H200 NVL vs MI300X
Detailed comparison of specifications, performance, and pricing between H200 NVL and MI300X
Difference Analysis
Full Specifications
| Specification | H200 NVL | MI300X | H100 |
|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA |
| Series | - | Data Center | Data Center |
| Architecture | - | CDNA3 | Hopper |
| VRAM | 143GB | 192GB | 80GB |
| VRAM Type | - | HBM3 | HBM3 |
| Memory Bandwidth | - | 5.3 TB/s | 3.4 TB/s |
| FP16 TFLOPS | - | 653.7 | 134.0 |
| Tensor TFLOPS | - | - | 2.0k |
| TDP | - | 750W | 700W |
| Form Factor | - | OAM | SXM |
| Hardware Price | - | $$18k | $$32k |
| Cloud Price (min) | $3.79/hr | $2.39/hr | $1.47/hr |
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H200 NVL vs MI300X FAQ
It depends on your use case. The MI300X offers 0% better performance (653.7 vs - TFLOPS). For raw performance, choose MI300X. For value, consider your budget and workload requirements.
The MI300X has more VRAM with 192GB compared to 143GB (34% 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 143GB, 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.
The MI300X actually offers 0% better performance. An "upgrade" to H200 NVL would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.
