The NVIDIA RTX 3060 Ti, with its 8GB of GDDR6 VRAM, falls short of the 26GB VRAM requirement for running LLaVA 1.6 13B in FP16 precision. This discrepancy means the entire model and its intermediate computations during inference cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors. While the RTX 3060 Ti's Ampere architecture provides a decent number of CUDA and Tensor cores, the limiting factor here is definitively the insufficient VRAM. The memory bandwidth of 0.45 TB/s would be adequate if the model fit, but becomes irrelevant when the model cannot be fully loaded.
Without sufficient VRAM, the system would need to rely on offloading parts of the model to system RAM, which is significantly slower. This process introduces substantial latency due to the slower transfer speeds between system RAM and the GPU. Consequently, the expected tokens/second would be drastically reduced, making real-time or interactive applications impractical. Furthermore, batch size would likely be limited to 1 or even 0 (effectively serial processing) to minimize VRAM usage, further hindering performance.
Due to the VRAM limitation, directly running LLaVA 1.6 13B on the RTX 3060 Ti in FP16 is not feasible. However, you can explore quantization techniques to reduce the model's memory footprint. Quantization to 4-bit (Q4) or 8-bit (Q8) precision could potentially bring the model's VRAM requirement down to a manageable level. Alternatively, consider using cloud-based inference services or upgrading to a GPU with significantly more VRAM (at least 24GB) if local execution is a must. Distributed inference across multiple GPUs is another option, although it adds complexity to the setup.
If you proceed with quantization, experiment with different quantization methods and frameworks to find the best balance between VRAM usage and performance. Be aware that quantization may slightly reduce the model's accuracy, so testing and validation are crucial. Using a framework like `llama.cpp` is highly recommended due to its efficient memory management and quantization support.