H100 SXM vs H100 NVL
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA H100 NVL
Difference Analysis
Full Specifications
| Specification | H100 SXM | H100 NVL | AMD Instinct MI250 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Hopper | CDNA 2 |
| VRAM | 80GB | 94GB | 128GB |
| VRAM Type | HBM3 | HBM3 | HBM2E |
| Memory Bandwidth | 3.4 TB/s | 3.9 TB/s | 3.3 TB/s |
| FP16 TFLOPS | 134.0 | 134.0 | 362.0 |
| Tensor TFLOPS | 2.0k | 2.0k | 724.0 |
| TDP | 700W | 400W | 500W |
| Form Factor | SXM | NVL | - |
| Hardware Price | $$32k | $$35k | - |
| Cloud Price (min) | $2.10/hr | $1.38/hr | - |
Related Comparisons
H100 SXM vs H100 NVL FAQ
It depends on your use case. The H100 SXM offers 0% better performance (2.0k vs 2.0k TFLOPS). However, the H100 SXM is 9% cheaper. For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 NVL has more VRAM with 94GB compared to 80GB (18% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 NVL is generally better due to its larger VRAM (94GB). 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.
The H100 SXM is 9% cheaper at $$32k vs $$35k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 0% more performance and similar VRAM. The upgrade cost difference is approximately $$3.0k. Consider if your workloads are bottlenecked by current GPU capabilities.