RTX 5090 vs H100 PCIe
Detailed comparison of specifications, performance, and pricing between RTX 5090 and H100 PCIe
Difference Analysis
Full Specifications
| Specification | RTX 5090 | H100 PCIe | H200 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA |
| Series | - | Data Center | Data Center |
| Architecture | - | Hopper | Hopper |
| VRAM | 32GB | 80GB | 141GB |
| VRAM Type | - | HBM2e | HBM3e |
| Memory Bandwidth | - | 2.0 TB/s | 4.8 TB/s |
| FP16 TFLOPS | - | 102.0 | 134.0 |
| Tensor TFLOPS | - | 1.5k | 2.0k |
| TDP | - | 350W | 700W |
| Form Factor | - | PCIe | SXM |
| Hardware Price | - | $$28k | $$38k |
| Cloud Price (min) | $0.990/hr | $2.89/hr | $2.30/hr |
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RTX 5090 vs H100 PCIe 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 32GB (150% 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 32GB, 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.
The H100 PCIe actually offers 0% better performance. An "upgrade" to RTX 5090 would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.
