H100 SXM vs A100 40GB
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA A100 40GB PCIe
Difference Analysis
Full Specifications
| Specification | H100 SXM | A100 40GB | AMD Instinct MI210 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Ampere | CDNA 2 |
| VRAM | 80GB | 40GB | 64GB |
| VRAM Type | HBM3 | HBM2e | HBM2E |
| Memory Bandwidth | 3.4 TB/s | 1.6 TB/s | 1.6 TB/s |
| FP16 TFLOPS | 134.0 | 78.0 | 181.0 |
| Tensor TFLOPS | 2.0k | 312.0 | 362.0 |
| TDP | 700W | 250W | 300W |
| Form Factor | SXM | PCIe | - |
| Hardware Price | $$32k | $$8.0k | - |
| Cloud Price (min) | $2.10/hr | $0.720/hr | - |
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H100 SXM vs A100 40GB FAQ
It depends on your use case. The H100 SXM offers 534% better performance (2.0k vs 312.0 TFLOPS). However, the A100 40GB is 300% cheaper. For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM has more VRAM with 80GB compared to 40GB (100% 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 40GB, the cheaper option may be more cost-effective.
The A100 40GB is 300% cheaper at $$8.0k vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 534% more performance and 100% more VRAM. The upgrade cost difference is approximately $$24k. Consider if your workloads are bottlenecked by current GPU capabilities.