The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM and Ampere architecture, is a powerful card, 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. This immense gap means the entire model cannot be loaded onto the GPU for inference. The RTX 3090's memory bandwidth of 0.94 TB/s is substantial, but irrelevant if the model cannot fit in VRAM. CUDA and Tensor cores will remain largely unused due to the memory constraint, rendering real-time inference impossible.
Even with aggressive quantization, the sheer size of the 405B parameter model presents a challenge. While the RTX 3090's architecture is capable, the VRAM limitation is a hard constraint. Techniques like offloading layers to system RAM could be attempted, but this would result in extremely slow inference speeds, rendering the model effectively unusable for practical applications. The high TDP of 350W is not a limiting factor in this scenario, as the GPU will likely not be fully utilized due to the VRAM bottleneck.
Given the VRAM limitations, running Llama 3.1 405B on a single RTX 3090 is not feasible. Consider using a smaller model that fits within the 24GB VRAM, such as a 7B or 13B parameter model. Alternatively, explore cloud-based solutions or services that offer access to GPUs with sufficient VRAM, or consider using multiple GPUs with NVLink to pool their memory resources, although this requires specialized software and hardware configurations.
If you are determined to run a large model locally, explore model parallelism techniques where the model is split across multiple GPUs. However, this approach requires significant expertise in distributed computing and deep learning frameworks. Another option is to use CPU-based inference, but this will be significantly slower than GPU inference, even with VRAM limitations.