The H200 offers 141GB HBM3e memory (vs 80GB HBM3 on H100) and 4.8 TB/s bandwidth (vs 3.35 TB/s). This 76% memory increase is critical for large batch inference and 70B+ parameter models. However, H200 supply remains constrained with 2-3x the lease cost of H100. Upgrade if you're memory-bound; stay on H100 if compute-bound.
| Specification | NVIDIA H100 SXM | NVIDIA H200 SXM | Improvement |
|---|---|---|---|
| GPU Memory | 80 GB HBM3 | 141 GB HBM3e | +76% |
| Memory Bandwidth | 3.35 TB/s | 4.8 TB/s | +43% |
| FP8 Performance | 3,958 TFLOPS | 3,958 TFLOPS | Same |
| TDP | 700W | 700W | Same |
| On-Demand Lease Rate | $2.50 - $3.50/hr | $6.00 - $8.00/hr | +2-3x |
| Availability | Good | Limited | - |
| Best For | Training, General Inference | Large Model Inference, 70B+ Models | - |
Essentially yes. The H200 uses the same Hopper architecture and CUDA cores as H100. The key upgrades are memory (141GB vs 80GB) and bandwidth (4.8 TB/s vs 3.35 TB/s). Compute performance is identical.
H200 pricing will likely stabilize when B200/B100 launches in volume (expected late 2026). Until then, supply constraints keep H200 at a 2-3x premium over H100. Consider reserved contracts for better rates.
If you can wait 12-18 months, B200 will offer better price/performance. B200 is expected to deliver 2x H100 training performance. However, if you need capacity now, H200 is the best available for memory-bound inference.
A single H200 (141GB) cannot fit Llama 405B (~400GB in FP16). You still need 3-4 H200s with tensor parallelism, vs 5-6 H100s. The memory increase reduces the number of GPUs needed by 40%.
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