The NVIDIA RTX 4070, equipped with 12GB of GDDR6X VRAM, falls short of the 14GB VRAM requirement for running LLaVA 1.6 7B in FP16 precision. This 2GB deficit means a direct, unoptimized execution will likely result in out-of-memory errors. While the RTX 4070's Ada Lovelace architecture, 5888 CUDA cores, and 0.5 TB/s memory bandwidth are substantial, they cannot compensate for the insufficient VRAM. The model's 7 billion parameters, combined with the FP16 precision, necessitate more memory than the GPU provides. Consequently, without employing specific optimization techniques, the model will not load and run successfully on this hardware.
To run LLaVA 1.6 7B on the RTX 4070, you must employ quantization techniques to reduce the model's memory footprint. Quantizing the model to 8-bit (INT8) or even 4-bit (INT4) precision can significantly decrease VRAM usage, potentially bringing it within the RTX 4070's 12GB limit. Consider using frameworks like llama.cpp or vLLM, which offer robust quantization support. Experiment with different quantization levels and monitor performance to find a balance between VRAM usage and output quality. If quantization proves insufficient, consider offloading some layers to system RAM, but be aware that this will substantially reduce inference speed.