Core Insights - The article discusses the KTransformers project, which allows for low-cost fine-tuning of large models using local resources, specifically highlighting the use of 2 GPUs and 2 CPUs for fine-tuning the DeepSeek 671B and Kimi K2 1TB models [1][4]. Group 1: KTransformers Overview - KTransformers is an open-source project that has garnered significant attention for its ability to run large models locally, appealing to users interested in personalized AI applications [2][4]. - The project aims to provide a cost-effective and high-performance solution for fine-tuning large models, which is crucial for the practical deployment of AI technologies [4]. Group 2: Key Contributors - Professor Zhang Mingxing from Tsinghua University is a key advisor for the KTransformers project, with a strong background in computer systems and numerous publications in top-tier conferences [6]. - Li Peilin, a core participant in the KTransformers project, is currently studying at Northwestern Polytechnical University and will pursue a PhD at Tsinghua University, contributing to the development of fine-tuning technologies [9]. Group 3: Upcoming Events - A live session is scheduled for the following evening at 19:00, inviting participants to engage in practical discussions about using KTransformers and its applications [5][10].