H200 vs H100 NVL
Detailed comparison of specifications, performance, and pricing between NVIDIA H200 SXM and NVIDIA H100 NVL
Difference Analysis
Full Specifications
| Specification | H200 | H100 NVL | AMD Instinct MI250 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Hopper | CDNA 2 |
| VRAM | 141GB | 94GB | 128GB |
| VRAM Type | HBM3e | HBM3 | HBM2E |
| Memory Bandwidth | 4.8 TB/s | 3.9 TB/s | 3.3 TB/s |
| FP16 TFLOPS | 134.0 | 134.0 | 362.0 |
| Tensor TFLOPS | 2.0k | 2.0k | 724.0 |
| TDP | 700W | 400W | 500W |
| Form Factor | SXM | NVL | - |
| Hardware Price | $$38k | $$35k | - |
| Cloud Price (min) | $2.30/hr | $1.38/hr | - |
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H200 vs H100 NVL FAQ
It depends on your use case. The H200 offers 0% better performance (2.0k vs 2.0k TFLOPS). However, the H100 NVL is 9% cheaper. For raw performance, choose H200. For value, consider your budget and workload requirements.
The H200 has more VRAM with 141GB compared to 94GB (50% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H200 is generally better due to its larger VRAM (141GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 94GB, the cheaper option may be more cost-effective.
The H100 NVL is 9% cheaper at $$35k vs $$38k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H200 would give you 0% more performance and 50% more VRAM. The upgrade cost difference is approximately $$3.0k. Consider if your workloads are bottlenecked by current GPU capabilities.