H200 vs MI300
Detailed comparison of specifications, performance, and pricing between NVIDIA H200 SXM and AMD Instinct MI300
Difference Analysis
Full Specifications
| Specification | H200 | MI300 | A100 80GB |
|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA |
| Series | Data Center | Data Center | - |
| Architecture | Hopper | CDNA3 | - |
| VRAM | 141GB | 128GB | 80GB |
| VRAM Type | HBM3e | HBM3 | - |
| Memory Bandwidth | 4.8 TB/s | 5.3 TB/s | - |
| FP16 TFLOPS | 134.0 | 490.3 | - |
| Tensor TFLOPS | 2.0k | - | - |
| TDP | 700W | 750W | - |
| Form Factor | SXM | OAM | - |
| Hardware Price | $$38k | $$15k | - |
| Cloud Price (min) | $2.30/hr | - | $1.39/hr |
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H200 vs MI300 FAQ
It depends on your use case. The H200 offers 304% better performance (2.0k vs 490.3 TFLOPS). However, the MI300 is 153% cheaper. For raw performance, choose H200. For value, consider your budget and workload requirements.
The H200 has more VRAM with 141GB compared to 128GB (10% 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 128GB, the cheaper option may be more cost-effective.
The MI300 is 153% cheaper at $$15k vs $$38k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H200 would give you 304% more performance and 10% more VRAM. The upgrade cost difference is approximately $$23k. Consider if your workloads are bottlenecked by current GPU capabilities.