A40 vs L40S

Compute specifications highlight the L40S dominance: 362 TFLOPS FP16 versus the A40's 37.4 TFLOPS accelerates deep learning training by nearly 9.7 times, reducing epochs for models like transformers. FP32 performance at 91 TFLOPS on the L40S outpaces the A40's 37.4 TFLOPS by 2.4 times, aiding precision-sensitive simulations. The L40S FP8 at 724 TFLOPS enables ultra-fast inference with quantization, ideal for deployment.

Memory bandwidth of 864 GB/s on the L40S exceeds the A40's 696 GB/s by 24 percent, supporting larger batch sizes in training and minimizing data starvation for 48 GB VRAM utilization. This delta enhances throughput in memory-bound workloads such as fine-tuning large models. The L40S 350W TDP versus 300W reflects higher performance density, though it requires robust power delivery.

In real-world terms, the L40S handles modern Ada-optimized frameworks efficiently, while the A40 suffices for Ampere-era codebases but lags in raw speed.

The A40 fits cost-sensitive or power-limited environments. Pricing starts at $0.24 per hour across 23 cloud offers, undercutting the L40S $0.40 per hour minimum, with 48 GB GDDR6 VRAM at 300W TDP suiting legacy servers. NVLink interconnect enables scalable multi-GPU training for Ampere-specific software stacks.

The L40S targets high-performance AI pipelines. Its 362 TFLOPS FP16 and 724 TFLOPS FP8 dwarf the A40 equivalents, speeding LLM training and inference, while 864 GB/s bandwidth handles large batches. Average $1.10 per hour across 18 offers delivers strong value for Ada workloads.