H100 vs A100 SXM
Detailed comparison of specifications, performance, and pricing between H100 and A100 SXM
Difference Analysis
Full Specifications
| Specification | H100 | A100 SXM | H100 NVL | B200 |
|---|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center | Data Center |
| Architecture | Hopper | Ampere | Hopper | Blackwell |
| VRAM | 80GB | 80GB | 94GB | 192GB |
| VRAM Type | HBM3 | HBM2e | HBM3 | HBM3e |
| Memory Bandwidth | 3.4 TB/s | 2.0 TB/s | 3.9 TB/s | 8.0 TB/s |
| FP16 TFLOPS | 134.0 | 78.0 | 134.0 | - |
| Tensor TFLOPS | 2.0k | 312.0 | 2.0k | 4.5k |
| TDP | 700W | 400W | 400W | 1000W |
| Form Factor | SXM | SXM | NVL | SXM |
| Hardware Price | $$32k | $$12k | $$35k | $$45k |
| Cloud Price (min) | $1.47/hr | $1.30/hr | $2.27/hr | $3.75/hr |
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H100 vs A100 SXM FAQ
It depends on your use case. The H100 offers 534% better performance (2.0k vs 312.0 TFLOPS). However, the A100 SXM is 167% cheaper. For raw performance, choose H100. For value, consider your budget and workload requirements.
The H100 has more VRAM with 80GB compared to 80GB (0% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 80GB, the cheaper option may be more cost-effective.
The A100 SXM is 167% cheaper at $$12k vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 would give you 534% more performance and 0% more VRAM. The upgrade cost difference is approximately $$20k. Consider if your workloads are bottlenecked by current GPU capabilities.
