NVIDIA A100 40GB SXM vs Tesla V100
Detailed comparison of specifications, performance, and pricing between NVIDIA A100 40GB SXM and Tesla V100
Difference Analysis
Full Specifications
| Specification | NVIDIA A100 40GB SXM | Tesla V100 |
|---|---|---|
| Brand | NVIDIA | NVIDIA |
| Series | Data Center | - |
| Architecture | Ampere | - |
| VRAM | 40GB | 16GB |
| VRAM Type | HBM2 | - |
| Memory Bandwidth | 1.6 TB/s | - |
| FP16 TFLOPS | 312.0 | - |
| Tensor TFLOPS | 624.0 | - |
| TDP | 400W | - |
| Form Factor | - | - |
| Hardware Price | - | - |
| Cloud Price (min) | $1.29/hr | $0.190/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 40GB SXM vs Tesla V100 FAQ
It depends on your use case. The NVIDIA A100 40GB SXM offers 0% better performance (624.0 vs - TFLOPS). For raw performance, choose NVIDIA A100 40GB SXM. For value, consider your budget and workload requirements.
The NVIDIA A100 40GB SXM has more VRAM with 40GB compared to 16GB (150% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the NVIDIA 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 16GB, 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 40GB SXM would give you 0% more performance and 150% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
