Consequently, the impressive acceleration of almost 300×, illustrated in Table , for matrix multiplications using reduced precision on GPUs can be readily exploited by AI, while QM simulations typically require double precision where only a tenfold speedup can be achieved for matrix multiplications using vendor libraries. For kernels written in OpenMP, OpenACC, or OpenCL, lacking the tensor core boost, an acceleration factor of less than five is usually expected in QM simulations. In the current QM landscape, many algorithms exhibit low arithmetic intensity, largely due to their historical development and the limitations of earlier hardware. As a result, the potential advantages of GPUs are not always clear for these traditional algorithms. To fully harness the computational power of modern GPUs, particularly their ability to perform dense matrix multiplications at reduced precision, new algorithms must be developed. These new approaches would need to align more closely with GPU strengths, optimizing for parallelism and reduced precision where possible, rather than relying on the double-precision calculations and sparse matrix operations that have characterized QM methods to date.