H100 vs H200 GPUs: Which Nvidia Hopper is right for your AI workloads?

When scaling AI workloads, the choice of GPU significantly impacts training speed, cost, and model capabilities, with NVIDIA's H100 and H200 GPUs setting the benchmark for high-performance computing. The H200, an enhancement of the H100 based on the Hopper architecture, offers substantial upgrades in memory and bandwidth, making it ideal for larger, memory-intensive models. While both GPUs maintain the same architecture and tensor cores, the H200 nearly doubles the memory capacity and increases bandwidth to 4.8 TB/s, allowing for more efficient handling of large datasets and faster training times. It supports larger Multi-instance GPU (MIG) partitions and maintains compatibility with existing software stacks, ensuring smooth transitions without workflow disruptions. Benchmarks indicate the H200's superior performance, particularly in large-model inference workloads, despite it being more costly on platforms like Northflank, where it is priced at $3.14/hr compared to the H100's $2.74/hr. The choice between H100 and H200 largely depends on specific use cases, budget constraints, and the importance of efficiency at scale, with the H100 being suitable for budget-conscious deployments and the H200 for maximum performance.