L40S vs RTX A4500

The L40S demonstrates superior compute throughput: its 362 TFLOPS FP16 rating enables accelerated AI model training and inference in half-precision formats, far exceeding the A4500's 19.2 TFLOPS. The FP32 performance of 91 TFLOPS on L40S supports demanding scientific simulations and rendering tasks, more than four times the A4500's 19.2 TFLOPS. FP8 capability at 724 TFLOPS on L40S further optimizes low-precision inference for large language models.

Memory specifications impact practical usage profoundly. The L40S 864 GB/s bandwidth sustains larger batch sizes during training, minimizing data transfer bottlenecks unlike the A4500's 448 GB/s limit which constrains scale for memory-intensive operations. This bandwidth edge proves critical for deep learning pipelines handling high-resolution datasets.

Power consumption varies: L40S TDP reaches 350 W for peak performance, while A4500 stays at 140 W, influencing deployment in power-sensitive environments. Overall, L40S specs translate to 4-5x faster execution in AI workloads.

Select the L40S for memory-bound tasks like training large-scale LLMs: its 48 GB GDDR6X VRAM accommodates models exceeding 16 GB limits of the A4500. High FP16 at 362 TFLOPS and FP8 at 724 TFLOPS excel in inference serving high query volumes.

The L40S suits datacenter-scale deployments where 864 GB/s bandwidth enables efficient large-batch processing, justifying $1.13 per hour average cost for superior throughput.

Choose the RTX A4500 for cost-sensitive applications: pricing from $0.10 per hour suits prototyping and small inference runs within 16 GB VRAM constraints. Lower 140 W TDP reduces operational costs in edge or multi-GPU setups.

It fits lighter visualization and fine-tuning where 19.2 TFLOPS FP32 suffices without needing L40S scale.