H100 SXM vs RTX A4000
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA RTX A4000
Difference Analysis
Full Specifications
| Specification | H100 SXM | RTX A4000 | AMD Radeon RX 7900 XTX |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Workstation | Consumer |
| Architecture | Hopper | Ampere | RDNA 3 |
| VRAM | 80GB | 16GB | 24GB |
| VRAM Type | HBM3 | GDDR6 | GDDR6 |
| Memory Bandwidth | 3.4 TB/s | 448 GB/s | 960 GB/s |
| FP16 TFLOPS | 134.0 | 38.4 | 122.0 |
| Tensor TFLOPS | 2.0k | - | - |
| TDP | 700W | 140W | 355W |
| Form Factor | SXM | PCIe | - |
| Hardware Price | $$32k | $$900 | - |
| Cloud Price (min) | $2.10/hr | $0.060/hr | - |
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H100 SXM vs RTX A4000 FAQ
It depends on your use case. The H100 SXM offers 5054% better performance (2.0k vs 38.4 TFLOPS). However, the RTX A4000 is 3456% cheaper. 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.
The RTX A4000 is 3456% cheaper at $$900 vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 5054% more performance and 400% more VRAM. The upgrade cost difference is approximately $$31k. Consider if your workloads are bottlenecked by current GPU capabilities.