Tether has open-sourced a production-ready implementation of Google’s TurboQuant algorithm, enabling advanced AI models to run on local devices with up to 5x lower memory requirements while reducing reliance on cloud infrastructure.
Tether’s AI Research Group has open-sourced a production-ready implementation of Google’s TurboQuant algorithm, bringing the AI memory-efficiency breakthrough into real-world deployments and enabling broader adoption of local and decentralised AI.
Designed to tackle one of the biggest barriers to running advanced AI models locally, TurboQuant significantly reduces memory consumption while preserving model performance. According to researchers, the technology can cut AI memory requirements by up to 5x, making it easier to run capable AI systems on laptops, smartphones, consumer GPUs, and edge devices.
The open-source release has been integrated into QVAC Fabric, Tether’s local AI engine, and is included in QVAC SDK 0.12.0. It ships with a complete quantisation pipeline, framework integrations, documentation, and deployment profiles aimed at production use.
“Google’s research showed that AI memory could be compressed far more efficiently than most people assumed. Our work brings that breakthrough into production software that developers, startups, and users can actually build with,” said Paolo Ardoino, CEO of Tether.
Tether believes the technology can help move AI workloads away from centralised cloud infrastructure by enabling longer context windows and improved performance on local hardware. The company says the approach supports AI systems that process long conversations, handle large files, retain project context, support software development workflows, and work with private data locally.
“If long context AI only works inside the largest data centers, then AI will be shaped by whoever owns the most hardware. TurboQuant changes what local AI can do by making memory less of a wall,” Ardoino added.
Tether positions the release as a step towards more accessible, decentralised, and user-controlled AI.
