目 录 | 一、审计报告……………………………………………………… 第 | 1—6 | | | 页 | | --- | --- | --- | --- | --- | | 二、财务报表……………………………………………………… 第 | 7—14 | | 页 | | | （一）合并资产负债表…………………………………………… | 第 | 7 | 页 | | | （二）母公司资产负债表………………………………………… | 第 | 8 | 页 | | | （三）合并利润表………………………………………………… | 第 | 9 | 页 | | | （四）母公司利润表………………………………………………第 | 10 | | 页 | | | （五）合并现金流量表……………………………………………第 | 11 | | 页 | | | （六）母公司现金流量表…………………………………………第 | 12 | | 页 | | | （七）合并所有者权益变动表……………………………………第 | 13 | | 页 | | | （八）母公司所有者权益变动表…………………………………第 | 14 | | 页 | | | 三、财务报表附注… ...

目 录 | | | | | 二、附件……………………………………………………………第 | 3-6 页 | | --- | --- | --- | | (一) | 本所营业执照复印件……………………………………第 | 3页 | | (二) | 本所执业证书复印件……………………………………第 | 4页 | | (三) | 签字注册会计师执业证书复印件……………………第 | 5-6页 | 内部控制审计报告 天健审〔2025〕6-419 号 云从科技集团股份有限公司全体股东： 按照《企业内部控制审计指引》及中国注册会计师执业准则的相关要求，我 们审计了云从科技集团股份有限公司（以下简称云从科技公司）2024 年 12 月 31 日的财务报告内部控制的有效性。 一、企业对内部控制的责任 我们认为，云从科技公司于 2024 年 12 月 31 日按照《企业内部控制基本规 范》和相关规定在所有重大方面保持了有效的财务报告内部控制。 天健会计师事务所（特殊普通合伙） 中国注册会计师： 中国·杭州 中国注册会计师： 按照《企业内部控制基本规范》《企业内部控制应用指引》以及《企业内部 控制评价指引》的规定，建立健全和有效实施内 ...

目 录 一、非经营性资金占用及其他关联资金往来情况的专项 | | | 二、非经营性资金占用及其他关联资金往来情况汇总表………………第 3 页 三、附件……………………………………………………………… 第 4-7 页 (一) 本所营业执照复印件…………………………………………第 4 页 (二) 本所执业证书复印件…………………………………………第 5 页 (三) 签字注册会计师执业证书复印件…………………………第 6-7 页 非经营性资金占用及其他关联资金往来情况的 专项审计说明 天健审〔2025〕6-421 号 云从科技集团股份有限公司全体股东： 我们接受委托，审计了云从科技集团股份有限公司（以下简称云从科技公司） 2024 年度财务报表，包括 2024 年 12 月 31 日的合并及母公司资产负债表，2024 年度的合并及母公司利润表、合并及母公司现金流量表、合并及母公司所有者权 益变动表，以及财务报表附注，并出具了审计报告。在此基础上，我们审计了后 附的云从科技公司管理层编制的 2024 年度《非经营性资金占用及其他关联资金 往来情况汇总表》（以下简称汇总表）。 一、对报告使用者和使用目的的限定 本报告仅 ...

三、附件…………………………………………………………… 第 10-13 页 (一) 本所营业执照复印件……………………………………… 第 10 页 (二) 本所执业证书复印件……………………………………… 第 11 页 (三) 签字注册会计师执业证书复印件………………………第 12-13 页 一、募集资金年度存放与使用情况鉴证报告………………………第 1—2 页 二、关于募集资金年度存放与使用情况的专项报告………………第 3—9 页 目 录 募集资金年度存放与使用情况鉴证报告 天健审〔2025〕6-420 号 云从科技集团股份有限公司全体股东： 我们鉴证了后附的云从科技集团股份有限公司（以下简称云从科技公司）管 理层编制的 2024 年度《关于募集资金年度存放与使用情况的专项报告》。 一、对报告使用者和使用目的的限定 本鉴证报告仅供云从科技公司年度报告披露时使用，不得用作任何其他目的。 我们同意将本鉴证报告作为云从科技公司年度报告的必备文件，随同其他文件一 起报送并对外披露。 二、管理层的责任 云从科技公司管理层的责任是提供真实、合法、完整的相关资料，按照《上 市公司监管指引第 2 号——上市公司募集资金管理 ...

云从科技公告，2025年第一季度营收为3723.32万元，同比下降31.56%；净亏损1.24亿元，去年同期净 亏损1.61亿元。 ...

Core Insights - The article discusses the multi-modal large model industry chain, highlighting its complexity and the various layers involved, including the foundational layer, model layer, and application layer [1][3]. Industry Overview - The multi-modal large model industry chain consists of three main layers: foundational (hardware and basic software), model (various types of multi-modal large models), and application (industry-specific large models) [1]. - Key players in the foundational layer include Intel and NVIDIA, while foundational software participants include Huawei, Tencent, and Unisoc [3]. Regional Distribution - The multi-modal large model industry is concentrated in major cities such as Beijing, Shanghai, Suzhou, Hangzhou, and Shenzhen, with a strong presence of upstream companies in Beijing and Shenzhen [4]. - Upstream companies in Beijing include Cambricon, Lenovo, and Inspur, while Shenzhen hosts Unisoc, HiSilicon, and ZTE [4]. Revenue Insights - The revenue distribution in China's multi-modal large model sector shows a concentration among leading companies, with Alibaba Cloud generating over 110 billion yuan, accounting for approximately 15% of its group's revenue [7]. - Huawei Cloud and Tencent Cloud follow, with revenues of 68.8 billion yuan and 63.6 billion yuan, respectively, each contributing around 5-7% to their parent companies [7]. Cost Structure - The training costs for mainstream large models in China range from tens of millions to hundreds of millions of dollars, with major players like Baidu, Alibaba, and Tencent investing over 200 million dollars [10]. - Startups like Kimi and DeepSeek manage to lower their training costs to between 30 million and 60 million dollars through technological optimization [10]. Product Cost Details - The training and cloud hosting costs for various multi-modal large models are detailed, with notable examples including: - Pangu Model (Huawei): ≥ 100 million USD - Wenxin Model (Baidu): ≥ 300 million USD - Mix Yuan Model (Tencent): ≈ 250 million USD - Xinghuo Model (iFlytek): = 80 million CNY [12].

曾几何时，商汤科技、旷视科技、云从科技、依图科技并称为"AI 四小龙"，承载着AI行业的无限期 待。时光流转，"AI 六小龙""杭州七小龙"等名号层出不穷，"AI 四小龙"在时代的洪流中逐渐失色。 从财报数据来看，"AI 四小龙"曾经的辉煌已如过眼云烟。 商汤科技2024 年度财报显示，全年总营收 37.72 亿元，却伴随着 43.06 亿元的净亏损，自 2018 年至 2024 年累计亏损超 546 亿元；云从科技2024 年总营收约 3.98 亿元，归母净亏损达6.63亿元。 持续亏损"老大难" 被誉为"AI 四小龙"之一的云从科技，往昔头顶着明星企业的光环，承载着行业与资本的诸多期待。然 而，在高研发投入下，亏损却不断增加，面临着商业化的难题。 数据显示，2024年，云从科技实现营业总收入3.98亿元，同比减少36.60%；营业利润-6.49亿元，上年同 期为-6.55亿元；归属于母公司所有者的净利润-6.37亿元，上年同期为-6.43亿元；归属于母公司所有者 的扣除非经常性损益的净利润-6.63亿元，上年同期为-6.89亿元。 从更长时间跨度来看，自2017 年起至 2024 年，云从科技仿佛陷入了一 ...

中新网湖北新闻4月23日电(李冬 晏君)作为全国首个国家网络安全人才与创新基地落户地，武汉临空港 经济技术开发区管委会4月22日在国家网安基地举办网络安全企业家恳谈会。 国内网安领军企业代表齐聚一堂，围绕"企业需求、产业发展"主题展开深度交流。本次恳谈会旨在搭建 政企"连心桥"，链接政策资源，携手推进网络安全产业高质量发展。 与会企业一行参观网安软件园展厅。 与会嘉宾走进武汉京东方、网安软件园展厅及中科晶上，实地考察武汉临空港经开区网安产业布局和创 新成果。在京东方企业展厅，高精度显示屏与智能化深度融合产品引发企业代表关注。在网安软件园展 厅，国家网安基地"1+2+N"产业发展方向和入驻企业创新成果集中亮相。中科晶上展示了其在星闪技术 研发和应用场景的突破性进展。 恳谈会上，武汉临空港经开区相关负责人从产业之城、网谷之城、枢纽之城、宜居之城、投资之城五方 面介绍区域发展前景，热忱欢迎大家走进临空港、考察临空港、投资临空港、享受临空港，表示武汉临 空港将用心用情当好企有所呼、我有所应的店小二，提供最优质的营商环境、最宜居的生活环境，与大 家共谋发展商机、共绘创业宏图、共享发展红利。 与会企业一行参观中科晶上。 ...

Core Insights - The global multimodal large model industry has evolved through distinct phases, from early exploration (1956-2005) to rapid growth (2006-2019), the rise of large models (2020-2022), and now into a phase of widespread application starting in 2023 [1] Market Size and Growth - The global AI hardware market, particularly for servers, is projected to grow from $19.5 billion in 2022 to $34.7 billion by 2026, with a compound annual growth rate (CAGR) of 17.3% [3][4] - The market for servers specifically used for generative AI is expected to increase its share from 11.9% in 2023 to 31.7% by 2026 [4] Computational Demand - The demand for computational power in AI is increasing, with models like ChatGPT requiring significant resources; for instance, the GPT-3 model needs 1,750 billion parameters and consumes 3,640 PF-days of computational power [5] - A tenfold increase in model parameters can lead to more than a tenfold increase in computational requirements, influenced by model architecture and hardware capabilities [5] Large Model Market Dynamics - The global large model market is experiencing rapid growth, with an estimated size of $21 billion in 2023 and a projected increase to $28 billion in 2024, reflecting a year-on-year growth of 33% [7] Competitive Landscape - According to the SuperCLUE benchmark report, GPT-4o leads the global model rankings with a score of 81, while six Chinese models have surpassed GPT-4-Turbo-0409, indicating a strong competitive presence in the market [10]

Core Viewpoint - The article discusses the evolution of artificial intelligence (AI) from its inception to the current era of large models, highlighting key milestones, technological advancements, and the impact on various industries. Group 1: Birth of Artificial Intelligence (Mid-20th Century) - In 1950, Alan Turing proposed the "Turing Test," defining the philosophical goal of AI [3] - The term "Artificial Intelligence" was first used in 1956 at Dartmouth College, marking the transition from philosophical speculation to applied technology [3] - Early AI systems, like the IBM701, had limited computational power, executing only 16,000 operations per second, which is significantly less than modern devices [3] Group 2: Symbolism and Its Failures (1960-1970) - The 1960s saw the rise of "symbolism," where researchers attempted to simulate human reasoning through rule-based expert systems [4] - The MYCIN system developed in 1976 achieved near-expert accuracy in diagnosing blood infections, demonstrating the commercial value of expert systems [4][5] - The "Fifth Generation Computer Systems" project in Japan, launched in 1982 with an investment of $850 million, aimed to create intelligent computers but ultimately failed due to over-reliance on symbolic methods and hardware limitations [8] Group 3: Rise of Machine Learning (1990s-2000s) - The 1990s marked a shift to machine learning, moving from rule-based systems to data-driven approaches, allowing machines to learn from data rather than relying solely on hard-coded rules [10] - IBM's DeepBlue defeated a chess champion in 1997, showcasing the potential of machine learning in closed tasks [12] - The introduction of Google's PageRank algorithm in 1998 demonstrated the commercial value of data correlation, transforming search engines into profitable ventures [12] Group 4: Deep Learning Revolution (2010s-2020) - The 21st century saw the emergence of deep learning, enabling AI to automatically extract features through multi-layer neural networks [13] - AlphaGo's victory over a world champion in 2016 highlighted the capabilities of deep reinforcement learning [13] - The rapid increase in model parameters from 60,000 in LeNet-5 to 600 million in AlexNet illustrated the exponential growth in AI's capacity to handle complex tasks [14] Group 5: Era of Large Models (2021-Present) - The introduction of large pre-trained models like GPT-3 in 2020 has propelled AI towards general intelligence, showcasing advanced language understanding and generation capabilities [15] - Applications of generative AI have expanded across various fields, including content creation, programming assistance, and image generation, significantly enhancing productivity [16] - The competition between open-source and closed-source models has intensified, with companies like HuggingFace promoting open-source development while others like OpenAI focus on proprietary advancements [17] Group 6: Future Directions and Challenges - The future of AI is expected to focus on specialized models for high-value sectors such as healthcare and finance, emphasizing efficiency and cost-effectiveness [38] - The relationship between AI and human employees is anticipated to evolve into deeper integration, enhancing decision-making and innovation within organizations [38] - Ethical challenges and societal risks associated with AI, such as job displacement and privacy concerns, remain critical issues that need addressing [39]