Because of the potential advantages, using a relatively lower precision is a common approach for performance optimization in machine learning and high-performance computing and has also been applied in various scientific areas such as numerical linear algebra, molecular dynamics, and lattice quantum chromodynamics. In quantum chemistry, some mixed-precision (MP) electron repulsion integral evaluation methods have been implemented. The main idea is that the upper bound of the integrals can be previously identified. Thus, integrals with a rather small upper bound can be evaluated in single precision, while others in double precision. To make better use of this dynamic precision scheme, Parrish et al. developed a "difference self-consistent field (dSCF)" approach, in which a difference density matrix instead of the actual density matrix is used. The difference density matrix is a numerically smaller matrix, resulting in a smaller integral upper bound. With the support of these mixed-precision integral methods, some mixed-precision Hartree-Fock and density functional theorey (DFT) methods have been developed, as well as some single-or mixed-precision post-Hartree-Fock methods, including the MP2 method within resolution-ofthe-identity (RI) and Cholesky decomposition (CD) schemes, coupled cluster with single and double excitation (CCSD) and perturbative triples correction (CCSD(T)). In 2018, Pokhilko et al. presented an implementation of coupled cluster and equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) in single precision. They claim that the chemical accuracy can be maintained merely using single precision, while the full double precision accuracy can be recovered with a few cleanup iterations. Wang et al. followed this idea and implemented the CCSD and CCSD(T) method with pure single precision on consumer GPUs and achieved 4-14 speed-up for CCSD calculation and 12-20 speed-up for (T) correction.