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Transformers are powerful, but are State Space Models even better? 🤔 Aaron Baughman explains how SSMs outperform transformers, handle sequential data, and improve generative AI efficiency. 🚀 Discover how this breakthrough in Machine Learning is solving challenges in memory, scalability, and performance!

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IBM Technology

State Space Models (SSMs) are neural building blocks that efficiently process sequential data by tracking hidden patterns over time through two key equations: a state equation (modeling internal evolution) and an observation equation (mapping to outputs). Unlike transformers that remember everything with O(n²) complexity, SSMs achieve O(n) efficiency by maintaining compact implicit memory. The S4 architecture introduced efficient long-sequence handling, while Mamba added selective attention-like capabilities without transformer costs. Modern hybrid models combining SSMs with transformers achieve superior performance with smaller parameter counts (350M-1B), enabling deployment on consumer hardware while addressing GPU memory bandwidth bottlenecks that plague traditional transformer inference.

What are State Space Models? Redefining AI & Machine Learning with Data