The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, falls significantly short of the VRAM requirements for running Llama 3.1 70B (70.00B) in FP16 precision, which demands 140GB. This discrepancy of 116GB means the entire model cannot be loaded onto the GPU simultaneously. The 3090 Ti's impressive memory bandwidth of 1.01 TB/s would be beneficial if the model fit, but it cannot compensate for the lack of memory capacity. The 10752 CUDA cores and 336 Tensor cores would contribute to faster computations, but are rendered useless without sufficient VRAM.
Due to the substantial VRAM deficit, running Llama 3.1 70B (70.00B) on a single RTX 3090 Ti is not feasible without significant compromises. Consider using quantization techniques, such as 4-bit or 8-bit quantization, which can drastically reduce the VRAM footprint of the model. Alternatively, explore distributed inference across multiple GPUs or offloading layers to system RAM, though this will significantly impact performance. As a last resort, consider using cloud-based GPU instances with adequate VRAM or smaller models that fit within the 3090 Ti's memory capacity.