A100 80GB vs MI300
Detailed comparison of specifications, performance, and pricing between NVIDIA A100 80GB SXM and AMD Instinct MI300
Difference Analysis
Full Specifications
| Specification | A100 80GB | MI300 |
|---|---|---|
| Brand | NVIDIA | AMD |
| Series | Data Center | Data Center |
| Architecture | Ampere | CDNA3 |
| VRAM | 80GB | 128GB |
| VRAM Type | HBM2e | HBM3 |
| Memory Bandwidth | 2.0 TB/s | 5.3 TB/s |
| FP16 TFLOPS | 78.0 | 490.3 |
| Tensor TFLOPS | 312.0 | - |
| TDP | 400W | 750W |
| Form Factor | SXM | OAM |
| Hardware Price | $$12k | $$15k |
| Cloud Price (min) | $1.15/hr | - |
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A100 80GB vs MI300 FAQ
It depends on your use case. The MI300 offers 57% better performance (490.3 vs 312.0 TFLOPS). However, the A100 80GB is 25% cheaper. For raw performance, choose MI300. 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.
The A100 80GB is 25% cheaper at $$12k vs $$15k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
The MI300 actually offers 57% better performance. An "upgrade" to A100 80GB would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.