Mamba-2, developed by Goomba AI Lab, is a novel sequential model designed to enhance the efficiency and performance of sequential models within the machine learning community. It utilizes the Structural State Space Dual (SSD) model, combining the advantages of state space models (SSM) and attention mechanisms, providing a more efficient training process and larger state dimensionality. Mamba-2's design allows for matrix multiplication during training, thereby improving hardware efficiency. Furthermore, Mamba-2 demonstrates strong performance in tasks like multi-query associative memory (MQAR), showcasing its potential in handling complex sequential processing tasks.