This paper presents an approach to accelerate the Meta-blocking phase in entity resolution (ER) by leveraging GPU computational power. We enhance the performance of conventional meta-blocking algorithms by utilizing sparse matrix representations of block collections. Our proposed solution remains orthogonal to existing blocking and matching techniques, ensuring that their effectiveness is not compromised. By converting a standard block collection to a one-hot encoded sparse matrix and implementing block purging, block filtering, and edge pruning on GPUs, we achieve up to 40 \(\times \) speedups compared to CPU-based implementations.