Each QM method relies on distinct computational techniques (e.g., fast Fourier transforms, matrix-vector operations, diagonalization of sparse or dense matrices), which determine the scaling behavior, while the specific hardware implementation governs the prefactor associated with this scaling. For instance, matrix multiplication kernels are central to CC methods that utilize large tensors, enabling efficient use of tensor cores and resulting in a relatively small prefactor despite the high scaling complexity. On the other hand, although QMC methods are inherently massively parallel, they often operate on relatively small matrices, leading to a large prefactor; therefore, scaling these approaches on accelerators requires significant effort .