L40S vs RTX 2080

The FP16 dense figure of 362.05 TFLOPS on the L40S exceeds the FP16 dense figure of 40.3 TFLOPS on the RTX 2080 by a factor derived directly from those values. This gap affects training and inference because higher dense FP16 throughput supports larger models or faster iteration when sparsity is not applied. When sparsity is used the L40S reaches 733 TFLOPS FP16 while the RTX 2080 figure remains at its dense value of 40.3 TFLOPS FP16. Memory bandwidth of 864 GB/s on the L40S versus 448 GB/s on the RTX 2080 influences batch sizes because greater bandwidth allows more data movement per second during tensor operations. The FP32 value of 91.6 TFLOPS on the L40S compared to 10 TFLOPS on the RTX 2080 further separates the GPUs for precision sensitive scientific workloads.

The L40S suits workloads that require 48 GB GDDR6 and FP16 dense throughput of 362.05 TFLOPS. Its 864 GB/s bandwidth and 350 W TDP support sustained operation on large batches where the RTX 2080 memory limit of 8 to 11 GB would constrain execution. Users select the L40S when PCIe 4.0 interconnect and FP8 dense performance of 733 TFLOPS become relevant for inference pipelines.

The RTX 2080 fits environments limited to 215 W TDP or systems that already incorporate NVLink. Its 10 TFLOPS FP32 and 448 GB/s bandwidth remain adequate for smaller models that fit within 8 to 11 GB GDDR6. The lower power draw and PCIe 3.0 compatibility make the RTX 2080 preferable when existing hardware cannot accommodate the 350 W requirement of the L40S.