Tether releases a cross-platform BitNet LoRA framework, supporting consumer-grade GPUs and large model training and inference on smartphones

Tether CEO Paolo Ardoino revealed that the Tether AI team has released a new version of QVAC Fabric, which integrates the cross-platform BitNet LoRA framework, enabling the training and inference of billion-parameter large models on consumer-grade GPUs and smartphones.

The new version of QVAC Fabric LLM has achieved cross-platform operation of BitNet LoRA fine-tuning and inference on AMD, Intel, Apple Metal, and mobile GPUs for the first time. On flagship devices, GPU inference speed is improved by 2 to 11 times compared to CPU, and memory usage is reduced by up to 90% compared to full precision models. The Tether team has completed fine-tuning of models with up to 3.8 billion parameters on flagship smartphones such as Pixel 9, S25, and iPhone 16, and has achieved fine-tuning of models with up to 13 billion parameters on the iPhone 16. The related code has been open-sourced to GitHub.

This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.