Which H100 Instance to Train Nanochat – Benchmarking PCIe, SXM, and NVL (bluenotebook.io)

A recent analysis benchmarked the performance of NVIDIA's H100 GPU instances—PCIe, SXM, and NVL—while training the Nanochat AI model, revealing significant differences in training efficiency and cost-effectiveness. The study found that while SXM instances are the most expensive per hour, they provide the best performance, completing training in approximately 3 hours for about $37, making them 2x cheaper than PCIe and 3x cheaper than NVL. The benchmarks highlighted the importance of network interconnects in multi-GPU training, as SXM utilizes NVSwitch for superior bandwidth, allowing more efficient data transfer compared to PCIe and NVL configurations. This performance assessment is crucial for the AI/ML community as it underscores the potential for cost savings and speed when selecting the right GPU architecture for large language model training. The findings, based on rigorous profiling of network latency and execution times, also showed that the SXM variant achieves a remarkable 7.3x reduction in NCCL kernel time compared to PCIe. Such insights inform developers on hardware choices, optimizing training runs, and managing operational costs, which is vital for scaling AI applications in a rapidly evolving sector.