Short answer: the H200 is an H100 with more and faster memory — 141 GB HBM3e versus 80 GB HBM3, and roughly 1.4x the bandwidth. Same core architecture (Hopper), but the extra VRAM helps large models and long contexts most.
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What Actually Changed
The H100 and H200 share the Hopper architecture and compute engines. The H200's advantage is memory: 141 GB of HBM3e at about 4.8 TB/s versus the H100's 80 GB of HBM3 at about 3.35 TB/s. For memory-bound workloads — large-model inference and long context — that gap is the whole story.
H100 vs H200 at a Glance
| Spec | H100 (SXM) | H200 (SXM) |
|---|---|---|
| Architecture | Hopper | Hopper |
| Memory | 80 GB HBM3 | 141 GB HBM3e |
| Bandwidth | ~3.35 TB/s | ~4.8 TB/s |
| Compute | Same class | Same class |
| Best for | Cost-sensitive training and inference | Large models, long context, higher throughput |
When the H200 Is Worth It
When To Stay on H100
If your model fits comfortably in 80 GB and your workload is compute-bound, the H100 usually offers better price-performance — especially on the spot and secondary markets where availability is wide. Pay for the H200 when memory is the bottleneck, not by default.
Where To Rent
Both are available across GPU clouds at very different prices. Compare providers in RunPod pricing, Modal vs RunPod, and private AI hardware costs.