H100 SXM vs NVIDIA A100 40GB SXM
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA A100 40GB SXM
Difference Analysis
Full Specifications
| Specification | H100 SXM | NVIDIA A100 40GB SXM | AMD Instinct MI210 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Data Center | Data Center |
| Architecture | Hopper | Ampere | CDNA 2 |
| VRAM | 80GB | 40GB | 64GB |
| VRAM Type | HBM3 | HBM2 | HBM2E |
| Memory Bandwidth | 3.4 TB/s | 1.6 TB/s | 1.6 TB/s |
| FP16 TFLOPS | 134.0 | 312.0 | 181.0 |
| Tensor TFLOPS | 2.0k | 624.0 | 362.0 |
| TDP | 700W | 400W | 300W |
| Form Factor | SXM | - | - |
| Hardware Price | $$32k | - | - |
| Cloud Price (min) | $2.10/hr | $1.29/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.
For Cloud Rental
Minimize hourly costs for cloud workloads.
Related Comparisons
H100 SXM vs NVIDIA A100 40GB SXM FAQ
It depends on your use case. The H100 SXM offers 217% better performance (2.0k vs 624.0 TFLOPS). For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM has more VRAM with 80GB compared to 40GB (100% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 SXM 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 40GB, 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 H100 SXM would give you 217% more performance and 100% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.