The NVIDIA H100 SXM, with its 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running large language models like Llama 3.1 70B. The model, when quantized to q3_k_m, requires approximately 28GB of VRAM, leaving a substantial 52GB headroom on the H100. This generous VRAM availability ensures that the entire model and necessary buffers can reside on the GPU, minimizing data transfer between the GPU and system memory, which can significantly slow down inference.
Furthermore, the H100's Hopper architecture boasts 16896 CUDA cores and 528 Tensor cores, providing ample computational resources for accelerating matrix multiplications and other operations crucial for LLM inference. The high memory bandwidth of 3.35 TB/s ensures that data can be fed to these cores quickly, preventing bottlenecks. The estimated tokens/sec of 63 indicates a reasonable inference speed, while a batch size of 3 allows for processing multiple requests simultaneously, improving overall throughput.
Given the H100's capabilities, users should leverage inference frameworks optimized for NVIDIA GPUs, such as vLLM or TensorRT, to maximize performance. Experimenting with different quantization levels might yield a further reduction in VRAM usage and potential speed improvements, although at the cost of accuracy. Monitor GPU utilization and temperature to ensure optimal operation within the H100's thermal design power (TDP) of 700W. Consider using techniques like speculative decoding or continuous batching to further improve throughput and reduce latency.