H100 vs B200 GPU Cost Efficiency Comparison for AI Workloads

The AI infrastructure landscape is shifting rapidly, and compute remains the largest line item for machine learning teams. When evaluating hardware for training and inference, engineering leaders frequently fall into the trap of comparing hourly rental rates. This metric completely ignores the realities of distributed training, memory bottlenecks, and actual time-to-market. The transition from NVIDIA's Hopper architecture to the new Blackwell generation represents a fundamental change in how we calculate the economics of AI compute. This comparison breaks down the technical specifications, memory bandwidth advantages, and real-world cost efficiency of the H100 versus the B200, providing a clear framework for optimizing your infrastructure investments.