Stanford CS25: Transformers United V6 I On the Tradeoffs of State Space Models and Transformers
Albert Gu analyzes the fundamental tradeoffs between State Space Models (SSMs) and Transformers, framing SSMs as "brain-like" fixed-size state compressors that enable linear inference complexity versus Transformers' "database-like" KV cache approach that scales quadratically but enables precise retrieval.