公众号记得加星标⭐️，第一时间看推送不会错过。 当前，AI技术正从"云端集中式计算"加速向"端侧分布式部署"转型，移动终端已成为承载用户 智能体验的核心载体。 Lumex CSS平台： 为移动终端量身打造的端侧AI全栈方案 据介绍，Arm Lumex CSS平台是一套专为旗舰级智能手机及下一代个人电脑打造的先进计算平台， 核心目标是通过技术创新全面优化端侧AI性能，其核心组件包括： Chris Bergey强调，移动计算已步入AI定义的新时代，我们需要协同设计、优化的集成平台来来应 对。从性能层面看，Lumex CSS平台优势显著。 Arm C1 CPU集群： 集成SME2技术， 构建全层级端侧AI算力底座 随着AI终端元年正式开启，消费电子设备领域迎来从"功能叠加"到"智能重构"的深刻变革。无论是从 实时响应的 AI 影像增强、本地运行的轻量化大模型，还是沉浸式移动游戏与多模态交互智能助手， 用户对"低延迟、高流畅、长续航"的端侧AI需求日益迫切，端侧AI也已从附加功能跃升为定义产品 竞争力的核心底座。 但行业快速发展的背后，端侧计算的核心矛盾愈发凸显：一方面，传统架构难以承载高密度AI任务， 语音助手延迟 ...

Core Viewpoint - AI is recognized as the most significant technological innovation of the era, with Arm accelerating its integration across various platforms from cloud to edge [1] Industry Trends - Three major trends in AI development have been identified: 1. Miniaturization and performance enhancement of models, exemplified by models like DeepSeek achieving superior decision-making capabilities with a smaller footprint [3] 2. Explosive growth in edge computing, with the skepticism about edge AI largely dissipating as computational power continues to rise [3] 3. Acceleration of commercial applications for AI entities and physical AI, leading to innovative applications such as rescue robots and delivery robots [3] AI Readiness Index Report - Arm released the "AI Readiness Index Research Report," surveying 655 business leaders across eight global markets, with over 100 from China, highlighting the active AI application in smart manufacturing, technology, and energy sectors [3][4] Investment Characteristics in China - Chinese enterprises exhibit three characteristics in AI investment: 1. Strategy and budget prioritization, with 43% of Chinese companies having a clear AI strategy compared to 39% globally [4] 2. A focus on efficiency improvement as the core objective, with 95% planning to increase AI budget in the next three years [4] 3. Deployment of AI technologies primarily in chatbots, natural language processing, and deep learning, reflecting the robust development of large models in China [4] Challenges in AI Industry - The AI industry faces three core challenges: 1. Infrastructure issues, particularly the imbalance between energy consumption and computational power, with data center energy consumption rising from megawatt to gigawatt levels [5] 2. Data security concerns, with 48% of global enterprises worried about data privacy breaches, prompting Arm to implement advanced security measures [5] 3. Talent shortages, identified by 49% of respondents as a major barrier to AI development, leading Arm to create a broad developer ecosystem to alleviate this pressure [6] Collaboration in Large Language Models - Arm collaborates deeply with leading local large language model vendors, leveraging Armv9 architecture and KleidiAI to enhance AI performance, driving significant advancements in models like Tongyi Qianwen and Wenxin [7]