NVIDIA A100 80GB PCIe vs MI300
Detailed comparison of specifications, performance, and pricing between NVIDIA A100 80GB PCIe and AMD Instinct MI300
Difference Analysis
Full Specifications
| Specification | NVIDIA A100 80GB PCIe | MI300 | H100 PCIe | H100 |
|---|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center | Data Center |
| Architecture | Ampere | CDNA3 | Hopper | Hopper |
| VRAM | 80GB | 128GB | 80GB | 80GB |
| VRAM Type | HBM2E | HBM3 | HBM2e | HBM3 |
| Memory Bandwidth | 2.0 TB/s | 5.3 TB/s | 2.0 TB/s | 3.4 TB/s |
| FP16 TFLOPS | 312.0 | 490.3 | 102.0 | 134.0 |
| Tensor TFLOPS | 624.0 | - | 1.5k | 2.0k |
| TDP | 300W | 750W | 350W | 700W |
| Form Factor | - | OAM | PCIe | SXM |
| Hardware Price | - | $$15k | $$28k | $$32k |
| Cloud Price (min) | - | - | $2.89/hr | $1.47/hr |
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.
Related Comparisons
NVIDIA A100 80GB PCIe vs MI300 FAQ
It depends on your use case. The NVIDIA A100 80GB PCIe offers 27% better performance (624.0 vs 490.3 TFLOPS). For raw performance, choose NVIDIA A100 80GB PCIe. For value, consider your budget and workload requirements.
The MI300 has more VRAM with 128GB compared to 80GB (60% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the MI300 is generally better due to its larger VRAM (128GB). 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.
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 27% more performance and similar VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
