The NVIDIA H100 PCIe, with its 80GB of HBM2e memory, offers substantial computational power for AI workloads. However, running Llama 3.1 405B, even in its Q4_K_M (4-bit) quantized form, presents a significant challenge. The quantized model still requires approximately 202.5GB of VRAM, far exceeding the H100's capacity. This VRAM deficit means the entire model cannot reside on the GPU, leading to inevitable out-of-memory errors and preventing successful inference. The H100's impressive 2.0 TB/s memory bandwidth would be beneficial *if* the model fit, enabling rapid data transfer between memory and the GPU's compute units (CUDA and Tensor Cores).
Unfortunately, running Llama 3.1 405B on a single NVIDIA H100 PCIe with 80GB VRAM is not feasible, even with aggressive quantization. The VRAM requirement simply exceeds the available resources. Consider using multiple GPUs with techniques like tensor parallelism or pipeline parallelism to distribute the model across devices. Alternatively, explore smaller language models that fit within the H100's VRAM, or utilize cloud-based solutions that offer access to larger GPU clusters. Another option is to explore extreme quantization methods, but this will likely result in significant accuracy degradation.