据知情人士透露，英伟达已经构建了一项位置验证技术，可用于指示其芯片所处的国家。这项举措 有望帮助阻止其人工智能芯片被走私至出口受限的国家。 该功能在过去几个月中已进行私下展示，但尚未正式发布。知情人士表示，它将作为一项可由客户 安装的软件选项，利用英伟达 GPU 的"机密计算"（ confidential computing ）能力来实现。 英伟达的一位负责人介绍，这款软件最初是为了让客户能够追踪芯片的整体计算性能——这是大型 数据中心运营商在采购大批处理器时的常见需求——并将通过与英伟达服务器通信的时间延迟来推 断芯片的大致位置，其精度大致与其他基于互联网的定位服务相当。 英伟达在一份声明中表示："我们正在实施一项新的软件服务，使数据中心运营商能够监控其整个 AI GPU 集群的健康状态和库存情况。这款由客户自行安装的软件代理将利用 GPU 遥测数据来监 测集群健康状况、完整性和库存信息。" 据这位英伟达负责人透露，该功能将首先在英伟达最新的" Blackwell "系列芯片上提供，因为它 们在"证明"（ attestation ）流程方面具备比此前的 Hopper 和 Ampere 两代产品更强的安全功 ...

Core Insights - Gartner identifies ten strategic technology trends that organizations need to focus on by 2026, emphasizing the unprecedented speed of innovation and transformation in the current year [1][3]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing resources to manage complex workloads, enhancing performance and innovation potential [6]. - By 2028, over 40% of leading companies will apply hybrid computing paradigms to critical business processes, a significant increase from the current 8% [8]. Group 2: Multi-Agent Systems - Multi-agent systems consist of multiple AI agents that interact to achieve complex individual or collective goals, enhancing automation and collaboration [10]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are tailored AI models trained on specific industry data, providing higher accuracy and compliance for specialized tasks compared to general models [11]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [13]. Group 4: AI Security Platforms - AI security platforms offer unified protection mechanisms for AI applications, helping organizations monitor activities and enforce usage policies [16]. - By 2028, over 50% of enterprises will utilize AI security platforms to safeguard their AI investments [16]. Group 5: AI Native Development Platforms - AI native development platforms enable rapid software development through generative AI, allowing non-technical experts to create applications [19]. - By 2030, 80% of enterprises will transform large software engineering teams into smaller, agile teams empowered by AI [19]. Group 6: Confidential Computing - Confidential computing protects sensitive data by isolating workloads in trusted execution environments, crucial for regulated industries [20]. - By 2029, over 75% of business processes handled in untrusted infrastructures will be secured through confidential computing [22]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety [23]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations shift from passive defense to active protection, with AI-driven solutions playing a key role [26]. Group 9: Digital Traceability - Digital traceability is essential for verifying the source and integrity of software and data, especially as reliance on third-party software increases [30]. Group 10: Geopolitical Repatriation - Geopolitical repatriation involves moving data and applications to local platforms to mitigate geopolitical risks, a trend expected to grow significantly by 2030 [33].

Core Points - The 2025 Global Computing Conference (CGC) is scheduled for November 7 at the Shenzhen Convention Center, focusing on six major themes: intelligent computing, embodied intelligence, open liquid cooling, confidential computing, AI cloud operating systems, and CloudDevice [1][3] Group 1: Conference Themes - The Intelligent Computing Forum will discuss pathways for improving computing efficiency and optimizing AI models [3] - The Embodied Intelligence Forum will focus on the practical applications of robotics and smart hardware in industrial and consumer scenarios [3] - The Open Liquid Cooling Technology Forum will share energy-efficient cooling solutions for high-density computing environments [3] - The Confidential Computing Forum will explore collaborative mechanisms for privacy protection and AI security in sectors like finance and the internet [3] - The AI Cloud Operating Systems Forum will release technical standards related to computing resource scheduling and heterogeneous integration [3] - The CloudDevice Forum will showcase innovative integration cases of cloud terminals in manufacturing and retail industries [3] Group 2: Interactive Exhibitions and Networking - The conference will feature a technology interaction display area, including CloudDevice XR experiences, demonstrations of embodied intelligent robots, and a mini development lab for intelligent computing [3] - The event aims to provide a platform for communication among technology research, industry applications, and cross-sector collaboration, allowing participants to understand the latest technological trends and connect with representatives from various enterprises and research institutions [3]

Core Insights - The "2025 Global Computing Conference (CGC)" is set to focus on key areas such as intelligent computing, embodied intelligence, open liquid cooling, confidential computing, AI cloud operating systems, and CloudDevice, promoting a vision of "new computing empowering a digital intelligence society" [1][2][3] Group 1: Conference Overview - The conference will feature six major forums aimed at addressing industry pain points and fostering collaboration among academia, industry, and research [1] - The event will take place at the Shenzhen Convention and Exhibition Center on November 7, providing opportunities for participants to connect with leading companies and research institutions [3] Group 2: Forum Highlights - The Intelligent Computing Forum will focus on upgrading computing productivity, showcasing AI model optimization and computing efficiency improvements through algorithm innovation and hardware acceleration [1][2] - The Embodied Intelligence Forum will address challenges in industrial robot interaction and consumer-grade smart device development, using case studies to explore commercialization strategies [1][2] - The Open Liquid Cooling Technology Forum will serve as a bridge between equipment manufacturers and data center users, providing deployment guidelines and cost-saving references through thermal efficiency comparisons [2] - The Confidential Computing Forum will discuss AI-era security and privacy protection, particularly in financial and internet applications [2] - The AI Cloud Operating System Forum will break down core technologies for computing scheduling and present solutions for heterogeneous computing orchestration [2] - The CloudDevice Forum will focus on the integration of cloud terminals across various industries, sharing smart terminal implementation cases [2] Group 3: Exhibition Features - The "GCC Annual Theme Exhibition Area" will feature interactive AI experiences, including the CloudDeviceXR interactive area and embodied intelligent robot interaction zone, allowing participants to engage with cutting-edge technologies [2] - The exhibition will also include a relaxation area with AI smart massage chairs and 3,000 gift packages, blending professional exchange with enjoyable experiences [2]

Core Viewpoint - The article emphasizes that 2026 will be a pivotal year for technology leaders, with unprecedented speed in transformation, innovation, and risk driven by artificial intelligence (AI) and a highly interconnected world [2]. Group 1: AI Supercomputing Platforms - AI supercomputing platforms integrate various computing paradigms to manage complex workloads, enhancing performance and innovation potential [5]. - By 2028, over 40% of leading companies will adopt hybrid computing architectures for critical business processes, a significant increase from the current 8% [6]. - The technology is already driving innovation across industries, significantly reducing drug modeling time in biotech and lowering portfolio risks in financial services [7]. Group 2: Multi-Agent Systems - Multi-agent systems consist of multiple AI agents that interact to achieve complex individual or collective goals, enhancing automation and collaboration [9]. - These systems allow for modular design, improving efficiency and adaptability in business processes [9]. Group 3: Domain-Specific Language Models (DSLM) - DSLMs are trained on specialized datasets for specific industries, providing higher accuracy and compliance compared to generic large language models (LLMs) [11]. - By 2028, over half of generative AI models used by enterprises will be domain-specific [12]. - Context is crucial for the success of AI agents based on DSLMs, enabling them to make informed decisions even in unfamiliar scenarios [13]. Group 4: AI Security Platforms - AI security platforms provide unified protection mechanisms for third-party and custom AI applications, helping organizations monitor AI activities and enforce usage policies [13]. - By 2028, over 50% of enterprises will utilize AI security platforms to safeguard their AI investments [15]. Group 5: AI-Native Development Platforms - AI-native development platforms enable rapid software development, allowing non-technical experts to create applications with AI assistance [17]. - By 2030, 80% of enterprises will transform large software engineering teams into smaller, more agile teams empowered by AI [17]. Group 6: Confidential Computing - Confidential computing reshapes how enterprises handle sensitive data by isolating workloads in trusted execution environments [18]. - By 2029, over 75% of business workloads processed in untrusted environments will be secured through confidential computing [18]. Group 7: Physical AI - Physical AI empowers machines and devices with perception, decision-making, and action capabilities, providing significant benefits in automation and safety-critical industries [19]. Group 8: Proactive Cybersecurity - Proactive cybersecurity is becoming a trend as organizations face increasing threats, with predictions that by 2030, proactive defense solutions will account for half of enterprise security spending [23]. Group 9: Geopolitical Data Migration - Geopolitical risks are prompting companies to migrate data and applications to sovereign or regional cloud services, enhancing control over data residency and compliance [26]. - By 2030, over 75% of enterprises in Europe and the Middle East will migrate virtual workloads to solutions that mitigate geopolitical risks, up from less than 5% in 2025 [26].

Core Viewpoint - Fujitsu is transitioning its focus from high-performance computing to scalable traditional data center infrastructure with its next-generation processor, Monaka, which is designed for cloud-native workloads and aims for efficient, secure computing [1][2][37]. Group 1: Monaka Processor Overview - Monaka is built on a 2nm core chip with a 3D multi-core layout, optimized for air-cooled servers and conventional memory, targeting confidential computing and low-voltage operation [1][3][6]. - The processor will feature 144 Armv9-A cores per slot in a dual-slot configuration, totaling 288 cores, and is designed for standard 2U data center servers [3][6]. - Monaka aims to achieve generational improvements in application performance and performance per watt, with a projected launch in fiscal year 2027 [10][38]. Group 2: Design and Efficiency - The design incorporates advanced silicon for critical areas while using more cost-effective processes for larger SRAM and IO chips, aligning with Japan's energy efficiency goals [6][10]. - Monaka will utilize DDR5 and PCIe Gen6 for high throughput IO, avoiding special packaging or HBM to enhance memory bandwidth [6][10]. - The chip is expected to have a power consumption of less than 500W, suitable for standard server racks, focusing on scalability and efficient throughput rather than peak floating-point performance [6][10]. Group 3: Security and Reliability - Monaka emphasizes trust with hardware-level isolation, workload protection, and system resilience, positioning it as a confidential computing platform for multi-tenant environments [19][22]. - It features full memory encryption and a hardware root of trust to enhance system-level security and verify firmware authenticity [22][19]. - Reliability features include error detection and correction mechanisms, thermal control, and maintainability, aiming for high uptime in large-scale distributed deployments [22][19]. Group 4: Software and Ecosystem - Monaka is designed to run standard Linux stacks, supporting upstream distributions and common development tools, ensuring compatibility and accessibility for developers [24][27]. - The development stack will support LLVM, GCC, and Python, maintaining consistency with tools used in previous Fujitsu architectures [27][24]. - Monaka will support CXL 3.0 for composable infrastructure and PCIe Gen6 for compatibility with next-generation storage and networking devices [30][24]. Group 5: Market Positioning - Fujitsu positions Monaka as a "mainframe-class" processor, focusing on predictable behavior, lifecycle control, and secure leasing, rather than just throughput aggregation [31][34]. - The processor targets markets that require platform integrity, such as sovereign cloud deployments, telecommunications, and defense sectors, emphasizing reliability over raw performance [34][31]. - Monaka represents a continuation of Fujitsu's decades-long experience in processor design, transitioning from SPARC to Arm architecture while prioritizing control and integration [35][37].