H100 vs GB200 NVL72 Training Benchmarks – Power, TCO, and Reliability Analysis, Software Improvement Over Time

Summary

The article evaluates Nvidia's H100 and GB200 NVL72 training performance, comparing compute throughput, power draw, total cost of ownership, and operational reliability. It focuses on how Blackwell-based GB200 NVL72 systems perform against H100 clusters as AI model training scales, including effects from software maturity, interconnect efficiency, and datacenter power constraints. The analysis suggests that raw accelerator performance is only part of deployment economics: electricity, cooling, s

Why It Matters

• •AI infrastructure buyers increasingly need system-level economics, not just peak accelerator benchmarks.
• •Power consumption, cooling limits, and reliability can determine whether newer GPU platforms reduce real training costs.
• •Software maturity over time can materially change the effective performance of large-scale AI clusters.
• •Comparisons between Hopper and Blackwell systems inform procurement decisions for hyperscalers, cloud providers, and AI labs.