L40S vs Quadro P4000

The L40S dominates in raw compute: 362 TFLOPS FP16 enables rapid AI training and inference, far exceeding the P4000's 5.3 TFLOPS, which limits it to smaller models or slower runs. The FP16 to FP32 ratio on the L40S (362 to 91 TFLOPS) supports mixed-precision training efficiently, while the P4000's equal 5.3 TFLOPS in both suits basic FP32 tasks but cannot handle large-scale deep learning.

Memory differences reshape workloads profoundly: 48 GB VRAM on the L40S accommodates massive models or large batch sizes, unlike the P4000's 8 GB cap, which forces model sharding or reduced batches. Bandwidth of 864 GB/s versus 243 GB/s accelerates data movement, reducing bottlenecks in training loops and enabling higher throughput in inference serving. The L40S's FP8 at 724 TFLOPS further boosts quantized inference, absent on the older P4000.

Choose the L40S for AI and machine learning tasks demanding high throughput: its 362 TFLOPS FP16 and 48 GB VRAM excel in training large language models or running Stable Diffusion at scale. Cloud renters benefit from PCIe 4.0 interconnect and 864 GB/s bandwidth for multi-GPU setups, despite 350W TDP.

It suits production inference where FP8 at 724 TFLOPS minimizes latency for high-volume queries.

The Quadro P4000 fits low-power, budget visualization or legacy CAD workflows: 105W TDP consumes far less energy than 350W, ideal for edge or small-scale rendering. At $0.51 per hour average, it provides 5.3 TFLOPS FP32 for professional apps without overkill.

Select it when 8 GB VRAM suffices for non-AI tasks and PCIe compatibility without modern interconnect needs.