Tether, the issuer of the USDT stablecoin, has announced the launch of QVAC Health, a new private personal wellness platform designed to solve the problem of fragmented data from various wearables and wellness apps.
According to the statement, the solution combines local artificial intelligence (AI), autonomous data analysis, and the principles of Tether’s decentralized infrastructure, which the company has been consistently developing in the cryptocurrency and Web3 sectors.
QVAC Health operates as a sovereign hub that collects data from various sources — biometric sensors, fitness watches, smart rings, nutrition trackers, medical reminders — into a single, fully encrypted, offline dashboard. This allows users to “see the full picture” of their own health without transferring data to large technology platforms.
At the centre of QVAC Health is an interface with local AI that allows you to interact with data in natural language. The user can dictate or enter any event or indicator by text: “feeling sluggish after lunch” or add a workout, symptom, or supplementation — the system instantly organizes everything in a single timeline.
QVAC Health turns the user’s device into a “private intelligent hub.” All AI models are downloaded via P2P technology and processed exclusively locally. The platform can:
Future updates will include a direct BLE connection to some wearables, which will allow reading raw sensor data, bypassing the manufacturer’s API.
The launch of QVAC Health comes amidst Tether’s active expansion in the field of decentralized AI, a field that the company is consistently integrating into the crypto ecosystem.
In November 2025, Tether confirmed a large-scale lease of 20,000 GPUs as part of a partnership with Rumble and Northern Data. The capacity will be used for AI research, development of tools for content creators, and acceleration of the QVAC (“Infinite Intelligence”) platform.
In early December, Tether Data AI introduced the QVAC-fabric-llm infrastructure, the world’s first solution that allows LoRA training of large language models within llama.cpp on any device, from smartphones to servers.
The company emphasized that the project is a “significant step in QVAC’s mission” as it eliminates dependence on large manufacturers and expands access to private, on-premises AI.

