Google Research unveiled TurboQuant, a new compression algorithm designed to dramatically reduce the memory footprint of large language models (LLMs) while also increasing inference speed. By targeting the key‑value cache—often described as a digital cheat sheet—TurboQuant can cut memory usage by up to six times and deliver performance gains of around eight times without sacrificing model quality. The technique relies on a novel PolarQuant conversion that represents vectors in polar coordinates, preserving essential information while enabling aggressive compression. Read more →

Google Research announced TurboQuant, an AI memory compression technique that dramatically reduces the working memory needed for inference. Using vector quantization, the method can shrink the KV cache by at least six times without harming performance. The breakthrough, likened by some online to the fictional “Pied Piper” compression tool, will be presented at the ICLR 2026 conference. While still in the lab stage, TurboQuant promises cheaper AI operation and could help address memory bottlenecks in AI systems. Read more →