L4 vs RTX 3080

The L4's FP16 performance of 121 TFLOPS significantly outpaces the RTX 3080's 29.8 TFLOPS, benefiting half-precision training and inference in deep learning pipelines that prioritize speed over full precision. FP32 throughput remains close, with the L4 at 30.3 TFLOPS against 29.8 TFLOPS, ensuring comparable single-precision scientific computing or rendering tasks. The L4's FP8 rating of 242 TFLOPS further accelerates quantized inference for large language models.

Memory bandwidth impacts batch processing: the RTX 3080's 760 GB/s supports larger batch sizes in data-intensive operations like image generation, reducing bottlenecks compared to the L4's 300 GB/s. However, the L4's 24 GB VRAM capacity allows deployment of models exceeding 12 GB, such as 13B parameter LLMs, without swapping, whereas the RTX 3080 risks out-of-memory errors.

Power efficiency defines deployment scenarios. The L4's 72 W TDP enables dense cloud configurations with lower cooling demands, ideal for sustained inference, while the RTX 3080's 320 W TDP suits bursty workloads but increases operational costs in power-sensitive environments.

The L4 excels in inference-heavy workloads requiring substantial VRAM, such as serving 24 GB models at 121 TFLOPS FP16. Its 72 W TDP supports edge or dense cloud deployments without excessive power draw. Datacenter users prioritizing PCIe 4.0 interconnect and Ada Lovelace optimizations choose the L4 for reliable, efficient AI serving.

The RTX 3080 fits budget-conscious training or rendering where 760 GB/s bandwidth accelerates data transfers for batch sizes up to the 10-12 GB VRAM limit. At $0.06/hr starting price, it appeals to experimenters or small-scale Stable Diffusion runs leveraging Ampere's 29.8 TFLOPS FP16. High-throughput creative tasks favor its consumer-grade performance per dollar.