GH200 vs AMD Instinct MI300A
Detailed comparison of specifications, performance, and pricing between GH200 and AMD Instinct MI300A
Difference Analysis
Full Specifications
| Specification | GH200 | AMD Instinct MI300A | B200 | H100 PCIe |
|---|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA | NVIDIA |
| Series | - | Data Center | Data Center | Data Center |
| Architecture | - | CDNA 3 | Blackwell | Hopper |
| VRAM | 96GB | 128GB | 192GB | 80GB |
| VRAM Type | - | HBM3 | HBM3e | HBM2e |
| Memory Bandwidth | - | 5.3 TB/s | 8.0 TB/s | 2.0 TB/s |
| FP16 TFLOPS | - | 980.0 | - | 102.0 |
| Tensor TFLOPS | - | 2.0k | 4.5k | 1.5k |
| TDP | - | 760W | 1000W | 350W |
| Form Factor | - | - | SXM | PCIe |
| Hardware Price | - | - | $$45k | $$28k |
| Cloud Price (min) | $2.29/hr | - | $3.75/hr | $2.89/hr |
Which Should You Choose?
For AI Training
Large model training needs maximum VRAM and memory bandwidth.
For AI Inference
Inference prioritizes throughput and cost efficiency.
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GH200 vs AMD Instinct MI300A FAQ
It depends on your use case. The AMD Instinct MI300A offers 0% better performance (2.0k vs - TFLOPS). For raw performance, choose AMD Instinct MI300A. For value, consider your budget and workload requirements.
The AMD Instinct MI300A 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 AMD Instinct MI300A 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.
The AMD Instinct MI300A actually offers 0% better performance. An "upgrade" to GH200 would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.
