NVIDIA B200 vs H200 GPU for Inference: A Deep Dive

As large language models scale in complexity, infrastructure teams face a critical challenge: compute is a major COGS, yet average GPU cluster utilization hovers around a dismal 40 percent. Overprovisioning leads to wasted budget, while underprovisioning triggers out-of-memory errors and severe latency spikes. For teams deploying production inference, the hardware selection between NVIDIA's Hopper-based H200 and the new Blackwell-based B200 dictates both performance and profitability. This guide provides a rigorous, engineer-to-engineer comparison of the B200 vs H200 GPU for inference, examining memory bandwidth, token throughput, and how workload-aware orchestration can eliminate hardware guesswork.