The NVIDIA RTX 3070, with its 8GB of GDDR6 VRAM, falls short of the 14GB VRAM requirement for running LLaVA 1.6 7B in FP16 (half-precision floating point). This means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors. While the RTX 3070's memory bandwidth of 0.45 TB/s is respectable, it becomes irrelevant if the model cannot fit in VRAM. The 5888 CUDA cores and 184 Tensor cores would contribute to decent processing speed if the model could be loaded, but the VRAM bottleneck is the primary limitation. The Ampere architecture is generally well-suited for AI tasks, but the limited VRAM constrains its capabilities with larger models like LLaVA 1.6 7B.
Even with optimizations, the 6GB VRAM deficit is substantial. Techniques like offloading layers to system RAM (CPU) would severely degrade performance due to the much slower transfer speeds between system RAM and the GPU. This would result in extremely slow token generation, making interactive use impractical. Furthermore, the lack of sufficient VRAM also prevents the use of larger batch sizes, which could have otherwise improved throughput, even with slower individual token generation speeds.
Given the VRAM limitations, directly running LLaVA 1.6 7B on an RTX 3070 is not feasible without significant compromises. Consider using a smaller model that fits within the 8GB VRAM, or explore cloud-based inference services that offer GPUs with more VRAM. Alternatively, you can try extreme quantization techniques, such as 4-bit quantization, which can significantly reduce the VRAM footprint, but this will come at the cost of reduced model accuracy and potential instability. If you choose to proceed with quantization, thoroughly test the model to ensure the reduced precision doesn't negatively impact the quality of the generated outputs.