The primary bottleneck in running Llama 3.1 405B on a single NVIDIA H100 PCIe card is the insufficient VRAM. Llama 3.1 405B, in FP16 precision, requires approximately 810GB of VRAM to load the model weights and handle intermediate activations during inference. The NVIDIA H100 PCIe, with its 80GB of HBM2e memory, falls significantly short, resulting in a VRAM deficit of 730GB. This means the model cannot be loaded onto the GPU in its entirety, preventing any meaningful computation. While the H100's impressive 2.0 TB/s memory bandwidth and powerful Tensor Cores would otherwise contribute to fast inference, they are irrelevant in this scenario due to the VRAM limitation.
Even if techniques like activation offloading were employed, the sheer size disparity makes practical inference highly improbable. Activation offloading shifts some of the memory burden from the GPU's VRAM to system RAM, but the substantial data transfer between system RAM and GPU would introduce a significant performance penalty, likely rendering inference unacceptably slow. Furthermore, the 14592 CUDA cores and 456 Tensor Cores, while powerful, cannot compensate for the fundamental inability to load the model.
Given the severe VRAM limitation, running Llama 3.1 405B on a single H100 PCIe is not feasible without substantial model parallelism. Consider using a distributed inference setup with multiple H100 GPUs or exploring alternative models with smaller parameter counts that fit within the H100's VRAM. Model parallelism frameworks like DeepSpeed or PyTorch's FSDP would be necessary to shard the model across multiple GPUs.
Alternatively, consider using cloud-based inference services that offer access to larger GPU clusters or optimized inference endpoints for large language models. If running locally is a must, explore using smaller models that fit within the H100's memory or consider purchasing multiple H100s and implementing model parallelism.