A100 SXM vs L40S
Detailed comparison of specifications, performance, and pricing between A100 SXM and L40S
Difference Analysis
Full Specifications
| Specification | A100 SXM | L40S | H100 PCIe | B200 |
|---|---|---|---|---|
| Brand | NVIDIA | NVIDIA | NVIDIA | NVIDIA |
| Series | Data Center | Data Center | Data Center | Data Center |
| Architecture | Ampere | Ada Lovelace | Hopper | Blackwell |
| VRAM | 80GB | 48GB | 80GB | 192GB |
| VRAM Type | HBM2e | GDDR6 | HBM2e | HBM3e |
| Memory Bandwidth | 2.0 TB/s | 864 GB/s | 2.0 TB/s | 8.0 TB/s |
| FP16 TFLOPS | 78.0 | 183.0 | 102.0 | - |
| Tensor TFLOPS | 312.0 | 733.0 | 1.5k | 4.5k |
| TDP | 400W | 350W | 350W | 1000W |
| Form Factor | SXM | PCIe | PCIe | SXM |
| Hardware Price | $$12k | $$9.0k | $$28k | $$45k |
| Cloud Price (min) | $1.15/hr | $0.910/hr | $2.89/hr | $3.75/hr |
Related Comparisons
A100 SXM vs L40S FAQ
It depends on your use case. The L40S offers 135% better performance (733.0 vs 312.0 TFLOPS). However, the L40S is 33% cheaper. For raw performance, choose L40S. For value, consider your budget and workload requirements.
The A100 SXM has more VRAM with 80GB compared to 48GB (67% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the A100 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 48GB, the cheaper option may be more cost-effective.
The L40S is 33% cheaper at $$9.0k vs $$12k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
The L40S actually offers 135% better performance. An "upgrade" to A100 SXM would be a downgrade in raw performance, though it may offer other benefits like lower power consumption or cost.
