B200 vs H100 SXM
Detailed comparison of specifications, performance, and pricing between NVIDIA B200 and NVIDIA H100 SXM
Difference Analysis
Full Specifications
| Specification | B200 | H100 SXM | A100 80GB |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center |
| Architecture | Blackwell | Hopper | Ampere |
| VRAM | 192GB | 80GB | 80GB |
| VRAM Type | HBM3e | HBM3 | HBM2e |
| Memory Bandwidth | 8.0 TB/s | 3.4 TB/s | 2.0 TB/s |
| FP16 TFLOPS | - | 134.0 | 78.0 |
| Tensor TFLOPS | 4.5k | 2.0k | 312.0 |
| TDP | 1000W | 700W | 400W |
| Form Factor | SXM | SXM | SXM |
| Hardware Price | $$45k | $$32k | $$12k |
| Cloud Price (min) | $3.75/hr | $2.10/hr | $1.15/hr |
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B200 vs H100 SXM FAQ
It depends on your use case. The B200 offers 127% better performance (4.5k vs 2.0k TFLOPS). However, the H100 SXM is 41% cheaper. For raw performance, choose B200. For value, consider your budget and workload requirements.
The B200 has more VRAM with 192GB compared to 80GB (140% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the B200 is generally better due to its larger VRAM (192GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 80GB, the cheaper option may be more cost-effective.
The H100 SXM is 41% cheaper at $$32k vs $$45k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to B200 would give you 127% more performance and 140% more VRAM. The upgrade cost difference is approximately $$13k. Consider if your workloads are bottlenecked by current GPU capabilities.