KV-cache compression

A new paper on arXiv introduces a self-pruning transformer architecture that achieves extreme compression of the KV-cache using a universal attention mechanism. The approach would let large language models run with far less memory, a major cost driver in inference. The work is drawing attention among AI engineers discussing efficiency improvements for serving models at scale.