TurboQuant: Redefining AI efficiency with extreme compression

Google Research's TurboQuant introduces a theoretically optimal approach to vector quantization that eliminates the memory overhead traditionally associated with compression, achieving high compression ratios with zero accuracy loss. This matters because KV cache bottlenecks are one of the biggest practical constraints on deploying large language models at scale — solving this efficiently could dramatically reduce inference costs and latency across the industry. The techniques (PolarQuant, QJL) also extend to vector search, meaning improvements could ripple through search infrastructure and retrieval-augmented generation pipelines.