The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, provides a robust platform for running large language models like Llama 3.1 70B. In its INT8 quantized form, Llama 3.1 70B requires approximately 70GB of VRAM, leaving a comfortable 10GB headroom on the H100. This headroom is crucial for accommodating the operating system, other processes, and potential memory fragmentation, ensuring stable and efficient inference. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, is optimized for the matrix multiplications and other linear algebra operations that are fundamental to deep learning, further accelerating the model's execution.
Given the ample VRAM and powerful architecture of the H100, users should prioritize inference speed and throughput. Start with a batch size of 1 and experiment with increasing it to maximize GPU utilization, while monitoring for any performance degradation. Utilize inference frameworks optimized for NVIDIA GPUs, such as vLLM or NVIDIA's TensorRT, to leverage hardware acceleration and achieve the best possible performance. Also, explore techniques like speculative decoding if the framework supports it.