We'll explore strategies to reduce CPU dependencies within existing hybrid CPU/GPU LAPACK routines, such as those implemented with the open-source MAGMA library. This will be carried out within the context developing an improved generalized eigensolver, written in CUDA Fortran for the open-source Quantum ESPRESSO library. The solver aims to replace offloaded subblock CPU computations within the existing hybrid algorithms with GPU resident subblock computations to limit dependencies on available CPU resources. Performance considerations and strategies used in developing the solver, including the use of profiling tools available within the CUDA toolkit will be covered. Additionally, we'll provide an example developing software using CUDA Fortran.