The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM, falls short of the 14GB VRAM requirement for running LLaVA 1.6 7B in FP16 (half-precision). This VRAM deficit means the entire model and its working memory cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors and preventing successful inference. While the RTX 3070 Ti boasts a memory bandwidth of 0.61 TB/s and 6144 CUDA cores, these specifications become irrelevant if the model cannot fit within the available VRAM. The Ampere architecture and the presence of 192 Tensor Cores would otherwise contribute to faster matrix multiplications and improved performance, but are bottlenecked by the insufficient memory capacity.
Even if techniques like CPU offloading were attempted, the performance would be severely degraded due to the slow transfer speeds between system RAM and the GPU. The model's context length of 4096 tokens further exacerbates the VRAM demand. Therefore, directly running LLaVA 1.6 7B on an RTX 3070 Ti without significant modifications is not feasible. The expected tokens per second would be negligibly low, and batch processing would be practically impossible due to the memory constraints.
To run LLaVA 1.6 7B on an RTX 3070 Ti, you will need to implement aggressive quantization techniques. Consider using 4-bit quantization (Q4_K_M or similar) via llama.cpp or similar frameworks, which can significantly reduce the VRAM footprint of the model. Alternatively, explore using CPU offloading, although this will drastically reduce inference speed. If neither of these options provides acceptable performance, consider using a cloud-based inference service or upgrading to a GPU with more VRAM (16GB or more). Experiment with different quantization methods to find a balance between VRAM usage and output quality.