L40S vs Quadro RTX 8000

Compute performance defines the core disparity between the L40S and Quadro RTX 8000. The L40S delivers 362 TFLOPS in FP16 and 91 TFLOPS in FP32, dwarfing the Quadro RTX 8000's 16.3 TFLOPS across both precisions. This translates to training large models up to 22 times faster on the L40S in FP16-heavy workflows, reducing epoch times from days to hours.

Inference benefits from the L40S's FP8 capability at 724 TFLOPS, enabling high-throughput serving of quantized models unavailable on the Quadro RTX 8000. Memory bandwidth plays a key role: 864 GB/s on the L40S versus 672 GB/s permits batch sizes 28 percent larger, minimizing out-of-memory errors and boosting GPU utilization in data-parallel tasks.

Power draw reflects capability differences, with the L40S at 350W TDP sustaining peaks longer than the Quadro RTX 8000's 260W. Interconnect varies too: PCIe 4.0 on the L40S suits single-node clouds, while NVLink on the Quadro RTX 8000 aids multi-GPU legacy setups, though overall throughput lags significantly.

The L40S stands out for modern AI and machine learning workloads demanding high throughput. Its 362 TFLOPS FP16 performance accelerates LLM training and fine-tuning, while 724 TFLOPS FP8 optimizes inference for deployed models. With 864 GB/s bandwidth, it handles large batches efficiently in cloud environments, available from $0.40 per hour.

Select the L40S for Stable Diffusion or scientific simulations requiring FP32 at 91 TFLOPS, far exceeding the Quadro RTX 8000's capabilities.

The Quadro RTX 8000 fits legacy professional visualization or CAD applications optimized for Turing architecture. Its NVLink interconnect enables multi-GPU configurations for tasks like rendering where PCIe 4.0 falls short. At 260W TDP, it consumes less power than the L40S's 350W, suiting constrained data centers.

Choose it if on-premises hardware already exists, as no cloud offers are available, avoiding migration costs for non-AI workloads.