L4 vs RTX 3060

The L4's FP16 performance of 121 TFLOPS vastly outpaces the RTX 3060's 12.7 TFLOPS, providing approximately 9.5 times the throughput for half-precision operations common in neural network training and inference. Its FP32 capability at 30.3 TFLOPS doubles the RTX 3060's 12.7 TFLOPS, benefiting single-precision scientific computing and model training phases requiring higher accuracy. The L4's FP8 support at 242 TFLOPS further accelerates quantized inference workloads.

Memory capacity proves decisive: the L4's 24 GB VRAM supports larger batch sizes and complex models without swapping, unlike the RTX 3060's 12 GB limit which constrains workloads like large language models. Although the RTX 3060 edges bandwidth at 360 GB/s over the L4's 300 GB/s, the doubled VRAM on the L4 mitigates this for memory-bound tasks, allowing sustained performance on datasets exceeding 12 GB.

Power draw underscores efficiency: the L4's 72W TDP enables dense cloud deployments, reducing cooling costs compared to the RTX 3060's 170W. In real-world terms, the L4 excels in high-throughput inference servers, while the RTX 3060 suits lightweight prototyping where cost trumps speed.

The L4 emerges as the superior choice for inference on large language models requiring over 12 GB VRAM, leveraging its 24 GB capacity and 242 TFLOPS FP8 performance. Datacenter environments benefit from its 72W TDP and PCIe 4.0 interconnect, supporting scalable clusters at $0.32 per hour starting price. Professionals prioritizing 121 TFLOPS FP16 throughput for rapid training iterations select the L4 over consumer alternatives.

Budget-driven users opt for the RTX 3060 when prototyping small models under 12 GB VRAM, capitalizing on its $0.03 per hour starting price across 12 cloud offers. Its 360 GB/s bandwidth aids bandwidth-sensitive tasks like Stable Diffusion generation on modest datasets. Developers testing Ampere-era codebases or running inference at 12.7 TFLOPS FP16 find the RTX 3060 adequate without premium costs.