KV-cache compression

A new research paper introduces a self-pruning transformer architecture that claims to achieve extreme compression of the KV-cache, a major memory bottleneck in running large language models, using a mechanism called universal attention. If the claims hold up, it could make it cheaper and faster to serve long-context models, a persistent pain point for AI developers. Early readers are weighing in on whether the technique generalizes beyond benchmark conditions.