The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, falls short of the 14GB VRAM requirement for running LLaVA 1.6 7B in FP16 (half-precision floating point). This 2GB deficit means the model, in its default FP16 configuration, cannot be loaded and executed directly on the GPU without encountering out-of-memory errors. The RTX 4070 Ti's memory bandwidth of 0.5 TB/s is substantial, but insufficient VRAM is the primary bottleneck here. The 7680 CUDA cores and 240 Tensor cores would contribute to reasonable inference speed if the model fit within the available memory.
To run LLaVA 1.6 7B on your RTX 4070 Ti, you'll need to employ quantization techniques to reduce the model's memory footprint. Quantization lowers the precision of the model's weights, effectively compressing it. Consider using a 4-bit quantization method (e.g., Q4_K_M) via llama.cpp or similar frameworks. This will significantly reduce VRAM usage, likely bringing it within the 12GB limit. Alternatively, explore offloading some layers to system RAM, though this will negatively impact performance. If the above methods are not satisfactory, consider using a cloud-based inference service or upgrading to a GPU with more VRAM.