A recent research paper introduces CODA, a novel framework that optimizes Transformer models by rewriting their computational blocks as GEMM-epilogue programs. This technique is designed to enhance hardware efficiency by fusing operations and reducing memory access overhead during neural network execution. Consequently, the proposed method offers a promising strategy for accelerating both the training and inference speeds of large-scale AI systems.