MoBA is a trainable block sparse attention mechanism for transformer-based LLMs where query tokens learn to attend to the most relevant key-value blocks, reducing quadratic complexity of traditional full attention. It introduces a parameter-less top-k gating mechanism that dynamically selects the most informative blocks per query, enabling efficient long-context processing. The architecture allows seamless transitions between full and sparse attention modes during training and inference.