Core Insights - The rise of edge cloud technology is revolutionizing data processing by shifting capabilities closer to the network edge, enhancing real-time data response and processing, particularly in the context of AI inference [1][5] - The demand for AI inference is significantly higher than for training, with estimates suggesting that inference computing needs could be 10 times greater than training needs [1][3] - Companies are increasingly focusing on the post-training phase and deployment issues, as edge cloud solutions improve the efficiency and security of AI inference [1][5] Group 1: AI Inference Demand - AI inference is expected to account for over 70% of total computing demand for general artificial intelligence, potentially reaching 4.5 times the demand for training [3] - The founder of NVIDIA predicts that the computational requirements for inference will exceed previous estimates by 100 times [3] - The transition from pre-training to inference is becoming evident, with industry predictions indicating that future investments in AI inference will surpass those in training by 10 times [4][6] Group 2: Edge Cloud Advantages - Edge cloud environments provide significant advantages for AI inference due to their proximity to end-users, which enhances response speed and efficiency [5][6] - The geographical distribution of edge cloud nodes reduces data transmission costs and improves user experience by shortening interaction chains [5] - Edge cloud solutions support business continuity and offer additional capabilities such as edge caching and security protection, enhancing the deployment and application of AI models [5][6] Group 3: Cost and Performance Metrics - Future market competition will hinge on cost/performance calculations, including inference costs, latency, and throughput [6] - Running AI applications closer to users improves user experience and operational efficiency, addressing concerns about data sovereignty and high data transmission costs [6] - The shift in investment focus within the AI sector is moving towards inference capabilities rather than solely on training [6]

Core Insights - The competition in the AI large model sector is shifting towards AI inference, marking the beginning of the AI inference era, with edge computing emerging as a new battleground in this field [1][2]. AI Inference Era - Major tech companies have been active in the AI inference space since last year, with OpenAI launching the O1 inference model, Anthropic introducing the "Computer Use" agent feature, and DeepSeek's R1 inference model gaining global attention [2]. - NVIDIA showcased its first inference model and software at the GTC conference, indicating a clear shift in focus towards AI inference capabilities [2][4]. Demand for AI Inference - According to a Barclays report, the demand for AI inference computing is expected to rise rapidly, potentially accounting for over 70% of the total computing demand for general artificial intelligence, surpassing training computing needs by 4.5 times [4]. - NVIDIA's founder Jensen Huang predicts that the computational power required for inference could exceed last year's estimates by 100 times [4]. Challenges and Solutions in AI Model Deployment - Prior to DeepSeek's introduction, deploying and training AI large models faced challenges such as high capital requirements and the need for extensive computational resources, making it difficult for small and medium enterprises to develop their own ecosystems [4]. - DeepSeek's approach utilizes large-scale cross-node expert parallelism and reinforcement learning to reduce reliance on manual input and data deficiencies, while its open-source model significantly lowers deployment costs to the range of hundreds of calories per thousand calories [4]. Advantages of Edge Computing - AI inference requires low latency and proximity to end-users, making edge or edge cloud environments advantageous for running workloads [5]. - Edge computing enhances data interaction and AI inference efficiency while ensuring information security, as it is geographically closer to users [5][6]. Market Competition and Player Strategies - The AI inference market is rapidly evolving, with key competitors including AI hardware manufacturers, model developers, and AI service providers focusing on edge computing [7]. - Companies like Apple and Qualcomm are developing edge AI chips for applications in AI smartphones and robotics, while Intel and Alibaba Cloud are offering edge AI inference solutions to enhance speed and efficiency [7][8]. Case Study: Wangsu Technology - Wangsu Technology, a leading player in edge computing, has been exploring this field since 2011 and has established a comprehensive layout from resources to applications [8]. - With nearly 3,000 global nodes and abundant GPU resources, Wangsu can significantly improve model interaction efficiency by 2 to 3 times [8]. - The company's edge AI platform has been applied across various industries, including healthcare and media, demonstrating the potential for AI inference to drive innovation and efficiency [8].