L40S vs Quadro RTX 4000

The L40S demonstrates overwhelming compute superiority over the Quadro RTX 4000: its FP32 throughput reaches 91 TFLOPS, over 12 times the 7.1 TFLOPS of the Quadro RTX 4000, enabling faster training and simulation runs. FP16 performance at 362 TFLOPS on the L40S, versus 7.1 TFLOPS, accelerates mixed-precision AI training where tensor cores shine, reducing epochs from days to hours for large models.

Memory specs further favor the L40S for real-world workloads: 48 GB GDDR6X VRAM supports batch sizes up to six times larger than the Quadro RTX 4000's 8 GB GDDR6 limit, preventing out-of-memory errors in LLM fine-tuning. Bandwidth of 864 GB/s on the L40S doubles the 416 GB/s of the Quadro RTX 4000, speeding data loading and inference latency by minimizing bottlenecks in diffusion models or scientific computations.

Power draw reflects capability differences: the L40S at 350W TDP sustains peak performance longer than the 160W Quadro RTX 4000, which throttles under sustained loads. FP8 capability at 724 TFLOPS on the L40S adds inference efficiency absent in the older GPU.

The L40S excels in demanding AI tasks requiring substantial VRAM: its 48 GB capacity handles large language models during training or inference, where the Quadro RTX 4000's 8 GB falls short. High FP16 performance of 362 TFLOPS and bandwidth of 864 GB/s enable efficient batch processing in Stable Diffusion or fine-tuning.

Cloud users benefit from 18 live offers starting at $0.40 per hour, ideal for scalable workloads on PCIe 4.0 interconnects.

The Quadro RTX 4000 fits low-intensity professional visualization or legacy CAD software: its 160W TDP consumes half the power of the L40S's 350W, suiting edge deployments or power-constrained clouds. At a consistent $0.56 per hour average across 5 offers, it provides cost-effective 7.1 TFLOPS FP32 for small datasets under 8 GB VRAM.

Users with Turing-optimized code avoid recompilation, leveraging the GPU's workstation heritage without needing Ada Lovelace features.