The primary limiting factor for running LLaVA 1.6 7B on an RTX 3060 Ti is the VRAM. LLaVA 1.6 7B, when operating in FP16 (half-precision floating point), requires approximately 14GB of VRAM to load the model and manage activations during inference. The RTX 3060 Ti is equipped with 8GB of GDDR6 VRAM. This creates a significant shortfall of 6GB, meaning the model, in its full FP16 precision, cannot be loaded onto the GPU. Attempting to run the model without addressing this VRAM limitation will result in out-of-memory errors, preventing successful inference. While the RTX 3060 Ti's Ampere architecture, CUDA cores, and Tensor cores are capable of accelerating the computations, the VRAM bottleneck is insurmountable without optimization.
Memory bandwidth, while important for performance, is secondary to the VRAM limitation in this case. The RTX 3060 Ti's 450 GB/s memory bandwidth is adequate for many AI workloads, but it cannot compensate for the inability to load the model in the first place. Even if the model could be squeezed into VRAM through aggressive quantization, the memory bandwidth would become a factor influencing the tokens/second generation rate. Performance would also be heavily influenced by the efficiency of the chosen inference framework and the degree of optimization applied.
To run LLaVA 1.6 7B on the RTX 3060 Ti, you must significantly reduce the model's memory footprint. The most effective method is quantization. Consider using 4-bit quantization (Q4_K_M or similar) which can drastically reduce VRAM usage, potentially bringing it within the 8GB limit. Frameworks like `llama.cpp` and `text-generation-inference` offer extensive quantization support. Be aware that quantization will reduce the model's accuracy and output quality to some degree, so experiment to find a balance between performance and quality.
Another option, although less practical for LLaVA due to its vision component, is offloading layers to system RAM. However, this will severely impact performance due to the much slower transfer speeds between system RAM and the GPU. Focus on quantization as the primary strategy. Also, close any unnecessary applications to free up as much VRAM as possible before attempting to load the model. Finally, using a smaller context length can slightly reduce VRAM usage.