登录新浪财经APP 搜索【信披】查看更多考评等级 出品：新浪财经创投Plus 编辑整理：shu 基于公开数据不完全统计，2025年7月新增登记的私募股权、创业投资基金管理人共16家，较6月激增77.8%，达到2024年7月的四倍；新增备案私募股权投 资基金、创业投资基金分别为130只、245只，合计同比增长7.1%，环比下降3.4%。 国内一级股权投资市场共发生552起融资事件，同比、环比分别增长5.1%、11.7%；披露总融资额约717.56亿元，较2024年7月提高142.0%，与2025年6月相比 涨幅超100%；平均单笔融资额接近1.3亿元，创下近7个月内最高点。 新浪财经创投Plus本月聚焦市场活跃度较高的3家机构，围绕其投资节奏、阶段、行业偏好以及被投项目等方面展开解读。 7月投资事件节奏强势反弹 跟投MOSS大模型创始人公司 从投资阶段来看，奇绩创坛作为孵化器重点关注早期阶段投资，其参投的天使轮项目约占比55.6%、种子轮占比16.7%、A轮占比22.2%。从所关注的行业赛 道来看，奇绩创坛本月的投资一半投向人工智能行业，其中智能机器人、AI通用应用获投公司合计约占三分之二。由国内首个对话式大型 ...

Core Insights - Qingcheng Jizhi, a leading player in the AI infrastructure sector, has successfully completed a new financing round exceeding 100 million yuan, marking its third round of funding within a year [1][3] - The investment was led by a prominent industry player, with participation from various influential capital sources in the computing power industry, indicating strong market interest in AI infrastructure [1][4] - The CEO of Qingcheng Jizhi highlighted that the funding will enhance the company's capabilities in product development, ecosystem building, and market expansion [2][4] Company Overview - Qingcheng Jizhi was established in December 2023 and focuses on developing intelligent computing system software, acting as a crucial link between intelligent computing and applications [3][6] - The company is led by a team from Tsinghua University, with its CEO being a PhD graduate from the same institution [3][6] Financing Strategy - The company has adopted a "small steps, quick runs" financing strategy due to the rapid changes in the AI infrastructure industry and the high demand for cash flow to support product development [4][6] - This strategy has attracted multiple investors who are optimistic about the company's technological capabilities [4][5] Technological Advancements - Qingcheng Jizhi's software solutions provide a full-stack approach to optimize underlying computing power, enhancing model training and inference efficiency while reducing costs for AI application development [6][7] - The company has developed the "Bagualu" high-performance model training system, which has shown significant acceleration in training tasks on large-scale domestic computing clusters [6][7] - The "Chitu" inference engine has been optimized for domestic computing power, achieving low latency and high throughput, thus supporting diverse application scenarios [6][7] Market Demand - The demand for computing power in the AI market has shifted from primarily training large models to a significant increase in inference power requirements [7][8] - Qingcheng Jizhi is addressing the diverse needs of clients, from small enterprises seeking cost-effective solutions to large organizations requiring comprehensive computing power solutions [8]

Core Insights - Tsinghua-affiliated AI company Qingcheng Jizhi has recently completed over 100 million yuan in financing, less than six months after its previous round [1] - The latest funding round was led by a well-known industry player, with participation from various notable investment institutions [1] Company Overview - Qingcheng Jizhi focuses on developing intelligent computing system software, acting as a crucial bridge between intelligent computing and AI applications [1] - The company's software efficiently links underlying hardware computing power with upper-layer AI model training, inference, and application needs, facilitating seamless collaboration among different hardware devices [1] Technological Advancements - The company has achieved significant improvements in training efficiency for domestic chips, which will enhance the utilization and performance of domestic computing resources while reducing costs for enterprises [2] - Qingcheng Jizhi's "Bagualu" high-performance large model training system has been validated on multiple large-scale domestic computing clusters, showing notable acceleration in training tasks for dense models and mixed expert models [2] - The "Chitu" inference engine, developed by Qingcheng Jizhi, is optimized for domestic computing, offering low latency, high throughput, and low memory usage, thus meeting diverse intelligent computing needs [3] Strategic Collaborations - The company collaborates with Tsinghua University to optimize key aspects of model algorithms and system design, enhancing the overall efficiency of large model training [2] - The open-source Chitu inference engine project aims to accelerate the establishment of a complete ecosystem comprising domestic intelligent computing chips, system software, and large models [3]