Google's TurboQuant: How They Cut LLM Memory by 6x Without Losing Accuracy

A plain-English breakdown of the Google Research paper that compresses KV cache by up to 6x with near-zero accuracy loss. No training. No calibration data. Just math. Read the full indepth article on Medium : Link Running large language models is not just expensive. It is wasteful. Every time you send a long prompt, the model stores massive amounts of intermediate data in something called the KV cache. This cache grows with every token. It quietly eats GPU memory, slows responses, and drives up inference costs. Most compression solutions force a tradeoff. You either save memory or you keep accuracy. Pick one. Google's TurboQuant breaks that tradeoff. It compresses the KV cache by up to 6x and, in several benchmarks, performs identically to the full-precision model. That is a different kind of result. This post explains why, in plain English. What Is the KV Cache? Before anything else, you need to understand what TurboQuant is actually compressing. When a language model processes text,