L40 vs RTX 4000 Ada

The L40 delivers 90.5 TFLOPS FP32 while the RTX 4000 Ada delivers 26.7 TFLOPS FP32 allowing the L40 to complete floating point operations at a higher rate in scientific workloads. FP16 dense performance on the L40 reaches 181.05 TFLOPS and FP16 with sparsity reaches 362.1 TFLOPS yet the RTX 4000 Ada provides no published FP16 figure so direct comparison remains limited to FP32. Memory bandwidth of 864 GB/s on the L40 versus 360 GB/s on the RTX 4000 Ada permits larger batch sizes before memory stalls occur during training or inference passes. FP8 with sparsity on the L40 reaches 724 TFLOPS while the RTX 4000 Ada reaches 427.6 TFLOPS FP8 with sparsity indicating the L40 holds an advantage in sparse low precision inference.

The L40 suits large scale training jobs that require 48 GB GDDR6 to hold extensive model weights and activations. Its 864 GB/s memory bandwidth supports higher throughput when moving data for batch sizes that exceed the 20 GB limit of the RTX 4000 Ada. The 90.5 TFLOPS FP32 rating further benefits precision sensitive scientific computing tasks.

The RTX 4000 Ada fits power constrained workstations where 130W TDP allows integration into standard desktop systems without specialized cooling. Its 20 GB GDDR6 remains adequate for inference on smaller models or fine tuning tasks that fit within that capacity while consuming less power than the 300W L40.