A100 PCIE vs AMD Instinct MI100
Detailed comparison of specifications, performance, and pricing between A100 PCIE and AMD Instinct MI100
Difference Analysis
Full Specifications
| Specification | A100 PCIE | AMD Instinct MI100 | B200 |
|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA |
| Series | Data Center | Data Center | Data Center |
| Architecture | Ampere | CDNA | Blackwell |
| VRAM | 40GB | 32GB | 192GB |
| VRAM Type | HBM2e | HBM2 | HBM3e |
| Memory Bandwidth | 1.6 TB/s | 1.2 TB/s | 8.0 TB/s |
| FP16 TFLOPS | 78.0 | 184.6 | - |
| Tensor TFLOPS | 312.0 | 184.6 | 4.5k |
| TDP | 250W | 300W | 1000W |
| Form Factor | PCIe | - | SXM |
| Hardware Price | $$8.0k | - | $$45k |
| Cloud Price (min) | $0.720/hr | - | $3.75/hr |
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A100 PCIE vs AMD Instinct MI100 FAQ
It depends on your use case. The A100 PCIE offers 69% better performance (312.0 vs 184.6 TFLOPS). For raw performance, choose A100 PCIE. For value, consider your budget and workload requirements.
The A100 PCIE has more VRAM with 40GB compared to 32GB (25% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the A100 PCIE is generally better due to its larger VRAM (40GB). 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.
Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.
Upgrading to A100 PCIE would give you 69% more performance and 25% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
