L40S vs Quadro RTX 6000

The L40S outperforms the Quadro RTX 6000 dramatically in compute: FP16 reaches 362 TFLOPS on the L40S versus 16.3 TFLOPS on the Quadro RTX 6000, accelerating AI training and inference by over 22 times. FP32 performance hits 91 TFLOPS on the L40S against 16.3 TFLOPS, benefiting simulation tasks. The FP16 to FP32 ratio on the L40S, 362 to 91 TFLOPS, supports mixed-precision training where inference leverages FP8 at 724 TFLOPS.

Memory bandwidth of 864 GB/s on the L40S allows larger batch sizes than the 672 GB/s on the Quadro RTX 6000: models with high VRAM demands, like those using 48 GB versus 24 GB, process bigger datasets without swapping. In real-world terms, this reduces training epochs for LLMs and enables Stable Diffusion at higher resolutions. Power draw at 350W for the L40S exceeds 260W, but PCIe 4.0 interconnect scales better than NVLink for multi-GPU clouds.

Choose the L40S for AI-driven workloads requiring vast memory: its 48 GB GDDR6X handles large language models that exceed the Quadro RTX 6000's 24 GB GDDR6. Bandwidth at 864 GB/s supports inference at scale, with FP8 at 724 TFLOPS ideal for deployment.

Cloud users benefit from 18 live offers starting at $0.40 per hour, making it viable for training runs leveraging 362 TFLOPS FP16.

The Quadro RTX 6000 suits legacy workstation environments with NVLink interconnect for multi-GPU rendering: its 260W TDP fits power-constrained setups better than the L40S's 350W. Applications certified for Turing architecture, like specific CAD software, avoid recompute on 16.3 TFLOPS FP32.

Without live cloud offers, it appeals to on-premises users avoiding migration costs.