L40S vs RTX 2080

Compute capabilities define the core disparity: the L40S's 362 TFLOPS FP16 performance accelerates deep learning training by enabling larger models and faster iterations, while the RTX 2080's 10.1 TFLOPS restricts it to smaller datasets. In FP32, the L40S delivers 91 TFLOPS for precision tasks like simulations, far exceeding the RTX 2080's matching 10.1 TFLOPS.

For inference, the L40S's FP8 throughput of 724 TFLOPS supports high-volume deployments, a capability absent in the RTX 2080. The 48 GB VRAM on the L40S permits batch sizes that fit massive models without swapping, unlike the RTX 2080's 8-11 GB limit which forces smaller batches or multi-GPU setups.

Memory bandwidth impacts data throughput: 864 GB/s on the L40S sustains high tensor operations, reducing bottlenecks compared to the RTX 2080's 616 GB/s. These specs translate to the L40S handling enterprise AI, while the RTX 2080 suits prototyping.

The L40S excels in large-scale AI training and inference where 48 GB GDDR6X VRAM accommodates models exceeding 11 GB. Its 362 TFLOPS FP16 and 724 TFLOPS FP8 enable rapid processing of LLMs or diffusion models at scale.

Datacenter deployments benefit from the PCIe 4.0 interconnect and 350W TDP for sustained 91 TFLOPS FP32 workloads, justifying $1.11/hr average cloud costs.

The RTX 2080 fits budget-conscious prototyping or light inference tasks with its 8-11 GB VRAM and 10.1 TFLOPS across FP16/FP32. Low power draw of 215W and NVLink support suit small-scale scientific computing or gaming simulations.

At $0.09/hr average pricing, it provides value for non-demanding workloads across 6 cloud offers, avoiding overkill for sub-10 GB model inference.