The primary limiting factor when running large language models like Llama 3.1 405B is VRAM. This model, even when quantized to INT8, requires approximately 405GB of VRAM. The NVIDIA RTX 4090, while a powerful GPU, only provides 24GB of VRAM. This creates a significant shortfall of 381GB. The RTX 4090's memory bandwidth of 1.01 TB/s and its 16384 CUDA cores would be beneficial *if* the model could fit into VRAM. As it stands, the model cannot be loaded onto the GPU due to insufficient memory.
Even with aggressive quantization techniques and offloading strategies, running a 405B parameter model on a single RTX 4090 is not feasible. The model's size far exceeds the GPU's capacity. Memory bandwidth, while important for performance, becomes irrelevant when the model cannot be loaded in the first place. Without sufficient VRAM, performance metrics like tokens/sec and batch size are not applicable. The Ada Lovelace architecture's Tensor Cores would accelerate computations, but this advantage is negated by the VRAM limitation.
Given the VRAM limitations of the RTX 4090, running Llama 3.1 405B directly is not possible. Consider using cloud-based inference services like NelsaHost, which offer access to GPUs with significantly larger VRAM capacities, or distributed inference setups that split the model across multiple GPUs. Alternatively, explore smaller models that can fit within the RTX 4090's VRAM, such as quantized versions of Llama 3.1 8B or 70B. Further quantization to INT4 or even lower may allow a smaller model to run, but with a potential trade-off in accuracy.