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 article emphasizes the imminent shift towards edge AI chips, predicting that by 2026, the focus on AI hardware will transition from cloud-based solutions to edge devices, marking a significant evolution in the AI landscape [2][11]. Group 1: Industry Trends - In 2025, major tech companies like Google and OpenAI are initiating significant AI projects, while simultaneously, a quiet revolution in AI hardware is occurring at the edge [3][4]. - The AI industry is witnessing a shift from cloud computing dominance to edge computing, where AI capabilities are increasingly integrated into everyday devices [4][11]. - 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% [12]. Group 2: Technological Evolution - The evolution of computing technology has historically been driven by paradigm shifts, such as the transition from CPU to GPU dominance in cloud computing [5][10]. - The emergence of large language models (LLMs) and visual language models (VLMs) has created a demand for "cognitive-level computing," necessitating advancements in both cloud and edge AI technologies [9][10]. - The transition from CPU-based general computing to GPU-centric intelligent computing has been rapid, with the share of CPU-based supercomputers dropping from nearly 90% in 2019 to less than 15% by 2025 [10]. Group 3: Edge AI Development in China - China's edge AI market is expected to reach 307.7 billion yuan by 2029, with a CAGR of 39.9%, driven by strong policy support and market demand [12][13]. - The country has a complete edge AI industry chain, from chip manufacturers to algorithm providers and terminal product developers, creating a unique ecosystem [13][14]. - Policies like the "14th Five-Year Plan" emphasize the importance of AI integration across various industries, aiming for over 90% penetration of smart terminals by 2030 [13]. Group 4: Model and Chip Innovations - Techniques like model distillation are enabling the compression of large models, making them suitable for deployment on edge devices while maintaining performance [12][23]. - The demand for edge computing power is surging, particularly for multi-modal models that require significant processing capabilities [24][25]. - The supply of edge computing chips is evolving, with new architectures providing higher performance and efficiency, such as the introduction of independent neural processing units (NPUs) [25][30]. Group 5: Future of Edge AI - The future of edge AI is expected to see a shift towards independent NPUs, which will dominate the landscape due to their performance advantages and flexibility [32][36]. - The integration of edge AI into daily life is anticipated to transform user experiences, moving from basic connectivity to advanced autonomous systems capable of complex decision-making [40][41]. - The ultimate goal is to achieve a seamless integration of AI into everyday devices, leading to a future where AI is ubiquitous and enhances human capabilities [48][49].