H100 SXM vs MI300X
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and AMD Instinct MI300X
Difference Analysis
Full Specifications
| Specification | H100 SXM | MI300X |
|---|---|---|
| Brand | NVIDIA | AMD |
| Series | Data Center | Data Center |
| Architecture | Hopper | CDNA3 |
| VRAM | 80GB | 192GB |
| VRAM Type | HBM3 | HBM3 |
| Memory Bandwidth | 3.4 TB/s | 5.3 TB/s |
| FP16 TFLOPS | 134.0 | 653.7 |
| Tensor TFLOPS | 2.0k | - |
| TDP | 700W | 750W |
| Form Factor | SXM | OAM |
| Hardware Price | $$32k | $$18k |
| Cloud Price (min) | $2.10/hr | $1.99/hr |
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H100 SXM vs MI300X FAQ
It depends on your use case. The H100 SXM offers 203% better performance (2.0k vs 653.7 TFLOPS). However, the MI300X is 78% cheaper. For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The MI300X has more VRAM with 192GB compared to 80GB (140% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the MI300X is generally better due to its larger VRAM (192GB). 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.
The MI300X is 78% cheaper at $$18k vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 203% more performance and similar VRAM. The upgrade cost difference is approximately $$14k. Consider if your workloads are bottlenecked by current GPU capabilities.