TurboQuant: Shattering the Memory Barrier in Large Language Models

This technique introduces a zero-calibration approach to compress AI vectors into just 2–4 bits by leveraging the geometric properties of high-dimensional space. It significantly enhances LLM inference efficiency by reducing memory overhead for KV caches and embeddings without sacrificing mathematical accuracy.