L4 vs RTX 4060

The L4 demonstrates substantial superiority in compute throughput: its 121 TFLOPS FP16 rating doubles the RTX 4060's 242 TFLOPS FP8 capability, enabling faster AI inference on large models. In contrast, the RTX 4060's balanced 15.1 TFLOPS across FP16 and FP32 suits lighter tasks, but falls short for demanding workloads. The FP16 to FP32 delta on the L4, 121 TFLOPS to 30.3 TFLOPS, supports efficient training where FP32 precision matters, unlike the RTX 4060's identical 15.1 TFLOPS in both.

Memory specifications further differentiate usage: the L4's 24 GB VRAM and 300 GB/s bandwidth accommodate larger batch sizes in training and inference, reducing data transfer bottlenecks compared to the RTX 4060's 8 GB VRAM and 272 GB/s. This impacts real-world scenarios like processing high-resolution images or extended sequences, where the L4 handles bigger datasets without swapping. Power efficiency also favors the L4 at 72W TDP versus 115W, lowering operational costs in prolonged cloud sessions.

Professionals select the L4 for memory-bound applications requiring 24 GB VRAM, such as deploying large language models where 8 GB limits the RTX 4060. Its 121 TFLOPS FP16 performance excels in inference tasks with high throughput demands.

Datacenter deployments benefit from the L4's 72W TDP and PCIe 4.0 interconnect, ensuring scalability and efficiency over the RTX 4060's higher 115W consumption.

Budget users opt for the RTX 4060 in cost-sensitive scenarios, with pricing from $0.08 per hour versus the L4's $0.32 per hour. Its 8 GB VRAM suffices for small-scale fine-tuning or gaming workloads at 15.1 TFLOPS FP32.

Entry-level inference or development testing favors the RTX 4060, where 272 GB/s bandwidth handles modest batch sizes without the L4's premium.