甬兴证券指出，AI算力需求驱动AI芯片行业快速发展，国内厂商正加速AI芯片国产化替代。AI芯片是 AI大模型的底座，技术迭代、国产化替代有望带来发展新机遇。英伟达GPU产品持续迭代，国内 GPU、ASIC（专用集成电路）厂商加速国产化替代，AI基础设施建设或将推动AI算力规模增长，从而 带动国内AI芯片企业实现收入增长。 数据显示，截至2025年12月31日，上证科创板芯片指数前十大权重股分别为中芯国际、海光信息、寒武 纪、澜起科技、中微公司、拓荆科技、芯原股份、华虹公司、沪硅产业、东芯股份(688110)，前十大权 重股合计占比57.76%。 科创芯片ETF（588200）跟踪上证科创板芯片指数，是布局科创板芯片板块的便利工具。 截至2026年1月9日11:00，上证科创板芯片指数上涨0.06%，成分股复旦微电上涨5.22%，芯原股份上涨 5.08%，睿创微纳上涨4.75%，芯动联科上涨3.94%，成都华微上涨3.58%。 没有股票账户的场外投资者可以通过科创芯片ETF联接基金（017470）关注国产芯片投资机遇。 消息方面，1月8日，三星电子披露业绩数据，核实按合并财务报表口径计算的公司2025年第四季度 ...

1月7日，工信部等八部门印发《"人工智能+制造"专项行动实施意见》（以下简称《意见》）。 《意见》提出，到2027年，推动3至5个通用大模型在制造业深度应用，形成特色化、全覆盖的行业大模 型，打造100个工业领域高质量数据集，推广500个典型应用场景。培育2至3家具有全球影响力的生态主 导型企业和一批专精特新中小企业，打造一批"懂智能、熟行业"的赋能应用服务商，选树1000家标杆企 业。建成全球领先的开源开放生态，安全治理能力全面提升，为人工智能发展贡献中国方案。 加快重点行业应用赋能 《意见》将"赋智升级：拓展推广高价值应用场景"放在专项行动的第二位，明确提出加快重点行业应用 赋能。 《意见》提出，要参考《指引》，分类制定"人工智能+制造"行业应用全景图和转型路线图，加快人工 智能赋能原材料、装备制造、消费品、电子信息、软件和信息技术服务等制造业相关重点行业，加快标 杆解决方案和经验推广应用。 《每日经济新闻》记者注意到，与《意见》一起发布的，还有两份配套附件，分别是《人工智能赋能制 造业重点行业转型指引》（以下简称《指引》）和《制造业企业人工智能应用指南》。 强化人工智能算力供给 《意见》把"创新筑基：夯 ...

智通财经APP获悉，汇丰环球投资研究部发表最新研报，维持对大型科技企业的评级及目标价，同时着 重指出2026年人工智能(AI)产业链(涵盖云计算、芯片、大语言模型及硬件领域)将面临的挑战与蕴藏的 机遇。 行业发展存在多重瓶颈：Cote-Colisson及其团队称："尽管基础设施建设正在加速推进，但电力供应与 芯片产能仍是短期内制约营收增长预期的关键因素。"分析师补充道，2026年的行业讨论焦点应集中在 如何实现电力供需平衡。同时，根据亚马逊财报电话会议的内容，分析师认为芯片供应短缺或将成为一 项长期难题。 资本支出呈持续扩张态势：分析师表示，2025年超大规模云计算厂商的资本支出指引不断上调，而基于 当前的产能短缺现状，这一增长趋势预计将在2026年延续。分析师预测，2026年全球云计算领域资本支 出总额将同比增长44%。 ASIC兴起，GPU仍有发展空间：Cote-Colisson及其团队称："英伟达GPU应仍是超大规模云计算厂商的 首选。不过，随着ASIC在性能上的提升以及成本优势的显现，其市场竞争力正不断增强。"分析师预 计，ASIC的对外芯片销售规模将在2027年迎来显著增长。ASIC与GPU两大品类 ...

1月7日，工信部等八部门印发《"人工智能+制造"专项行动实施意见》（以下简称《意见》）。 《意见》提出，到2027年，推动3至5个通用大模型在制造业深度应用，形成特色化、全覆盖的行业大模 型，打造100个工业领域高质量数据集，推广500个典型应用场景。培育2至3家具有全球影响力的生态主 导型企业和一批专精特新中小企业，打造一批"懂智能、熟行业"的赋能应用服务商，选树1000家标杆企 业。建成全球领先的开源开放生态，安全治理能力全面提升，为人工智能发展贡献中国方案。 《每日经济新闻》记者注意到，与《意见》一起发布的，还有两份配套附件，分别是《人工智能赋能制 造业重点行业转型指引》（以下简称《指引》）和《制造业企业人工智能应用指南》。 优先选择原材料、装备制造、消费品等五大重点行业赋能 《意见》将"赋智升级：拓展推广高价值应用场景"放在了专项行动的第二位，明确提出加快重点行业应 用赋能。 《意见》提出，要参考《指引》，分类制定"人工智能+制造"行业应用全景图和转型路线图，加快人工 智能赋能原材料、装备制造、消费品、电子信息、软件和信息技术服务等制造业相关重点行业，加快标 杆解决方案和经验推广应用。 实现AI+制造 ...

1月7日，工信部等八部门印发《"人工智能+制造"专项行动实施意见》（以下简称《意见》）。 《意见》提出，到2027年，推动3至5个通用大模型在制造业深度应用，形成特色化、全覆盖的行业大模 型，打造100个工业领域高质量数据集，推广500个典型应用场景。培育2至3家具有全球影响力的生态主 导型企业和一批专精特新中小企业，打造一批"懂智能、熟行业"的赋能应用服务商，选树1000家标杆企 业。建成全球领先的开源开放生态，安全治理能力全面提升，为人工智能发展贡献中国方案。 《每日经济新闻》记者注意到，与《意见》一起发布的，还有两份配套附件，分别是《人工智能赋能制 造业重点行业转型指引》（以下简称《指引》）和《制造业企业人工智能应用指南》。 实现AI+制造，AI才能被视为真正的生产力工具 《意见》把"创新筑基：夯实人工智能赋能底座"放在了专项行动的首位，首先提出强化人工智能算力供 给。 例如，对于装备制造行业，《指引》明确提出加速汽车行业全链条智能化升级。打造汽车大模型，自动 生成车身造型、内饰布局等方案，实时仿真动态优化结构强度、风阻系数等参数，推动智能研发新范 推动智能芯片软硬协同发展，支持突破高端训练芯片、端侧推 ...

公众号记得加星标⭐️，第一时间看推送不会错过。 人工智能革命才刚刚开始三年，但考虑到其发展速度之快，感觉好像还要等二十年。半导体是人工智 能领域创新最为迅猛的领域之一。以下是您在2026年可以期待的芯片和人工智能加速器领域的发展趋 势。 Saxena表示，新的一年将迎来一场系统之战，而非芯片之战。"浮点运算性能不再是衡量性能的唯一 标准，互连、内存和编译器将决定最终的性能表现，"他说道。"NVLink Fusion和定制交换机ASIC 正在重塑集群规模的拓扑结构，而软件锁定将成为新的护城河，因为编排和编译器将决定资源利用 率。HBM和GPU供应紧张将推高云AI的价格，欧洲方面已暗示将在2026年初迎来价格上涨。" 半导体制造商使用的技术也将在 2026 年发生变革。达索系统全球高科技产业战略家约翰·麦卡利表 示，这将对芯片产生深远的影响。 "半导体行业正经历着指数级的变革，其驱动力包括日益增长的复杂性、新兴技术以及不断变化的全 球需求，"麦卡利表示。"预计到2025年，先进工艺节点将达到2纳米，研究目标是实现埃级精度。展 望2026年，3D封装、量子计算和人工智能加速器等创新技术正在塑造下一代芯片，而企业高管 ...

生成式AI与大语言模型运算需求持续扩张，云端算力竞局再度升温。因应谷歌（Google）自研芯 片TPU订单动能强劲，供应链透露，博通、联发科纷纷调高2026年投片量。半导体业界指出，联 发科已在内部进行资源调度，将手机芯片部门部分人力，转往ASIC、车用等新蓝海，目标直指资 料中心与CSP客制化芯片商机。 TPU挟成本及生态系优势，挑战辉达AI霸主地位。法人指出，谷歌TPU 2026年将迈入第八代、第 三季开始量产，规模有望在2027年达500万颗、2028年进一步提高至700万颗，较先前大幅上 修。 ASIC伙伴包括博通、联发科皆积极为其准备产能。 如果您希望可以时常见面，欢迎标星收藏哦~ 联发科副董事长暨执行长蔡力行指出，首个ASIC案件进展顺利，预计2026年贡献营收约10亿美 元，2027年放大至数十亿美元，第二个专案则从2028年挹注营收。供应链推测，第二个CSP客户 为Meta，并将采用2纳米制程打造，凸显联发科已具备与国际大厂比拼肌肉的能力。 （来源 ： 工商时报 ） 推荐阅读 10万亿，投向半导体 芯片巨头，市值大跌 黄仁勋：HBM是个技术奇迹 Jim Keller：RISC-V一定会胜出 业 ...

Core Viewpoint - Nvidia has agreed to acquire Groq, a high-performance AI accelerator chip design company, for $20 billion in cash, marking Nvidia's largest transaction to date, nearly tripling Groq's previous valuation of $6.9 billion within three months [3][7]. Group 1: Acquisition Details - The acquisition involves key Groq executives, including founder and CEO Jonathan Ross, joining Nvidia while Groq will continue to operate as an independent entity [4]. - Groq, founded in 2016 by former Google engineers, focuses on high-performance AI accelerator chip design, particularly for inference tasks [4][11]. - Nvidia's acquisition strategy is seen as a form of "acqui-hire," allowing the company to gain talent and technology while avoiding potential regulatory hurdles associated with traditional acquisitions [4][8]. Group 2: Financial Implications - Nvidia's offer includes generous compensation for Groq's shareholders, with approximately 85% of the payment made in cash upfront, and the remaining distributed over the next few years [9]. - Groq employees, approximately 600, will receive substantial financial incentives, with potential equity values estimated at $5 million per employee [4][9]. Group 3: Strategic Significance - The acquisition is viewed as a strategic move to strengthen Nvidia's competitive edge in the GPU market, especially as AI model focus shifts from training to inference, where traditional GPUs face limitations [4][12]. - Nvidia's purchase of Groq is compared to Microsoft's acquisition of GitHub, emphasizing its strategic importance in the AI landscape [11]. - The deal is expected to lock in customers, as AI labs now face the choice of either purchasing Nvidia GPUs or adopting Groq's LPU technology, thereby consolidating Nvidia's market position [12]. Group 4: Industry Trends - The AI chip market is evolving, with a clear divide between GPU-centric and non-GPU architectures, as companies like Google and Groq push for alternatives to traditional GPUs [14]. - The global AI chip market is projected to reach $413.8 billion by 2030, with non-GPU architectures expected to capture over 21% of the market share [15]. - In China, the trend towards non-GPU solutions is accelerating, with the market for non-GPU accelerated servers expected to approach 50% by 2029 [16].

Core Insights - The development of generative AI technology is driving a hardware innovation wave in consumer electronics, with a focus on edge AI deployment and the increasing demand for ASICs due to their cost-effectiveness and customization advantages [1][2]. Group 1: AI Technology and Consumer Electronics - The traditional consumer electronics market has entered a phase of low growth, with stable sales for smartphones and PCs, while TWS is experiencing slow growth [1]. - The release of ChatGPT has prompted companies to invest in large model technologies, leading to continuous iterations in AI models [1]. - Edge AI offers advantages such as low cost, high performance, and privacy security, facilitating the deployment of AI technologies in consumer electronics [1]. Group 2: ASIC Market Growth - The ASIC market is expected to grow significantly, with a forecasted increase from $6.6 billion in 2023 to $55 billion by 2028, representing a compound annual growth rate (CAGR) of 53% [2]. - Major companies like Google and Amazon are actively supplying ASICs, indicating strong demand and commercial viability for these chips [2]. Group 3: PCB Market Dynamics - The capital expenditure by AI and internet giants on data centers is projected to grow at a CAGR of 21% until 2029, driving the expansion of computing clusters [3]. - The increasing complexity of AI servers and high-speed switches is leading to a rise in PCB value, with both volume and price increasing rapidly in the AI PCB market [3]. Group 4: Edge AI Implementation - The advancement of model compression technology is laying the groundwork for deploying large models on edge devices, enhancing the application of AI in smartphones [4]. - Companies like Huawei and ByteDance are showcasing the significant application value of edge AI in mobile devices, with predictions of a CAGR of 115% for advanced AI processors and 32% for AI smartphones from 2023 to 2027 [4]. Group 5: Investment Recommendations - Companies to watch in the edge AI sector include Rockchip (603893.SH), Hengxuan Technology (688608.SH), Espressif Systems (688018.SH), Zhongke Blue Communication (688332.SH), and Horizon Robotics (09660) [5]. - In the ASIC sector, recommended companies include Chipone Technology (688521.SH), Aowei Technology (688220.SH), and Cambricon Technologies (688256.SH) [5].

Core Insights - The global AI infrastructure market is facing a severe supply shortage, particularly for GPUs, with an expected order volume of 2 million units this year against only 700,000 available units [1] - The demand for self-developed ASICs by cloud service companies is projected to grow at a rate of 44.6%, surpassing the 16.1% growth rate for GPUs, indicating a structural shift towards ASIC adoption due to GPU supply constraints [1] - The supply chain risks for GPUs are expected to peak this year, with production processes and high bandwidth memory (HBM) being interlinked, meaning any bottleneck could disrupt overall supply [1] Group 1 - TSMC is expanding its advanced packaging production lines, crucial for AI accelerators, but the gap between rapidly growing order volumes and actual shipments will persist due to the time required for capacity expansion [2] - ASIC chips, initially led by Google's TPU, are gaining attention as they are designed for specific AI workloads, offering advantages in energy efficiency, performance, and total cost of ownership (TCO) in the long run [2] - The AI accelerator market for ASIC users is expected to maintain a compound annual growth rate (CAGR) of approximately 28% until 2030, with the generative AI ASIC market projected to grow from about $24.9 billion in 2024 to approximately $186.7 billion by 2032, reflecting an annual growth rate of around 28.6% [2] Group 2 - This year is viewed as a critical turning point for the ASIC market, with industry executives noting that the current GPU supply shortage is a short-term issue but will have long-term implications on decision-making [3] - Major tech companies are increasingly viewing GPUs as strategic assets rather than stable commodities, leading to a shift towards reducing GPU dependency and increasing the share of ASICs in new data center investment plans [3]