L40S vs RTX 3060

The L40S demonstrates overwhelming compute superiority: 362 TFLOPS FP16 performance towers over the RTX 3060's 12.7 TFLOPS, a 28-fold increase ideal for mixed-precision training. FP32 throughput hits 91 TFLOPS on the L40S versus 12.7 TFLOPS, accelerating full-precision model training by over seven times. The L40S's FP8 capability at 724 TFLOPS further optimizes low-precision inference for large language models.

Memory specs amplify real-world advantages. With 48 GB VRAM, the L40S supports batch sizes four times larger than the RTX 3060's 12 GB limit, reducing out-of-memory errors in transformer models. Bandwidth of 864 GB/s versus 360 GB/s minimizes data transfer bottlenecks, enabling 2.4 times faster processing for memory-bound tasks like Stable Diffusion generation.

Power draw differs at 350W for the L40S against 170W, but cloud environments mitigate cooling concerns. Overall, these specs position the L40S for enterprise-scale AI, while the RTX 3060 handles entry-level workloads efficiently.

The L40S excels in high-throughput AI production: large-scale LLM training requires its 48 GB VRAM to load models exceeding 12 GB, and 91 TFLOPS FP32 sustains rapid iterations. Datacenter users benefit from PCIe 4.0 interconnect and 864 GB/s bandwidth for multi-GPU clusters handling billion-parameter models.

Inference deployments favor the L40S when FP8 at 724 TFLOPS delivers sub-second latencies for real-time serving, justifying $1.10 per hour average cost over budget constraints.

The RTX 3060 suits cost-sensitive prototyping: at $0.07 per hour average, it processes small models with 12.7 TFLOPS FP16/FP32, ideal for initial fine-tuning or hobbyist inference. Lower 170W TDP enables dense cloud deployments without premium power infrastructure.

Entry-level tasks like lightweight Stable Diffusion or scientific simulations fit its 360 GB/s bandwidth, offering value when 12 GB VRAM suffices and performance demands stay below datacenter thresholds.