A30 vs L40S

The L40S demonstrates superior compute capabilities over the A30, particularly in mixed-precision workloads. Its FP16 performance reaches 362 TFLOPS compared to the A30's 10.3 TFLOPS, enabling faster model training where half-precision dominates. FP32 throughput of 91 TFLOPS on the L40S versus 10.3 TFLOPS on the A30 accelerates single-precision scientific simulations and certain inference pipelines. The addition of FP8 at 724 TFLOPS on the L40S optimizes large language model inference by reducing precision without quality loss.

Memory differences impact real-world scalability: the L40S's 48 GB GDDR6X supports larger batch sizes for training massive models, while the A30's 24 GB HBM2 limits it to smaller datasets despite higher 933 GB/s bandwidth over the L40S's 864 GB/s. Higher TDP of 350W on the L40S demands robust cooling but yields up to 35 times FP16 uplift, ideal for throughput-oriented tasks. The A30's 165W efficiency suits power-sensitive environments, though overall performance lags significantly.

The A30 excels in power-constrained deployments requiring 165W TDP. Its 933 GB/s HBM2 bandwidth outperforms the L40S's 864 GB/s for memory-intensive tasks with smaller models fitting within 24 GB VRAM. NVLink interconnect enables efficient multi-GPU scaling for legacy Ampere-optimized software where no live cloud offers reduce procurement risks in on-premises setups.

The L40S suits modern AI workloads leveraging 48 GB VRAM for large models and 362 TFLOPS FP16 for rapid training. Availability from $0.40 per hour across 18 providers makes it practical for cloud scaling, with FP8 at 724 TFLOPS optimizing inference latency. PCIe 4.0 interconnect supports high-bandwidth clusters without NVLink dependency.