The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM, falls significantly short of the 68GB VRAM required to load LLaVA 1.6 34B in FP16 (half-precision floating point). This memory shortfall prevents the model from being loaded onto the GPU for inference. While the RTX 3070 Ti boasts a memory bandwidth of 0.61 TB/s, 6144 CUDA cores, and 192 Tensor cores, these specifications become irrelevant when the model cannot fit within the GPU's memory. The Ampere architecture provides a solid foundation for AI tasks, but the limited VRAM is the primary bottleneck in this scenario. Attempting to run the model without sufficient VRAM will result in out-of-memory errors, preventing any meaningful computation.
Due to the substantial VRAM deficit, directly running LLaVA 1.6 34B on the RTX 3070 Ti is not feasible without significant modifications. Consider exploring quantization techniques, such as 4-bit or even lower precision, to drastically reduce the model's memory footprint. Tools like `llama.cpp` or `text-generation-inference` are excellent for this purpose. Alternatively, offloading layers to system RAM (CPU) might allow the model to run, but this will severely impact performance. As a last resort, consider using cloud-based GPU services that offer instances with sufficient VRAM or exploring smaller vision models that fit within the RTX 3070 Ti's memory capacity.