The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is a powerful GPU, but it falls significantly short of the VRAM requirements for running Llama 3.1 405B, even in its Q4_K_M (4-bit) quantized form. The quantized model requires approximately 202.5GB of VRAM, leaving a deficit of 178.5GB. While the 3090 Ti boasts a high memory bandwidth of 1.01 TB/s and a substantial number of CUDA and Tensor cores, the VRAM limitation is the primary bottleneck. This means the entire model cannot be loaded onto the GPU, preventing inference from occurring directly on the card without employing techniques to offload layers to system RAM or other GPUs.
Due to the substantial VRAM discrepancy, directly running Llama 3.1 405B on a single RTX 3090 Ti is not feasible. Consider exploring alternative strategies such as using a cluster of GPUs to distribute the model, or utilizing CPU offloading, which will significantly reduce inference speed. Alternatively, consider using smaller models, such as Llama 3 8B, or even quantized versions of Llama 2 models which can fit within the 3090 Ti's VRAM. Furthermore, cloud-based inference services or renting time on a more powerful GPU with sufficient VRAM are viable options for running the full 405B model.