H200 vs NVIDIA A100 80GB PCIe
Detailed comparison of specifications, performance, and pricing between NVIDIA H200 SXM and NVIDIA A100 80GB PCIe
Difference Analysis
Full Specifications
| Specification | H200 | NVIDIA A100 80GB PCIe | AMD Instinct MI300A |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Ampere | CDNA 3 |
| VRAM | 141GB | 80GB | 128GB |
| VRAM Type | HBM3e | HBM2E | HBM3 |
| Memory Bandwidth | 4.8 TB/s | 2.0 TB/s | 5.3 TB/s |
| FP16 TFLOPS | 134.0 | 312.0 | 980.0 |
| Tensor TFLOPS | 2.0k | 624.0 | 2.0k |
| TDP | 700W | 300W | 760W |
| Form Factor | SXM | - | - |
| Hardware Price | $$38k | - | - |
| Cloud Price (min) | $2.30/hr | - | - |
Related Comparisons
H200 vs NVIDIA A100 80GB PCIe FAQ
It depends on your use case. The H200 offers 217% better performance (2.0k vs 624.0 TFLOPS). For raw performance, choose H200. For value, consider your budget and workload requirements.
The H200 has more VRAM with 141GB compared to 80GB (76% 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 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 H200 would give you 217% more performance and 76% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.