L4 vs RTX 2080

Compute capabilities define the core disparity: the L4 delivers 121 TFLOPS FP16 and 30.3 TFLOPS FP32, providing roughly 12 times the FP16 throughput and 3 times the FP32 of the RTX 2080's 10.1 TFLOPS in each. This delta translates to accelerated neural network training and inference on the L4, as FP16 precision dominates modern deep learning workflows, enabling 10 to 12 times faster iterations on large models.

Memory specifications further tilt toward the L4 for real-world tasks. Its 24 GB GDDR6 supports larger batch sizes in training compared to the RTX 2080's 8 to 11 GB limit, which constrains model scale and increases swapping overhead. Although the RTX 2080 offers higher 616 GB/s bandwidth versus the L4's 300 GB/s, the L4's FP8 capability at 242 TFLOPS enhances inference efficiency for quantized models, offsetting bandwidth in memory-bound scenarios.

Power and interconnects influence deployment: the L4's 72W TDP suits dense cloud racks, while the RTX 2080's 215W demands more cooling. PCIe 4.0 on the L4 ensures modern data transfer rates over the RTX 2080's NVLink, benefiting multi-GPU setups in inference pipelines.

The L4 excels in memory-intensive machine learning workloads: its 24 GB GDDR6 handles large language models during training or inference, where the RTX 2080's 8 to 11 GB falls short. Professionals prioritize it for FP16 tasks at 121 TFLOPS, achieving up to 12 times the speed of the RTX 2080's 10.1 TFLOPS.

Cloud users select the L4 for efficient scaling in datacenter environments, leveraging 72W TDP and PCIe 4.0 at $0.32 per hour starting price across 15 offers.

The RTX 2080 suits budget-constrained prototyping or lightweight inference: its $0.05 per hour starting price across 6 offers undercuts the L4's $0.32, ideal for small-scale tasks fitting within 8 to 11 GB VRAM.

Gaming-adjacent or legacy applications benefit from its 616 GB/s bandwidth and NVLink, where high throughput outweighs the L4's compute advantages in non-ML scenarios.