H100 vs H200: Should You Upgrade?
What's the difference between H100 and H200?
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.
Key Data Points
- GPU Memory: 141GB HBM3e vs 80GB HBM3 (+76%)
- Memory Bandwidth: 4.8 TB/s vs 3.35 TB/s (+43%)
- FP8 Performance: Identical (3,958 TFLOPS)
- Lease Rates: $6.00-$8.00/hr vs $2.50-$3.50/hr
- Best For: 70B+ model inference and large context windows
Head-to-Head Specifications
| 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 | - |
When to Upgrade to H200
Upgrade to H200 If:
- •Running inference on 70B+ parameter models
- •Memory-bound workloads (large context windows)
- •Need to serve Llama 70B/405B without tensor parallelism
- •Latency-sensitive inference where batch size matters
- •Budget allows 2-3x higher compute costs
Stay on H100 If:
- •Training workloads (compute-bound, not memory-bound)
- •Running 7B-13B models that fit in 80GB
- •Cost optimization is priority over latency
- •Can use tensor parallelism across multiple H100s
- •Waiting for B200 availability (skip H200 generation)
Frequently Asked Questions
Is H200 just an H100 with more memory?
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.
When will H200 prices drop?
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.
Should I wait for B200 instead of buying H200?
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.
Can I run Llama 405B on H200?
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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