H100 SXM vs H100 PCIe
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA H100 PCIe
Difference Analysis
Full Specifications
| Specification | H100 SXM | H100 PCIe | RTX 4080 Super | AMD Instinct MI100 |
|---|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Consumer | Data Center |
| Architecture | Hopper | Hopper | Ada Lovelace | CDNA |
| VRAM | 80GB | 80GB | 16GB | 32GB |
| VRAM Type | HBM3 | HBM2e | GDDR6X | HBM2 |
| Memory Bandwidth | 3.4 TB/s | 2.0 TB/s | 736 GB/s | 1.2 TB/s |
| FP16 TFLOPS | 134.0 | 102.0 | 104.4 | 184.6 |
| Tensor TFLOPS | 2.0k | 1.5k | - | 184.6 |
| TDP | 700W | 350W | 320W | 300W |
| Form Factor | SXM | PCIe | PCIe | - |
| Hardware Price | $$32k | $$28k | $$1.1k | - |
| Cloud Price (min) | $2.10/hr | $2.39/hr | - | - |
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H100 SXM vs H100 PCIe FAQ
It depends on your use case. The H100 SXM offers 31% better performance (2.0k vs 1.5k TFLOPS). However, the H100 PCIe is 14% cheaper. For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM has more VRAM with 80GB compared to 80GB (0% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 SXM is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 80GB, the cheaper option may be more cost-effective.
The H100 PCIe is 14% cheaper at $$28k vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 31% more performance and 0% more VRAM. The upgrade cost difference is approximately $$4.0k. Consider if your workloads are bottlenecked by current GPU capabilities.