On March 24, 2026 Amir Zandieh and Vahab Mirrokni from Google Research published an article on TurboQuant. TurboQuant is a compression algorithm to address the challenge of memory overhead in key-value storage for AI models with zero accuracy loss. The article said that TurboQuant achieves perfect downstream results across all benchmarks while reducing the key value memory size by a factor of at least 6x. This could reduce memory requirements in AI inference workloads, for instance, for context aware memory storage. But by enabling AI with lower memory and storage requirements, we make that memory and storage even more useful and this will likely increase AI workflows, particularly on-premise. This could increase the memory and storage demand for implementing local AI inference.

Full report : TurboQuant: Redefining AI efficiency with extreme compression.