H100 PCIe vs T4G
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 PCIe and T4G
Difference Analysis
Full Specifications
| Specification | H100 PCIe | T4G | AMD Radeon RX 7900 XT |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | - | Consumer |
| Architecture | Hopper | - | RDNA 3 |
| VRAM | 80GB | 16GB | 20GB |
| VRAM Type | HBM2e | - | GDDR6 |
| Memory Bandwidth | 2.0 TB/s | - | 800 GB/s |
| FP16 TFLOPS | 102.0 | - | 104.0 |
| Tensor TFLOPS | 1.5k | - | - |
| TDP | 350W | - | 315W |
| Form Factor | PCIe | - | - |
| Hardware Price | $$28k | - | - |
| Cloud Price (min) | $2.39/hr | $0.420/hr | - |
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H100 PCIe vs T4G FAQ
It depends on your use case. The H100 PCIe offers 0% better performance (1.5k vs - TFLOPS). For raw performance, choose H100 PCIe. For value, consider your budget and workload requirements.
The H100 PCIe 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 PCIe 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 PCIe would give you 0% more performance and 400% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.