大模型战火烧到端侧：一场重构产业格局的算力革命

Core Viewpoint - The AI industry is undergoing a significant transformation, shifting from cloud-based computing to edge AI, with a focus on developing AI chips for end devices, which is expected to reshape the future of technology and user interaction [3][8][29]. Group 1: Industry Trends - The global edge AI market is projected to reach 1.2 trillion yuan by 2029, with a compound annual growth rate (CAGR) of 39.6% [8]. - China's edge AI market is expected to achieve 307.7 billion yuan by 2029, with a CAGR of 39.9% [9]. - The transition from cloud-based AI to edge AI is driven by the need for lower latency and cost-effective solutions in various applications, including industrial and consumer sectors [8][10]. Group 2: Technological Evolution - The evolution of computing technology has transitioned from CPU-dominated general computing to GPU-centric intelligent computing, with a significant shift in the architecture of supercomputers from 90% CPU reliance in 2019 to less than 15% by 2025 [6]. - The emergence of large language models (LLMs) and vision-language models (VLMs) has created a demand for "cognitive-level computing," necessitating advancements in both cloud and edge AI chip technologies [5][12]. Group 3: Market Dynamics - Major tech companies are competing in the edge AI space, with significant investments in AI hardware and software solutions, such as OpenAI's acquisition of io for $6.5 billion and the introduction of AI smartphones by ByteDance [3][4]. - The development of model distillation technology allows for the compression of large models, making them suitable for deployment on edge devices, thus enhancing their performance while reducing computational complexity [8][14]. Group 4: Future Outlook - The future of edge AI is expected to involve a shift towards independent neural processing units (dNPUs) as the primary computing architecture, moving away from integrated solutions to meet the growing demands for AI performance [19][21]. - The evolution of edge AI will lead to a multi-tiered approach to computing power, with low, medium, and high-performance solutions tailored to specific application needs [20][21].