially, potentially bringing it within the 12GB VRAM limit, although performance will be degraded. Another strategy is to explore offloading layers to system RAM, but this will drastically reduce inference speed due to the slower transfer rates between system RAM and GPU VRAM. Consider using a framework like `vLLM` for optimized memory management and potentially better performance even with quantization.