The primary limiting factor in running large language models (LLMs) like Llama 3.1 405B is VRAM capacity. This model, even when quantized to q3_k_m, requires 162GB of VRAM to load and operate. The NVIDIA RTX 3090, while a powerful card, only offers 24GB of VRAM. This creates a significant shortfall of 138GB, meaning the model cannot be loaded onto the GPU in its entirety. Memory bandwidth, while important for performance, is secondary to the fundamental requirement of fitting the model within the available VRAM. The 3090's 0.94 TB/s bandwidth would be sufficient if the model *could* fit. Because the VRAM requirement is not met, the model will not run, and performance metrics like tokens/sec and batch size are not applicable.
Given the VRAM limitations, running Llama 3.1 405B on a single RTX 3090 is not feasible. Several options exist. First, consider using a smaller model variant that fits within your 24GB of VRAM. Second, explore using cloud-based GPU instances with sufficient VRAM. Third, investigate model parallelism, which involves splitting the model across multiple GPUs, but this requires significant technical expertise and compatible software frameworks. Finally, consider offloading some layers to system RAM, but this will drastically reduce inference speed.