H100 SXM vs A100 80GB
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and A100 80GB
Difference Analysis
Full Specifications
| Specification | H100 SXM | A100 80GB | AMD Instinct MI250 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | - | Data Center |
| Architecture | Hopper | - | CDNA 2 |
| VRAM | 80GB | 80GB | 128GB |
| VRAM Type | HBM3 | - | HBM2E |
| Memory Bandwidth | 3.4 TB/s | - | 3.3 TB/s |
| FP16 TFLOPS | 134.0 | - | 362.0 |
| Tensor TFLOPS | 2.0k | - | 724.0 |
| TDP | 700W | - | 500W |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | $1.39/hr | - |
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H100 SXM vs A100 80GB FAQ
It depends on your use case. The H100 SXM offers 0% better performance (2.0k vs - TFLOPS). For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM 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 SXM 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.
Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.
Upgrading to H100 SXM would give you 0% more performance and 0% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.