L40S vs RTX 3080

The L40S outperforms the RTX 3080 dramatically in compute throughput: its 362 TFLOPS FP16 ratio to 91 TFLOPS FP32 supports efficient mixed-precision training, enabling faster convergence on large models compared to the RTX 3080's balanced 29.8 TFLOPS in both formats. This delta means training sessions on L40S complete over 12 times quicker in FP16-heavy workflows, ideal for deep learning where half-precision accelerates without accuracy loss.

Memory differences impact real-world scalability: L40S 48 GB VRAM and 864 GB/s bandwidth handle batch sizes up to 4-8 times larger than RTX 3080's 10-12 GB and 760 GB/s, reducing out-of-memory errors in transformer models or high-resolution rendering. Larger batches on L40S optimize GPU utilization, cutting effective training time by minimizing data loading overhead.

Power draw reflects capability gaps: L40S 350W TDP sustains peak performance in dense inference, while RTX 3080 320W limits sustained loads. For inference, L40S FP8 at 724 TFLOPS enables quantized models to serve 20+ times more queries per second than RTX 3080 FP16.

Choose the L40S for large-scale AI training or inference: its 48 GB VRAM accommodates models exceeding 10 GB, such as 70B parameter LLMs, impossible on RTX 3080's 10-12 GB. The 362 TFLOPS FP16 and 864 GB/s bandwidth support batch sizes that maximize throughput in cloud clusters.

Datacenter tasks like scientific simulations benefit from PCIe 4.0 and 724 TFLOPS FP8, where RTX 3080 falls short despite lower $0.40 per hour starting price versus $1.10 average.

Select the RTX 3080 for budget-conscious prototyping: at $0.06 per hour average $0.15, it handles small models under 10 GB VRAM with 29.8 TFLOPS FP32 sufficient for quick iterations.

Gaming or lightweight Stable Diffusion runs favor its 760 GB/s bandwidth and 320W TDP in single-GPU setups, avoiding L40S overhead when scale is unnecessary.