H100 SXM vs NVIDIA GeForce RTX 3090 Ti
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA GeForce RTX 3090 Ti
Difference Analysis
Full Specifications
| Specification | H100 SXM | NVIDIA GeForce RTX 3090 Ti | AMD Radeon RX 7900 XT |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Consumer | Consumer |
| Architecture | Hopper | Ampere | RDNA 3 |
| VRAM | 80GB | 24GB | 20GB |
| VRAM Type | HBM3 | GDDR6X | GDDR6 |
| Memory Bandwidth | 3.4 TB/s | 1.0 TB/s | 800 GB/s |
| FP16 TFLOPS | 134.0 | 80.0 | 104.0 |
| Tensor TFLOPS | 2.0k | 320.0 | - |
| TDP | 700W | 450W | 315W |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | $0.460/hr | - |
Which Should You Choose?
For AI Training
Large model training needs maximum VRAM and memory bandwidth.
For AI Inference
Inference prioritizes throughput and cost efficiency.
For Cloud Rental
Minimize hourly costs for cloud workloads.
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H100 SXM vs NVIDIA GeForce RTX 3090 Ti FAQ
It depends on your use case. The H100 SXM offers 518% better performance (2.0k vs 320.0 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 24GB (233% 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 24GB, 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 518% more performance and 233% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.