H100 SXM vs NVIDIA GeForce RTX 4070 Ti Super
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA GeForce RTX 4070 Ti Super
Difference Analysis
Full Specifications
| Specification | H100 SXM | NVIDIA GeForce RTX 4070 Ti Super | AMD Radeon RX 7900 XT |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Consumer | Consumer |
| Architecture | Hopper | Ada Lovelace | RDNA 3 |
| VRAM | 80GB | 16GB | 20GB |
| VRAM Type | HBM3 | GDDR6X | GDDR6 |
| Memory Bandwidth | 3.4 TB/s | 672 GB/s | 800 GB/s |
| FP16 TFLOPS | 134.0 | 88.2 | 104.0 |
| Tensor TFLOPS | 2.0k | 353.0 | - |
| TDP | 700W | 285W | 315W |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | - | - |
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H100 SXM vs NVIDIA GeForce RTX 4070 Ti Super FAQ
It depends on your use case. The H100 SXM offers 461% better performance (2.0k vs 353.0 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 461% more performance and 400% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.