The primary limiting factor in running large language models (LLMs) like LLaVA 1.6 13B is the available VRAM on the GPU. LLaVA 1.6 13B, when using FP16 (half-precision floating point) data type, requires approximately 26GB of VRAM to load the model weights and perform inference. The NVIDIA RTX 3060, while a capable card, only provides 12GB of VRAM. This significant deficit of 14GB means the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors and preventing successful inference. The RTX 3060's memory bandwidth of 0.36 TB/s is also a factor, but less critical than the VRAM limitation in this case. Even if the model could somehow fit, lower bandwidth would translate to slower data transfer rates between the GPU and its memory, impacting performance. CUDA cores and Tensor cores contribute to computational throughput, but they are irrelevant if the model can't fit in memory.
Due to the VRAM limitations, running LLaVA 1.6 13B directly on an RTX 3060 12GB is not feasible without significant compromises. Consider using quantization techniques such as 4-bit or 8-bit quantization (using libraries like bitsandbytes with `llama.cpp` or `transformers`) to drastically reduce the VRAM footprint. Alternatively, explore offloading some model layers to system RAM, though this will severely impact inference speed. As a last resort, consider using cloud-based GPU services or upgrading to a GPU with more VRAM (e.g., RTX 3090, RTX 4080, or newer).