Core Insights - The article discusses the cost comparison between H100 and GB200 NVL72 servers, highlighting that the total upfront capital cost for GB200 NVL72 is approximately 1.6 to 1.7 times that of H100 per GPU [2][3] - It emphasizes that the operational costs of GB200 NVL72 are not significantly higher than H100, primarily due to the higher power consumption of GB200 NVL72 [4][5] - The total cost of ownership (TCO) for GB200 NVL72 is about 1.6 times higher than that of H100, indicating that GB200 NVL72 needs to be at least 1.6 times faster than H100 to be competitive in terms of performance/TCO [4][5] Cost Analysis - The price of H100 servers has decreased to around $190,000, while the total capital cost for a typical hyperscaler server setup can reach $250,866 [2][3] - For GB200 NVL72, the upfront capital cost per server is approximately $3,916,824, which includes additional costs for networking, storage, and other components [3] - The capital cost per GPU for H100 is $31,358, while for GB200 NVL72, it is $54,400, reflecting a significant difference in initial investment [3] Operational Costs - The operational cost per GPU per month for H100 is $249, while for GB200 NVL72, it is $359, indicating a smaller margin in operational expenses [4][5] - The electricity cost remains constant at $0.0870 per kWh across both systems, with a utilization rate of 80% and a Power Usage Effectiveness (PUE) of 1.35 [4][5] Recommendations for Nvidia - The article suggests that Nvidia should enhance its benchmarking efforts and increase transparency to benefit the machine learning community [6][7] - It recommends expanding benchmarking beyond NeMo-MegatronLM to include native PyTorch, as many users prefer this framework [8][9] - Nvidia is advised to improve diagnostic and debugging tools for the GB200 NVL72 backplane to enhance reliability and performance [9][10] Benchmarking Insights - The performance of training models like GPT-3 175B using H100 has shown improvements in throughput and efficiency over time, with significant gains attributed to software optimizations [11][12] - The article highlights the importance of scaling in training large models, noting that weak scaling can lead to performance drops as the number of GPUs increases [15][17] - It provides detailed performance metrics for various configurations, illustrating the relationship between GPU count and training efficiency [18][21]