B200 vs MI300
Detailed comparison of specifications, performance, and pricing between NVIDIA B200 and AMD Instinct MI300
Difference Analysis
Full Specifications
| Specification | B200 | MI300 |
|---|---|---|
| Brand | NVIDIA | AMD |
| Series | Data Center | Data Center |
| Architecture | Blackwell | CDNA3 |
| VRAM | 192GB | 128GB |
| VRAM Type | HBM3e | HBM3 |
| Memory Bandwidth | 8.0 TB/s | 5.3 TB/s |
| FP16 TFLOPS | - | 490.3 |
| Tensor TFLOPS | 4.5k | - |
| TDP | 1000W | 750W |
| Form Factor | SXM | OAM |
| Hardware Price | $$45k | $$15k |
| Cloud Price (min) | $3.75/hr | - |
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B200 vs MI300 FAQ
It depends on your use case. The B200 offers 818% better performance (4.5k vs 490.3 TFLOPS). However, the MI300 is 200% cheaper. For raw performance, choose B200. For value, consider your budget and workload requirements.
The B200 has more VRAM with 192GB compared to 128GB (50% 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 128GB, the cheaper option may be more cost-effective.
The MI300 is 200% cheaper at $$15k vs $$45k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to B200 would give you 818% more performance and 50% more VRAM. The upgrade cost difference is approximately $$30k. Consider if your workloads are bottlenecked by current GPU capabilities.