L40S vs A100 40GB SXM
Detailed comparison of specifications, performance, and pricing between L40S and A100 40GB SXM
Difference Analysis
Full Specifications
| Specification | L40S | A100 40GB SXM | H100 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center |
| Architecture | Ada Lovelace | Ampere | Hopper |
| VRAM | 48GB | 40GB | 80GB |
| VRAM Type | GDDR6 | HBM2 | HBM3 |
| Memory Bandwidth | 864 GB/s | 1.6 TB/s | 3.4 TB/s |
| FP16 TFLOPS | 183.0 | 312.0 | 134.0 |
| Tensor TFLOPS | 733.0 | 624.0 | 2.0k |
| TDP | 350W | 400W | 700W |
| Form Factor | PCIe | - | SXM |
| Hardware Price | $$9.0k | - | $$32k |
| Cloud Price (min) | $0.910/hr | $1.99/hr | $1.47/hr |
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L40S vs A100 40GB SXM FAQ
It depends on your use case. The L40S offers 17% better performance (733.0 vs 624.0 TFLOPS). For raw performance, choose L40S. For value, consider your budget and workload requirements.
The L40S has more VRAM with 48GB compared to 40GB (20% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the L40S is generally better due to its larger VRAM (48GB). 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 L40S would give you 17% more performance and 20% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
