Tether’s AI Research Group announced the open-source release of TurboQuant, a production-ready implementation of the Google Research algorithm aimed at dramatically decreasing memory requirements in artificial intelligence models. The company reported that this technology integrates directly into QVAC Fabric, Tether’s local AI engine.
This release addresses memory consumption, one of the main obstacles to running advanced AI on mass-consumer devices like phones and laptops. By compressing storage demands up to five times without altering model performance, the development reduces reliance on centralized data centers. Paolo Ardoino, CEO of Tether, highlighted that the initiative seeks to prevent the evolution of AI from being conditioned solely by corporations with massive hardware infrastructures.
Integrated into the new QVAC SDK 0.12.0, this tool drives private data processing locally and securely. For independent developers and startups in the ecosystem, what follows is the implementation of these quantization profiles to deploy more efficient decentralized applications.
Source: https://goo.su/qUkPjsh
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