L40 vs Quadro P6000

The FP32 figure of 90.5 TFLOPS on the L40 exceeds the 12.6 TFLOPS on the Quadro P6000 by a factor of seven which translates to faster matrix operations in training workloads. Dense FP16 performance on the L40 measures 181.05 TFLOPS while the Quadro P6000 figure remains unpublished by the vendor so direct comparison stays limited to FP32. With sparsity the L40 reaches 362.1 TFLOPS in FP16 allowing accelerated inference on compatible models. Memory bandwidth at 864 GB/s on the L40 doubles the 432 GB/s on the Quadro P6000 which permits larger batch sizes before hitting memory limits during data parallel operations. The 48 GB capacity on the L40 versus 24 GB on the Quadro P6000 further extends the feasible model scale in both dense and sparse tensor computations.

The L40 suits LLM inference and fine tuning because its 48 GB GDDR6 memory and 864 GB/s bandwidth accommodate larger context windows than the 24 GB and 432 GB/s on the Quadro P6000. Dense FP8 performance at 362 TFLOPS on the L40 enables efficient low precision inference paths unavailable on the older GPU. Users select the L40 for scientific computing tasks that benefit from the 90.5 TFLOPS FP32 rating and PCIe 4.0 interconnect.

Its 250 W TDP draws less power than the 300 W of the L40 in sustained desktop workloads. Organizations retain the Quadro P6000 when existing PCIe 3.0 systems cannot utilize the newer interconnect on the L40.