H100 SXM vs NVIDIA V100 16GB
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA V100 16GB
Difference Analysis
Full Specifications
| Specification | H100 SXM | NVIDIA V100 16GB | AMD Instinct MI100 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Volta | CDNA |
| VRAM | 80GB | 16GB | 32GB |
| VRAM Type | HBM3 | HBM2 | HBM2 |
| Memory Bandwidth | 3.4 TB/s | 900 GB/s | 1.2 TB/s |
| FP16 TFLOPS | 134.0 | 125.0 | 184.6 |
| Tensor TFLOPS | 2.0k | 125.0 | 184.6 |
| TDP | 700W | 300W | 300W |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | - | - |
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H100 SXM vs NVIDIA V100 16GB FAQ
It depends on your use case. The H100 SXM offers 1483% better performance (2.0k vs 125.0 TFLOPS). For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM has more VRAM with 80GB compared to 16GB (400% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 SXM is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 16GB, 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 1483% more performance and 400% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.