Olive Song describes MiniMax M3's sparse-attentionSparse attention reduces transformer computation by allowing each token to attend to a selected subset of other tokens instead of comparing with every token. design, separating an index branch from the main attention computation. The approach learns which blocks deserve attention while preserving useful diversity across key-value heads, rather than selecting tokens without regard to the underlying hardwareAI compute infrastructure is the hardware, facilities, networks, power, cooling, storage, and software used to train and run AI models..
Efficient attention is also an implementation problem. Block-level selection, contiguous memory access and the overhead of top-k selection all affect whether theoretical savings become faster inferenceAI inference is the process of running a trained model on new input to produce a prediction, classification, generated response or action.. Song explains the architectural choices alongside those practical constraints.
The second half covers native multimodalityA multimodal model can process or generate more than one kind of data, such as text, images and audio.. Training vision and languageA vision-language model jointly processes images and language so it can describe, answer questions about or act on visual information. together from the beginning aims to avoid the degradation that can arise when vision is bolted onto a mature language model. Attention-map fusion and cross-frame visual processing extend this design to video while balancing representation quality against compute cost.
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