A100 SXM vs RTX 4090
Detailed comparison of specifications, performance, and pricing between A100 SXM and RTX 4090
Difference Analysis
Full Specifications
| Specification | A100 SXM | RTX 4090 | H200 | B200 |
|---|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Consumer | Data Center | Data Center |
| Architecture | Ampere | Ada Lovelace | Hopper | Blackwell |
| VRAM | 80GB | 24GB | 141GB | 192GB |
| VRAM Type | HBM2e | GDDR6X | HBM3e | HBM3e |
| Memory Bandwidth | 2.0 TB/s | 1.0 TB/s | 4.8 TB/s | 8.0 TB/s |
| FP16 TFLOPS | 78.0 | 165.2 | 134.0 | - |
| Tensor TFLOPS | 312.0 | - | 2.0k | 4.5k |
| TDP | 400W | 450W | 700W | 1000W |
| Form Factor | SXM | PCIe | SXM | SXM |
| Hardware Price | $$12k | $$1.8k | $$38k | $$45k |
| Cloud Price (min) | $1.15/hr | $0.268/hr | $2.30/hr | $3.75/hr |
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A100 SXM vs RTX 4090 FAQ
It depends on your use case. The A100 SXM offers 89% better performance (312.0 vs 165.2 TFLOPS). However, the RTX 4090 is 567% cheaper. For raw performance, choose A100 SXM. For value, consider your budget and workload requirements.
The A100 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 A100 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.
The RTX 4090 is 567% cheaper at $$1.8k vs $$12k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to A100 SXM would give you 89% more performance and 233% more VRAM. The upgrade cost difference is approximately $$10k. Consider if your workloads are bottlenecked by current GPU capabilities.
