H200 vs NVIDIA GeForce RTX 4070 Ti Super
Detailed comparison of specifications, performance, and pricing between NVIDIA H200 SXM and NVIDIA GeForce RTX 4070 Ti Super
Difference Analysis
Full Specifications
| Specification | H200 | NVIDIA GeForce RTX 4070 Ti Super | AMD Instinct MI100 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Consumer | Data Center |
| Architecture | Hopper | Ada Lovelace | CDNA |
| VRAM | 141GB | 16GB | 32GB |
| VRAM Type | HBM3e | GDDR6X | HBM2 |
| Memory Bandwidth | 4.8 TB/s | 672 GB/s | 1.2 TB/s |
| FP16 TFLOPS | 134.0 | 88.2 | 184.6 |
| Tensor TFLOPS | 2.0k | 353.0 | 184.6 |
| TDP | 700W | 285W | 300W |
| Form Factor | SXM | - | - |
| Hardware Price | $$38k | - | - |
| Cloud Price (min) | $2.30/hr | - | - |
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H200 vs NVIDIA GeForce RTX 4070 Ti Super FAQ
It depends on your use case. The H200 offers 461% better performance (2.0k vs 353.0 TFLOPS). For raw performance, choose H200. For value, consider your budget and workload requirements.
The H200 has more VRAM with 141GB compared to 16GB (781% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H200 is generally better due to its larger VRAM (141GB). 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 H200 would give you 461% more performance and 781% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.