The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls significantly short of the 810GB VRAM required to load the Llama 3.1 405B model in FP16 precision. This vast discrepancy means the entire model cannot reside on the GPU's memory simultaneously. While the RTX 3090's memory bandwidth of 0.94 TB/s is substantial, it's irrelevant in this scenario because the model's size necessitates offloading significant portions to system RAM or even disk, which are orders of magnitude slower. The RTX 3090's 10496 CUDA cores and 328 Tensor Cores would theoretically provide good compute performance *if* the model fit into memory, but they are bottlenecked by the memory limitations. The Ampere architecture is powerful, but memory capacity is the limiting factor here.
Even with aggressive quantization, such as INT4, the model would still require approximately 202.5GB of VRAM, far exceeding the RTX 3090's capacity. This implies that even with extreme optimization techniques like CPU offloading, the performance would be severely hampered by the constant data transfer between the GPU and system memory. The limited VRAM will lead to minimal batch sizes and severely restricted context lengths, resulting in extremely slow token generation speeds, likely making interactive use impractical. The high TDP of the RTX 3090 (350W) will be largely wasted as the GPU spends most of its time waiting for data.
Due to the immense VRAM requirements of the Llama 3.1 405B model, the RTX 3090 is not a suitable GPU for running it directly. Consider using cloud-based inference services that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100 instances. Alternatively, explore smaller Llama 3 models with fewer parameters that can fit within the RTX 3090's VRAM. Fine-tuning a smaller model on a relevant dataset might provide a more practical solution for your specific needs. Finally, if you absolutely must run the 405B model locally, investigate CPU-based inference or distributed inference across multiple GPUs, but be prepared for extremely slow performance.