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Odaily reported that, according to an official announcement, Tether has launched a cross-platform BitNet LoRA fine-tuning framework within QVAC Fabric, enabling training and inference optimization for Microsoft BitNet (1-bit LLM). This framework significantly reduces computing power and memory requirements, allowing billion-parameter models to be trained and fine-tuned on laptops, consumer-grade GPUs, and smartphones. This solution achieves, for the first time, BitNet model fine-tuning on mobile GPUs (including Adreno, Mali, and Apple Bionic). Tests show that a 125M parameter model can be fine-tuned in about 10 minutes, while a 1B model can be completed in roughly 1 hour, and even on mobile devices, it can scale up to a 13B parameter model. Additionally, the framework supports heterogeneous hardware such as Intel, AMD, and Apple Silicon, and for the first time enables 1-bit LLM LoRA fine-tuning on non-NVIDIA devices. In terms of performance, BitNet models achieve inference speeds on mobile GPUs that are 2 to 11 times faster than CPUs, while memory usage is reduced by up to 77.8% compared to traditional 16-bit models. Tether stated that this technology is expected to break the dependence on high-end computing power and cloud infrastructure, promote the decentralization and localization of AI training, and provide a foundation for new application scenarios such as federated learning.

Foresight News reports that on Polymarket, the probability of the Federal Reserve cutting interest rates once by 25 basis points this year is 30%, the probability of no rate cut is 23%, and the probability of two rate cuts totaling 50 basis points is 23%. Currently, the total trading volume in this prediction market exceeds $10.25 million.

格隆汇 March 17｜Montreal Bank stated that it plans to open more than 130 financial centers in California, USA, and about 15 financial centers in Arizona over the next five years. After selling several branches across the United States last year, the bank hopes to expand its business in the western United States.