TLDR
- Tether released a new AI training framework called QVAC Fabric that works on smartphones and consumer GPUs
- The system uses Microsoft’s BitNet architecture and LoRA techniques to cut memory use by up to 90%
- Models with up to 13 billion parameters can be fine-tuned on an iPhone 16
- The framework supports AMD, Intel, Apple Silicon, and mobile chips from Qualcomm and Apple
- This is part of Tether’s broader shift from stablecoin issuer to AI and infrastructure company
Tether, the company behind the USDT stablecoin, has released a new AI training framework that lets users fine-tune large language models on everyday devices like smartphones and non-Nvidia GPUs.
JUST IN: Tether’s QVAC unveils BitNet LoRA framework, bringing large-scale AI training to consumer GPUs and smartphones. pic.twitter.com/kiQOZp744L
— SwanDesk (@SwanDesk) March 17, 2026
The framework is part of Tether’s QVAC platform and uses Microsoft’s BitNet architecture along with a technique called LoRA. Together, these cut the memory and computing power needed to train AI models, making it possible to run them on devices that would normally struggle with such tasks.
Tether says the system reduces memory requirements by up to 90% compared to standard 16-bit models. That means larger AI models can run on phones, laptops, and budget GPUs.
The company says its engineers fine-tuned models with up to 1 billion parameters on smartphones in under two hours. Smaller models took just minutes.
On flagship phones like the iPhone 16, Pixel 9, and Galaxy S25, the team pushed fine-tuning to models as large as 3.8 billion parameters. On the iPhone 16 specifically, they reached 13 billion parameters.
The framework works across a wide range of hardware. It supports AMD, Intel, and Apple Silicon chips, as well as mobile GPUs from Qualcomm and Apple.
Mobile GPUs running BitNet models can operate between 2 and 11 times faster than CPU-only setups, according to Tether.
On-Device AI Without the Cloud
One use case Tether highlights is federated learning. This allows AI models to be updated across many devices without sending personal data to central servers, reducing reliance on cloud infrastructure.
This approach means users could personalize AI models locally, keeping their data on-device rather than uploading it to a third party.
The code behind QVAC has been open-sourced on GitHub, allowing developers and smaller labs to build on it.
Crypto Companies Are Investing in AI Infrastructure
Tether’s move fits a broader trend across the crypto industry. Many companies that started in digital assets are now putting money into AI and computing infrastructure.
In September 2024, Google took a 5.4% stake in Cipher Mining as part of a $3 billion deal tied to AI data center capacity.
Bitcoin miner IREN announced plans in December 2024 to raise around $3.6 billion for AI infrastructure.
In February 2025, HIVE Digital Technologies reported record revenue of $93.1 million, driven by growth in AI and high-performance computing.
Core Scientific secured a $500 million loan facility from Morgan Stanley in March, with the option to expand it to $1 billion.
On the same day Tether made its announcement, World, the identity network co-founded by OpenAI’s Sam Altman, launched AgentKit. The toolkit allows AI agents to verify they are linked to a real human using World ID and make payments through the x402 micropayments protocol.
Also in February, Alchemy launched a system allowing AI agents to access blockchain data services using USDC on Base.
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