Characterizing State Space Model and Hybrid Language Model Performance with Long Context

Understand

Emerging applications such as AR are driving demands for machine intelligence capable of processing continuous and/or long-context inputs on local devices.

• However, currently dominant models based on Transformer architecture suffers from the quadratic computational and memory overhead, which hinders applications required to process long contexts.
• This has spurred a paradigm shift towards new architectures like State Space Models (SSMs) and SSM-Transformer hybrid models, which provide near-linear scaling.
• The near-linear scaling enabled efficient handling of millions of tokens while delivering high performance in recent studies.