L40S vs RTX PRO 6000

Dense FP16 throughput reaches 362.05 TFLOPS on the L40S and 503.8 TFLOPS on the RTX PRO 6000 so the latter delivers higher arithmetic intensity for training loops that rely on dense matrix operations. With sparsity the figures become 733 TFLOPS versus 1007.6 TFLOPS allowing the RTX PRO 6000 to accelerate pruned networks by the same proportional margin. Memory bandwidth of 864 GB/s on the L40S compared with 1792 GB/s on the RTX PRO 6000 directly limits sustainable batch sizes because data movement for activations and gradients must stay within those transfer ceilings. FP32 performance of 91.6 TFLOPS on the L40S versus 126 TFLOPS on the RTX PRO 6000 further widens the gap for mixed precision workloads that fall back to single precision reductions.

Choose the L40S when the model you serve, with its KV cache, fits in 48 GB and you do not need the newer card. Its 733 TFLOPS of dense FP8 and 362.05 TFLOPS of dense FP16 are plenty for single-model inference, diffusion and video work, and its 91.6 TFLOPS of FP32 covers rendering and simulation. Expect roughly half the decode speed of the RTX PRO 6000 on bandwidth bound workloads because it has 864 GB/s against 1792 GB/s. It is a PCIe 4.0 card with no NVLink, the same as the RTX PRO 6000 in that respect, so neither is a multi-GPU training part.

The RTX PRO 6000 fits scenarios that require 96 GB GDDR7 and 1792 GB/s bandwidth to accommodate larger context windows or bigger batch sizes during inference. Its 503.8 TFLOPS dense FP16 and 126 TFLOPS FP32 also shorten iteration times for fine tuning runs that fit within the 600W envelope.