For timings, we tested our code on a growing series of three-dimensional water and benzene clusters (geometries available in the supporting information), as shown in Figures and. We performed our tests in the cc-pVDZ, jun-cc-pVDZ, and cc-pVTZ basis sets. In these figures, we present log-log plots, using our timings to calculate the empirical scaling of our algorithm. We used a log-log regression of the walltime (in minutes) compared to the system size (by number of basis functions) to perform our analysis, in order to fit a function of the form t = a • n b , where the t is the run-time, and a is the pre-factor, n the system size, and b the computational scaling. The results of our analysis are presented in Figures and. For each system, across all basis sets, the empirical scalings are all below cubic scaling, with non-diffuse basis sets scaling quadratic or less. Even though the linear scaling regime has not been achieved yet with our system sizes, it remains a drastic improvement from the seventh power scaling of canonical CCSD(T). The results of the analysis performed for the DLPNO-CCSD as well as the DLPNO-(T) components of the computation are presented in the supplementary material. Finally, to assess the limits of the capabilities of our algorithm, we tested our code on an insulin peptide hormone, as show in Figure . The geometry was obtained from the work of Bykov et al., and is also presented in the supplementary material. For this computation, we used the def2-SV(P) basis set of Weigend et al., with 6458 basis functions. In Table , we present the DLPNO-CCSD(T) correlation energy, as well as its various contributions, at both the NormalPNO and TightPNO convergences. In Table , we present timings for the most important portions of the computation (excluding SCF). For these sets of timings, we used 32 CPU cores on a single 2nd generation AMD EPYC Rome (2.9 GHz processing speed, 2000 GB RAM) as part of the Sapelo2 computing cluster at the University of Georgia.