A100 40GB SXM vs AMD Instinct MI100
Detailed comparison of specifications, performance, and pricing between A100 40GB SXM and AMD Instinct MI100
Difference Analysis
Full Specifications
| Specification | A100 40GB SXM | AMD Instinct MI100 | H200 | H100 PCIe |
|---|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center | Data Center |
| Architecture | Ampere | CDNA | Hopper | Hopper |
| VRAM | 40GB | 32GB | 141GB | 80GB |
| VRAM Type | HBM2 | HBM2 | HBM3e | HBM2e |
| Memory Bandwidth | 1.6 TB/s | 1.2 TB/s | 4.8 TB/s | 2.0 TB/s |
| FP16 TFLOPS | 312.0 | 184.6 | 134.0 | 102.0 |
| Tensor TFLOPS | 624.0 | 184.6 | 2.0k | 1.5k |
| TDP | 400W | 300W | 700W | 350W |
| Form Factor | - | - | SXM | PCIe |
| Hardware Price | - | - | $$38k | $$28k |
| Cloud Price (min) | $1.99/hr | - | $2.30/hr | $2.89/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
A100 40GB SXM vs AMD Instinct MI100 FAQ
It depends on your use case. The A100 40GB SXM offers 238% better performance (624.0 vs 184.6 TFLOPS). For raw performance, choose A100 40GB SXM. For value, consider your budget and workload requirements.
The A100 40GB SXM 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 40GB SXM 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 40GB SXM would give you 238% more performance and 25% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
