[Tether Releases Cross-Platform BitNet LoRA Framework, Supporting Consumer-Grade GPUs and Smartphone-Based Large Model Training and Inference] Deep Tide TechFlow reports that on March 17, according to Tether CEO Paolo Ardoino, the Tether AI team released a new version of QVAC Fabric, integrating the cross-platform BitNet LoRA framework. This enables the training and inference of billion-parameter-scale large models on consumer-grade GPUs and smartphones. The new QVAC Fabric LLM has, for the first time, achieved cross-platform operation of 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, and memory usage is 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 smartphones 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.