Tether's AI Research Group has released an open-source version of TurboQuant, a Google Research algorithm aimed at significantly reducing AI memory usage. This technology, now integrated into Tether's QVAC Fabric AI engine, includes a comprehensive quantization pipeline and deployment profiles for practical applications. TurboQuant addresses the challenge of high memory consumption in AI systems, enabling them to run efficiently on local devices like laptops and phones by reducing memory demands up to fivefold while maintaining performance. Tether CEO Paolo Ardoino highlighted the importance of this development, stating that TurboQuant allows AI tools to process extensive data locally, enhancing their contextual awareness and reducing reliance on cloud infrastructure. This advancement supports Tether's vision of decentralizing AI workloads, facilitating longer context windows and improved performance on personal devices and local networks.