MI300 vs GH200
Detailed comparison of specifications, performance, and pricing between AMD Instinct MI300 and GH200
Difference Analysis
Full Specifications
| Specification | MI300 | GH200 | H200 |
|---|---|---|---|
| Brand | AMD | NVIDIA | NVIDIA |
| Series | Data Center | - | Data Center |
| Architecture | CDNA3 | - | Hopper |
| VRAM | 128GB | 96GB | 141GB |
| VRAM Type | HBM3 | - | HBM3e |
| Memory Bandwidth | 5.3 TB/s | - | 4.8 TB/s |
| FP16 TFLOPS | 490.3 | - | 134.0 |
| Tensor TFLOPS | - | - | 2.0k |
| TDP | 750W | - | 700W |
| Form Factor | OAM | - | SXM |
| Hardware Price | $$15k | - | $$38k |
| Cloud Price (min) | - | $2.29/hr | $2.30/hr |
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MI300 vs GH200 FAQ
It depends on your use case. The MI300 offers 0% better performance (490.3 vs - TFLOPS). For raw performance, choose MI300. For value, consider your budget and workload requirements.
The MI300 has more VRAM with 128GB compared to 96GB (33% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the MI300 is generally better due to its larger VRAM (128GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 96GB, 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 MI300 would give you 0% more performance and 33% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
