The NVIDIA H100 PCIe, while a powerful GPU, faces a significant limitation when running Llama 3.1 405B (405.00B) due to insufficient VRAM. Llama 3.1 405B (405.00B), even when quantized to INT8, requires 405GB of VRAM. The H100 PCIe only provides 80GB. This 325GB VRAM deficit prevents the model from loading and running effectively. The H100's impressive memory bandwidth of 2.0 TB/s and substantial compute power (14592 CUDA cores, 456 Tensor Cores) become irrelevant as the model cannot fit within the available memory.
Even with aggressive quantization techniques beyond INT8, such as INT4 or even lower, fitting the entire model and its working memory into the H100's 80GB VRAM remains highly improbable. Furthermore, even if a portion of the model could be loaded, the constant swapping of model layers between system RAM and the GPU's VRAM would introduce crippling latency, rendering inference speeds unacceptably slow. The large context length of 128000 tokens further exacerbates the memory requirements, compounding the incompatibility issue.
Unfortunately, running Llama 3.1 405B (405.00B) on a single NVIDIA H100 PCIe with 80GB VRAM is not feasible. Consider using multiple GPUs with NVLink to pool their VRAM, or explore cloud-based solutions that offer access to GPUs with sufficient memory capacity. Model parallelism, where the model is split across multiple GPUs, is crucial in this scenario.
Alternatively, consider using a smaller language model that fits within the H100's memory constraints. Fine-tuning a smaller model on a relevant dataset can often achieve comparable performance to a larger model with significantly reduced memory requirements. Another approach is to explore model distillation techniques, where a smaller model is trained to mimic the behavior of the larger Llama 3.1 405B (405.00B).