MI300 vs NVIDIA H200 NVL
Detailed comparison of specifications, performance, and pricing between AMD Instinct MI300 and NVIDIA H200 NVL
Difference Analysis
Full Specifications
| Specification | MI300 | NVIDIA H200 NVL |
|---|---|---|
| Brand | AMD | NVIDIA |
| Series | Data Center | - |
| Architecture | CDNA3 | - |
| VRAM | 128GB | 143GB |
| VRAM Type | HBM3 | - |
| Memory Bandwidth | 5.3 TB/s | - |
| FP16 TFLOPS | 490.3 | - |
| Tensor TFLOPS | - | - |
| TDP | 750W | - |
| Form Factor | OAM | - |
| Hardware Price | $$15k | - |
| Cloud Price (min) | - | $3.39/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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MI300 vs NVIDIA H200 NVL 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 NVIDIA H200 NVL has more VRAM with 143GB compared to 128GB (12% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the NVIDIA H200 NVL is generally better due to its larger VRAM (143GB). 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.
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 similar VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.