Mem-GF introduces a memory-efficient graph filtering method that approximates polynomial graph filters using Krylov subspaces, eliminating the need to store the full item similarity graph. It achieves up to 5.74× lower memory usage and 4.38× faster runtime while maintaining superior recommendation accuracy compared to state-of-the-art methods, scaling effectively to datasets with tens of millions of interactions.