The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM, faces a significant challenge when attempting to run Llama 3.1 405B. Even with Q4_K_M quantization, the model requires approximately 202.5GB of VRAM, vastly exceeding the GPU's capacity. This discrepancy means the entire model cannot reside on the GPU, leading to out-of-memory errors or necessitating offloading layers to system RAM, which severely impacts performance. The RTX 4090's memory bandwidth of 1.01 TB/s is excellent, but it cannot compensate for the sheer lack of on-device memory required to efficiently run such a large model. The 16384 CUDA cores and 512 Tensor cores would be underutilized due to the VRAM bottleneck.
Running Llama 3.1 405B on a single RTX 4090 is impractical due to the massive VRAM requirements. Consider using a multi-GPU setup with NVLink if feasible, although even that might struggle depending on the specific implementation and available bandwidth. Alternatively, explore cloud-based solutions that offer instances with sufficient GPU memory, such as those provided by NelsaHost. For local experimentation, focus on smaller models that fit within the RTX 4090's VRAM or leverage extreme quantization techniques, acknowledging a substantial reduction in model accuracy.