AI is advancing, with Transformers leading the charge. However, State Space Models (SSMs), like Mamba, are emerging as an alternative with comparable performance and faster speeds than Transformers. Mamba tackles the quadratic bottleneck issue in Transformers, providing linear scaling in sequence length and fast inference. The Selective State Space Model in Mamba enhances efficiency and effectiveness by focusing on relevant information and discarding less important data. By dynamically compressing and selecting data into the state, Mamba offers a solution that pushes the boundaries of the effectiveness/efficiency tradeoff. Overall, Mamba presents a promising approach to sequence models, challenging the dominance of Transformers in AI.
https://thegradient.pub/mamba-explained/