NVIDIA A100 80GB PCIe vs AMD Instinct MI210
Detailed comparison of specifications, performance, and pricing between NVIDIA A100 80GB PCIe and AMD Instinct MI210
Difference Analysis
Full Specifications
| Specification | NVIDIA A100 80GB PCIe | AMD Instinct MI210 |
|---|---|---|
| Brand | NVIDIA | AMD |
| Series | Data Center | Data Center |
| Architecture | Ampere | CDNA 2 |
| VRAM | 80GB | 64GB |
| VRAM Type | HBM2E | HBM2E |
| Memory Bandwidth | 2.0 TB/s | 1.6 TB/s |
| FP16 TFLOPS | 312.0 | 181.0 |
| Tensor TFLOPS | 624.0 | 362.0 |
| TDP | 300W | 300W |
| Form Factor | - | - |
| Hardware Price | - | - |
| Cloud Price (min) | - | - |
Which Should You Choose?
For AI Training
Large model training needs maximum VRAM and memory bandwidth.
For AI Inference
Inference prioritizes throughput and cost efficiency.
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NVIDIA A100 80GB PCIe vs AMD Instinct MI210 FAQ
It depends on your use case. The NVIDIA A100 80GB PCIe offers 72% better performance (624.0 vs 362.0 TFLOPS). For raw performance, choose NVIDIA A100 80GB PCIe. For value, consider your budget and workload requirements.
The NVIDIA A100 80GB PCIe has more VRAM with 80GB compared to 64GB (25% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the NVIDIA A100 80GB PCIe is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 64GB, 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 NVIDIA A100 80GB PCIe would give you 72% more performance and 25% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.