国盛证券（002670）指出，Coherent公布最新财季财报，多项核心指标超出市场预期，光通信龙头企业 强劲表现反映了AI数据中心、数据中心互连对光学组件需求的增长，将从AI计算基础设施的持续扩张 中受益。该行指出，硅光技术核心价值在于芯片设计与晶圆制造，这使产业范式从"封装主导"转向"芯 片设计主导"。传统方案EML芯片短缺，硅光凭借高集成度、低损耗等性能优势，以及产能弹性和成本 优势，市场份额逐步提升。而前瞻布局、芯片设计能力优秀的光通信龙头企业将持续扩大优势，充分受 益于算力高景气。 剑桥国际(06166)涨超4%，截至发稿，涨4.23%，报83.8港元，成交额8009.26万港元。 消息面上，近日，剑桥科技（603083）在投资者互动平台表示，在硅光芯片领域，公司与3家供应商合 作并通过保供协议或投资保障供应，800G及以上光模块以硅光技术为主，已做关键物料储备，在硅光 技术路线上持续深耕，同步推进CPO集成硅光引擎、大功率激光技术CPO器件的研发与送样准备。剑桥 科技此前披露三季报时指出，公司业绩实现高质量增长，核心驱动力来自高速光模块与电信宽带接入两 大主营业务。 ...

Group 1 - AI technology is driving transformation across industries, with AI computing infrastructure evolving from single GPU clusters to integrated AI factories [1][2] - Single GPU clusters have been essential for AI model training but are increasingly inadequate for complex applications, leading to the emergence of comprehensive AI factories that integrate computing, storage, networking, and cooling systems [1] - Integrated AI factories enhance development efficiency and operational performance for various AI tasks through dynamic resource allocation and advanced data management [1] Group 2 - The demand for distributed computing is growing exponentially as AI enters the inference paradigm and multi-agent systems, necessitating innovations in liquid cooling, high-bandwidth memory, and dedicated interconnect networks [2] - The strategic significance of AI infrastructure has transcended technical aspects, becoming crucial for national competitiveness and data sovereignty in the digital age [2] - Countries and large enterprises are actively establishing AI sovereignty capabilities and deploying local AI factories tailored to their unique advantages and needs, such as Indonesia and India focusing on cultural adaptation and Germany on railway automation [2] Group 3 - The global development of AI infrastructure is characterized by specialization, sovereignty, and regional differentiation, presenting a historical opportunity for countries and enterprises to build AI systems that meet their development needs [3] - Leveraging AI technology is essential for driving comprehensive digital transformation and initiating a new chapter in the development of the digital economy [3]

Core Viewpoint - The article discusses the advancements in AI technology and infrastructure by NVIDIA, highlighting the launch of new architectures and partnerships aimed at enhancing AI capabilities and performance in various sectors [1][2][3][4][5][6]. Group 1: AI Architecture Developments - NVIDIA is transitioning from Generative AI to Agentic AI, with future developments leading to Physical AI, indicating a significant evolution in AI capabilities [1]. - The Blackwell architecture has been fully launched, showcasing the Grace Blackwell NVLink 72 chip, which integrates 72 Blackwell GPUs and achieves 1.4 EFLOPS performance [2]. - The Blackwell Ultra NVL72 platform is set to double the bandwidth and increase memory speed by 1.5 times compared to its predecessor, paving the way for advanced AI inference [3]. Group 2: Market Demand and Procurement - The top four U.S. cloud service providers have purchased 1.3 million Hopper chips in 2024 and are expected to acquire 3.6 million Blackwell chips in 2025, indicating a strong demand for AI computing infrastructure [2]. - By 2028, capital expenditures for intelligent computing centers are projected to exceed $1 trillion, reflecting the growing investment in AI technologies [2]. Group 3: Future Product Launches - The Vera Rubin platform is anticipated to start shipping in the second half of 2026, featuring NVLink 144 technology and achieving performance levels 3.3 times greater than the GB300 NVL72 [4]. - The next-generation Rubin Ultra NVL576 is expected to launch in the second half of 2027, with performance projected to be 14 times that of the GB300 NVL72 [4]. Group 4: Strategic Partnerships and Collaborations - NVIDIA is expanding its collaboration with General Motors to develop autonomous vehicles and enhance AI model training [5]. - Partnerships with Cisco and T-Mobile aim to explore AI-native networks for next-generation 6G wireless technology [5]. - The introduction of the GR00T N1 robot model and collaboration with Google DeepMind and Disney for a physics engine indicates NVIDIA's commitment to advancing robotics [5]. Group 5: Cost Efficiency and Market Impact - The efficiency of computing power is improving at a rate of three times every eight months, leading to significant cost reductions, which benefits global cloud providers and applications [6].