1.【消息称英伟达拟将Rubin处理器CoWoS中间基板材料替换为碳化硅，台积电正推进相关研发】为 提升性能，英伟达在新一代Rubin处理器的开发蓝图中，计划把CoWoS先进封装环节的中间基板材 料，由硅换成碳化硅（SiC）。目前台积电邀请各大厂商共同研发碳化硅中间基板的制造技术，英伟 达第一代Rubin GPU仍会采用硅中间基板。但由于英伟达对性能进步的要求极高，当芯片内产生的热 超过极限，就必须采用碳化硅，最晚2027年，碳化硅就会进入先进封装。（新浪财经） 2. 【国内首个无屏蔽、移动式磁共振成像系统获批】9月4日，人工智能医学影像企业深至科技宣 布，旗下子公司杭州微影医疗科技有限公司自主研发的wMR-510系列移动式头部磁共振成像系统获 得国家药监局颁发的第三类医疗器械注册证及生产许可证，深至科技也成为中国首家掌握无屏蔽、移 动式MRI核心技术并成功实现产品注册上市的企业。（新京报） 3. 【海光信息将开放CPU能力，推动国产AI算力生态建设】记者从海光信息方面获悉，该公司将开放 CPU能力，向产业生态伙伴提供直连IP、开放协议及定制化指令集，实现与国内AI芯片的高效衔接， 推动应用顺畅对接与调用。据 ...

Group 1 - The Core Point: The semiconductor sector is experiencing growth, driven by advancements in AI chip design and increasing demand for computing power in China [1][2] - The DeepSeek-V3.1 model was officially released on August 21, enhancing the design capabilities for domestic chips and supporting complex model inference [1] - As of June 30, 2025, China has 4.55 million 5G base stations and 226 million gigabit broadband users, positioning the country second globally in computing power [1] Group 2 - The Kexin 100 ETF fund closely tracks the Shanghai Stock Exchange's Kexin 100 Index, which includes 100 medium-cap stocks with good liquidity [2] - As of July 31, 2025, the top ten weighted stocks in the Kexin 100 Index account for 23.52% of the index, with notable companies including BoRui Pharmaceutical and BeiGene [2]

Core Viewpoint - The article discusses the challenges and solutions related to the adaptation of large models to domestic chips in China, emphasizing the need for a collaborative platform to bridge the gap between model development and chip compatibility [2][3][35]. Group 1: Model Adaptation Challenges - The successful deployment of large models requires overcoming three main hurdles: adapting the inference engine, adapting the computing platform, and adapting the upper scheduling for business system integration [9][10]. - Current tools for supporting large model inference and adaptation are diverse, but the challenge lies in effectively connecting and coordinating these fragmented tools and experiences [11]. Group 2: Collaboration Initiatives - The "Model Inference Adaptation Collaboration Plan" was launched by the Modelers community to gather developers, algorithm teams, chip manufacturers, and inference tool partners to build an open-source collaborative ecosystem [5][30]. - The community upgraded its "Mirror Center" to a "Tool Center," elevating the importance of the toolchain to be on par with model libraries and datasets [13][14]. Group 3: Community Engagement and Development - The community introduced a "Collaboration Space" where all users can submit pull requests (PRs) to contribute to documentation, adaptation code development, and optimization of inference configurations [20][29]. - The collaboration mechanism aims to aggregate dispersed adaptation efforts into a unified platform, allowing for easy downloading and secondary development [29]. Group 4: Industry Partnerships - The community collaborates with various domestic computing power manufacturers to provide developers with hardware, tools, and technical support [31]. - The initiative also integrates a diverse ecosystem of adaptation and inference software, helping developers quickly master the adaptation toolchain [32]. Group 5: Future Prospects - The "Adaptation Plan" will continue to be open for more chip manufacturers, model developers, and developers to join, with a focus on standardizing adaptation technology [34]. - If successful, this collaborative mechanism could address the critical "coordination shortfall" in the domestic chip ecosystem, facilitating the systematic implementation of models on chips [35].