以下文章来源于直面AI ，作者涯角 直面AI . 聚焦前沿科技，抢先看到未来。 本文来自微信公众号： 直面AI ，作者：涯角，编辑：胡润，原文标题：《当全世界向云端大模型狂 奔，苹果选择回归设备》，题图来自：AI生成 几天前，苹果在 HuggingFace 上全面开源了视觉语言模型 FastVLM 和 MobileCLIP2，再次在 AI 社 区掀起震动。 这两款模型的直观特征只有一个字：快。FastVLM 在部分任务上的响应速度比同类模型快出 85 倍，并且能在 iPhone 这样的个人设备上流畅运行。但这并非一次孤立的技术秀。 与 MobileCLIP2 等开源模型一道，FastVLM 构成了苹果"B 计划"的核心：端侧 AI 小模型战略。 苹果亮剑小模型 用最通俗的语言解释FastVLM。它是一个"看得懂图、读得懂话"的多模态模型，重点有2个，1个是 名字里的"Fast"——快；另一个则是"VLM"。 正如其名，FastVLM最引人注目的特点就是"快"。这种快并非简单的性能提升，而是数量级的飞 跃，使其能够在手机、电脑等个人设备上实现以往需要云端服务器才能完成的实时任务。 因此，其作为VLM (视觉语言 ...

Core Viewpoint - Apple has open-sourced its visual language models FastVLM and MobileCLIP2 on HuggingFace, marking a significant move in the AI community, particularly focusing on edge AI small model strategies. Group 1: FastVLM Features and Performance - FastVLM is characterized by its speed, being 85 times faster than similar models in certain tasks and capable of running smoothly on personal devices like iPhones [2][6]. - The model's architecture includes a new hybrid visual encoder, FastViTHD, which reduces the number of tokens generated from high-resolution images, thus improving processing speed without sacrificing accuracy [7][9]. - FastVLM has multiple versions available, including 0.5B, 1.5B, and 7B, and can perform real-time tasks without cloud services, such as live browser subtitles [13][14]. Group 2: Apple's AI Strategy - Apple's "B Plan" focuses on small models for edge AI, contrasting with the industry trend towards large cloud-based models [3][40]. - The company has faced criticism for its slow progress in AI compared to competitors like Google and Microsoft, but it is now responding with significant investments and a clear strategy [36][39]. - Apple's approach emphasizes user privacy and seamless integration of hardware and software, which aligns with its core business model [43][49]. Group 3: Market Context and Implications - The interest in small models is rising across the industry, with various companies exploring their potential for specific vertical markets [54]. - Apple's focus on small models is seen as a strategic necessity to maintain its competitive edge and ensure user trust in privacy [50][56]. - The company's efforts in developing small models are positioned as a way to leverage its hardware capabilities while addressing the challenges posed by larger AI models [51][56].