H200 vs AMD Instinct MI100
Detailed comparison of specifications, performance, and pricing between NVIDIA H200 SXM and AMD Instinct MI100
Difference Analysis
Full Specifications
| Specification | H200 | AMD Instinct MI100 | RTX 5090 |
|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA |
| Series | Data Center | Data Center | - |
| Architecture | Hopper | CDNA | - |
| VRAM | 141GB | 32GB | 32GB |
| VRAM Type | HBM3e | HBM2 | - |
| Memory Bandwidth | 4.8 TB/s | 1.2 TB/s | - |
| FP16 TFLOPS | 134.0 | 184.6 | - |
| Tensor TFLOPS | 2.0k | 184.6 | - |
| TDP | 700W | 300W | - |
| Form Factor | SXM | - | - |
| Hardware Price | $$38k | - | - |
| Cloud Price (min) | $2.30/hr | - | $0.890/hr |
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H200 vs AMD Instinct MI100 FAQ
It depends on your use case. The H200 offers 972% better performance (2.0k vs 184.6 TFLOPS). For raw performance, choose H200. For value, consider your budget and workload requirements.
The H200 has more VRAM with 141GB compared to 32GB (341% 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 32GB, 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 972% more performance and 341% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.