B200 vs V100
Detailed comparison of specifications, performance, and pricing between NVIDIA B200 and NVIDIA V100 32GB
Difference Analysis
Full Specifications
| Specification | B200 | V100 | AMD Instinct MI100 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Blackwell | Volta | CDNA |
| VRAM | 192GB | 32GB | 32GB |
| VRAM Type | HBM3e | HBM2 | HBM2 |
| Memory Bandwidth | 8.0 TB/s | 900 GB/s | 1.2 TB/s |
| FP16 TFLOPS | - | 31.4 | 184.6 |
| Tensor TFLOPS | 4.5k | 125.0 | 184.6 |
| TDP | 1000W | 300W | 300W |
| Form Factor | SXM | SXM | - |
| Hardware Price | $$45k | $$2.5k | - |
| Cloud Price (min) | $3.75/hr | $0.140/hr | - |
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B200 vs V100 FAQ
It depends on your use case. The B200 offers 3500% better performance (4.5k vs 125.0 TFLOPS). However, the V100 is 1700% cheaper. For raw performance, choose B200. For value, consider your budget and workload requirements.
The B200 has more VRAM with 192GB compared to 32GB (500% 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 32GB, the cheaper option may be more cost-effective.
The V100 is 1700% cheaper at $$2.5k vs $$45k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to B200 would give you 3500% more performance and 500% more VRAM. The upgrade cost difference is approximately $$43k. Consider if your workloads are bottlenecked by current GPU capabilities.