H100 SXM vs AMD Instinct MI250
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and AMD Instinct MI250
Difference Analysis
Full Specifications
| Specification | H100 SXM | AMD Instinct MI250 | RTX PRO 6000 |
|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA |
| Series | Data Center | Data Center | - |
| Architecture | Hopper | CDNA 2 | - |
| VRAM | 80GB | 128GB | 96GB |
| VRAM Type | HBM3 | HBM2E | - |
| Memory Bandwidth | 3.4 TB/s | 3.3 TB/s | - |
| FP16 TFLOPS | 134.0 | 362.0 | - |
| Tensor TFLOPS | 2.0k | 724.0 | - |
| TDP | 700W | 500W | - |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | - | $1.84/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.
Related Comparisons
H100 SXM vs AMD Instinct MI250 FAQ
It depends on your use case. The H100 SXM offers 173% better performance (2.0k vs 724.0 TFLOPS). For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The AMD Instinct MI250 has more VRAM with 128GB compared to 80GB (60% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the AMD Instinct MI250 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 80GB, 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 H100 SXM would give you 173% more performance and similar VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.