Tesla V100 vs A100 40GB SXM
Detailed comparison of specifications, performance, and pricing between Tesla V100 and A100 40GB SXM
Difference Analysis
Full Specifications
| Specification | Tesla V100 | A100 40GB SXM | H100 | H200 |
|---|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA | NVIDIA |
| Series | - | Data Center | Data Center | Data Center |
| Architecture | - | Ampere | Hopper | Hopper |
| VRAM | 16GB | 40GB | 80GB | 141GB |
| VRAM Type | - | HBM2 | HBM3 | HBM3e |
| Memory Bandwidth | - | 1.6 TB/s | 3.4 TB/s | 4.8 TB/s |
| FP16 TFLOPS | - | 312.0 | 134.0 | 134.0 |
| Tensor TFLOPS | - | 624.0 | 2.0k | 2.0k |
| TDP | - | 400W | 700W | 700W |
| Form Factor | - | - | SXM | SXM |
| Hardware Price | - | - | $$32k | $$38k |
| Cloud Price (min) | $0.190/hr | $1.99/hr | $1.47/hr | $2.30/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
Tesla V100 vs A100 40GB SXM FAQ
It depends on your use case. The A100 40GB SXM offers 0% better performance (624.0 vs - 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 16GB (150% 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 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.
The A100 40GB SXM actually offers 0% better performance. An "upgrade" to Tesla V100 would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.
