B200 vs A100 80GB
Detailed comparison of specifications, performance, and pricing between NVIDIA B200 and NVIDIA A100 80GB SXM
Difference Analysis
Full Specifications
| Specification | B200 | A100 80GB | NVIDIA A100 40GB SXM |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center |
| Architecture | Blackwell | Ampere | Ampere |
| VRAM | 192GB | 80GB | 40GB |
| VRAM Type | HBM3e | HBM2e | HBM2 |
| Memory Bandwidth | 8.0 TB/s | 2.0 TB/s | 1.6 TB/s |
| FP16 TFLOPS | - | 78.0 | 312.0 |
| Tensor TFLOPS | 4.5k | 312.0 | 624.0 |
| TDP | 1000W | 400W | 400W |
| Form Factor | SXM | SXM | - |
| Hardware Price | $$45k | $$12k | - |
| Cloud Price (min) | $3.75/hr | $1.15/hr | $1.29/hr |
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B200 vs A100 80GB FAQ
It depends on your use case. The B200 offers 1342% better performance (4.5k vs 312.0 TFLOPS). However, the A100 80GB is 275% cheaper. For raw performance, choose B200. For value, consider your budget and workload requirements.
The B200 has more VRAM with 192GB compared to 80GB (140% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the B200 is generally better due to its larger VRAM (192GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 80GB, the cheaper option may be more cost-effective.
The A100 80GB is 275% cheaper at $$12k vs $$45k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to B200 would give you 1342% more performance and 140% more VRAM. The upgrade cost difference is approximately $$33k. Consider if your workloads are bottlenecked by current GPU capabilities.