Core Viewpoint - The article discusses the considerations for choosing between local and cloud-based AI models, emphasizing the importance of computational requirements and privacy needs when selecting hardware for AI applications [5][6][17]. Group 1: AI Model Deployment - Local deployment of AI models is suitable for applications requiring high computational power and privacy, particularly when handling sensitive data [16][17]. - The article outlines the parameters of AI models, indicating that a model with 1 billion parameters requires approximately 4GB of memory for full precision, while half-precision models can reduce this requirement significantly [11][14]. - For local deployment, models with fewer than 14 billion parameters are generally manageable, while larger models may necessitate high-end GPUs like the RTX 4090 or 5090 [14][19]. Group 2: Hardware Recommendations - The article provides recommendations for laptops suitable for AI applications across different price ranges, highlighting models with specific GPU configurations [26][29][31]. - For a budget of around 5000 yuan, the Mechrevo Aurora X with a 5060 GPU is suggested as a high-value option [26]. - In the 6000 yuan range, the HP Shadow Elf 11 with a 5060 GPU is recommended, while the 7000 yuan range includes upgraded versions of the same model [29][31]. Group 3: Privacy and Security - Local deployment is emphasized as a necessity for applications involving sensitive data, such as business secrets or medical information, to prevent data leaks [17][18]. - The article argues that using local models ensures that all computations are performed on the user's hardware, eliminating the risk of data exposure to third-party services [16][17].