The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the memory requirements for running LLaVA 1.6 34B in FP16 precision. LLaVA 1.6 34B, with its 34 billion parameters, necessitates approximately 68GB of VRAM when using FP16 (half-precision floating point). This is because, in FP16, each parameter requires 2 bytes of storage (34B parameters * 2 bytes/parameter = 68GB). The 4070 Ti's memory bandwidth of 0.5 TB/s, while substantial, is irrelevant in this scenario as the model cannot even be loaded onto the GPU. The lack of sufficient VRAM means the model will not be able to perform inference, leading to out-of-memory errors and preventing any meaningful computation.
Due to the substantial VRAM deficit, directly running LLaVA 1.6 34B on an RTX 4070 Ti is not feasible without significant modifications. Consider using quantization techniques like 4-bit or even 3-bit quantization to drastically reduce the model's memory footprint. Frameworks like llama.cpp are optimized for running large language models with quantization. Alternatively, explore cloud-based inference services or platforms that offer GPUs with sufficient VRAM. If local execution is a must, consider using a different model with a smaller parameter count that fits within the 12GB VRAM limit, or explore distributed inference across multiple GPUs, though this introduces significant complexity.