According to Tether CEO Paolo Ardoino, the Tether AI team has released a new version of QVAC Fabric, integrating the cross-platform BitNet LoRA framework to enable training and inference of billion-parameter large models on consumer-grade GPUs and smartphones. The new QVAC Fabric LLM achieves, for the first time, cross-platform BitNet LoRA fine-tuning and inference on AMD, Intel, Apple Metal, and mobile GPUs. On flagship devices, GPU inference speeds are 2 to 11 times faster than CPU, with memory usage reduced by up to 90% compared to full-precision models. The Tether team has successfully fine-tuned models with up to 3.8 billion parameters on flagship phones such as the Pixel 9, S25, and iPhone 16, and achieved fine-tuning of models with up to 13 billion parameters on the iPhone 16. The related code has been open-sourced on GitHub.
Tether Launches Cross-Platform BitNet LoRA Framework for Consumer GPUs and Smartphones
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Summary
Tether announced the launch of the updated QVAC Fabric by Tether AI, featuring a cross-platform BitNet LoRA framework. This framework enables billion-parameter models to run on consumer GPUs and smartphones. The new LLM supports AMD, Intel, Apple Metal, and mobile GPUs, delivering inference speeds 2 to 11 times faster than CPU with 90% less memory usage. Tether has fine-tuned 3.8-billion- and 130-billion-parameter models on devices such as the iPhone 16. The code is now open-sourced on GitHub. On-chain news highlights this major update, and new token listings may benefit from this AI advancement.
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