Apple Silicon 五周年，无心插柳长出了一片AI 市场

爱范儿2025-12-08

本文来自微信公众号：爱范儿，作者：发现明日产品的，原文标题：《Apple Silicon 五周年，无心插柳长出了一片 AI 市场｜硬哲学》四台十万元的Mac Studio并联在一起，就能部署一套价值上百亿人民币的超大规模AI模型？听上去像是天方夜谭，但这的确就是Apple Silicon给苹果的自信。前几个月，爱范儿就曾经报道过来自牛津大学的Alex Cheema与Seth Howes共同创建的...

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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20251208183223a43b3cec&s=b><strong>爱范儿</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>本文来自微信公众号：爱范儿，作者：发现明日产品的，原文标题：《Apple Silicon 五周年，无心插柳长出了一片 AI 市场｜硬哲学》四台十万元的Mac Studio并联在一起，就能部署一套价值上百亿人民币的超大规模AI模型？听上去像是天方夜谭，但这的确就是Apple Silicon给苹果的自信。前几个月，爱范儿就曾经报道过来自牛津大学的Alex Cheema与Seth Howes共同创建的...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20251208183223a43b3cec&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU2065171311.SGD","symbol_name":"M&G (LUX) GLOBAL MAXIMA \"A\" (SGD) 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Silicon特化的“开源阵列框架”，它的主要作用就是让开发者在macOS程序中部署和微调自带的AI模型，并实现自带模型的“纯本地运行”。","collect":0,"end_time":0,"defaultTopTitle":"qq.com","property":[],"viewcount":null,"language":"zh","relate_stocks":{"LU2065171311.SGD":"M&G (LUX) GLOBAL MAXIMA \"A\" (SGD) ACC","LU1839511570.USD":"WELLS FARGO GLOBAL FACTOR ENHANCED EQUITY \"I\" (USD) ACC","LU2065171402.SGD":"M&G (LUX) GLOBAL MAXIMA \"A\" (SGD) INC","IE00BJTD4N35.SGD":"Neuberger Berman US Long Short Equity A1  Acc SGD-H","LU0823421333.USD":"BNP PARIBAS DISRUPTIVE TECHNOLOGY \"C\" (USD) ACC","LU2602419157.SGD":"HSBC ISLAMIC GLOBAL EQUITY INDEX \"AC\" (SGD) ACC","LU0823421416.USD":"BNP PARIBAS DISRUPTIVE TECHNOLOGY \"C\" (USD) INC","LU0943347566.SGD":"安联收益及增长平衡基金AM H2-SGD","IE00BFSS7M15.SGD":"Janus Henderson Balanced A Acc SGD-H","LU1935042215.USD":"MANULIFE GF GLOBAL MULTI-ASSET DIVERSIFIED INCOME  \"AA\" (USD) INC A","LU0861579265.USD":"联博低波幅策略股票基金A","IE0003U64NQ7.SGD":"PIMCO BALANCED INCOME AND GROWTH \"M\" (SGDHDG) ACC","LU0823434583.USD":"BNP 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inadvertently","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"MACW.SI":0.6,"AAPL":1.96},"content_text":"本文来自微信公众号：爱范儿，作者：发现明日产品的，原文标题：《Apple Silicon 五周年，无心插柳长出了一片 AI 市场｜硬哲学》四台十万元的Mac Studio并联在一起，就能部署一套价值上百亿人民币的超大规模AI模型？听上去像是天方夜谭，但这的确就是Apple Silicon给苹果的自信。前几个月，爱范儿就曾经报道过来自牛津大学的Alex Cheema与Seth Howes共同创建的Exo Labs公司，借助自己开发的分布式模型调度平台，将两台Mac Studio串联起来实现本地运行百亿级参数模型的事例：延伸阅读： 俩人拼出40万的Mac Studio“缝合怪”，双开满血DeepSeek不在话下苹果显然也注意到了Exo Labs的成果。在最新的macOS 26.2 Beta中，苹果为macOS的AI能力进行了一些极具针对性的加强，瞄准的方向正是Exo Labs所展现的——Mac集群化部署。简单来说，在最新版的macOS 26.2 Developer Beta中，苹果做出了两项重大改进：苹果开发的开源阵列框架MLX也可以调用M5处理器的神经网络加速器了Mac集群可以使用一种新的基于雷雳5协议的更高速传输通道了神经网络加速器“普惠万家”在刚刚更新的14寸MacBook Pro中，苹果除了给M5处理器用上最新的3nm N3P工艺之外，还在GPU的每个核上都加入了一个神经网络加速器，实现了跑本地模型“10核赶上24核”的效果：而macOS 26.2中更新的MLX框架，则进一步拓展了M5上神经网络加速器的使用场景：现在不仅“亲儿子”Apple Intelligence可以用，借助MLX部署的第三方AI模型也能用。MLX是一套苹果机器学习团队开发的、专为Apple Silicon特化的“开源阵列框架”，它的主要作用就是让开发者在macOS程序中部署和微调自带的AI模型，并实现自带模型的“纯本地运行”。这样一来，开发者在自己的app里部署模型，就可以用M5处理器的神经网络加速器提高响应速度了。同时还保留着自己（或者让用户）对模型进行微调的能力、灵活性比Apple Intelligence更高——直观地说，就是利好那些目前用不上Apple Intelligence的Mac用户，比如我们。况且，在MLX借助神经网络加速器“如虎添翼”之后，Mac的整体AI能力还会得到更进一步的提升。而这也为macOS 26.2 Beta中带来的另一项AI升级，打下了坚实的基础。正如前面提到的，Exo Labs的“Mac集群”方案，从最初勉强带动405B的Lllama，到最后顺跑671B的DeepSeek V3，借助他们设计的动态负载分配规则，几乎将Mac的本地AI性能推向了电脑的硬件上限。而macOS 26.2 Beta中所做的，则是进一步提升——或者说解锁了——Mac的能力上限，给类似Exo Labs的本地AI工具以更广阔的发挥空间。那苹果究竟做了什么，才能让Mac在不改变硬件的前提下，凭空多出来一部分本地AI性能呢？答案是，这个听起来和“下载免费内存”一样的神奇操作，实际上是通过优化Mac集群之间的数据传输方式实现的。雷雳5的一百种用法在之前版本的Exo Labs中，进行物理连接、建立拓扑网络、汇总统一内存池和进行负载分配的工作，都是借助Mac Studio之间的雷雳5信道完成的。然而尽管雷雳5的纸面规格很高，但macOS却只能通过古老的TCP-IP方式连接各台Mac。这就带来了一个问题：TCP-IP并不是专门为AI集群优化的协议。尤其是“并行AI计算”这种对带宽和延迟要求极高的场景，TCP-IP的“节点间延迟”还会被进一步放大。而macOS在设备互联的时候使用TCP-IP，进一步导致了Exo V2哪怕可以借助2TB统一内存加载一个巨大的模型，却只能使用相对低效的“管线并行”（pipeline parallel）方式将负载分配到四块处理器上——相当于这四台Mac Studio的集群，坐拥2TB内存和240个GPU核心，却只能等一个节点上的Mac Studio处理完，才能传给下一个节点的Mac Studio做进一步处理，任务分配效率并没有达到最优解。现在，这个协议导致的瓶颈终于得到了解决——苹果开发了一套新的以雷雳5为基础的macOS连接协议，在原本TCP-IP的基础上，为Mac提供了一种大幅改进传输延迟的新的“建群”方案。换句话说，现在仅凭Mac自带的雷雳5接口和控制器，就可以实现超低延迟的大带宽、低延迟交换。放在Mac集群里，相当于是让其中的每块M3 Ultra处理器在任意时间都能直接调度全部2TB的统一内存池。值得注意的是：虽然苹果这套新方案的效果非常像RDMA（远程内存直接访问），但并不需要RDMA那样设置以太网卡或者光模块，而是完全依赖现有的雷雳5硬件实现的。这样一来，原本Exo V2碰到的节点间通讯延迟（inter-node latency）问题就得到了极大的优化，也让Exo Labs得以在最新版本的软件Exo V3里，实现了Mac集群里的“张量并行”（tensor parallel）分配。相比“TCP-IP+管线并行”的组合，macOS 26.2 Beta的“雷雳5+张量并行”方案，可以极大优化负载的分配效率、让四台Mac Studio互相协调和分配任务的时间，从而进一步提升每秒钟生成的token数。甚至借助新的雷雳5传输方案，Exo V3还为构建集群的方式提供了灵活选择。现在不仅可以在不同型号的M系列处理器之间搭建集群，用户还可以自行选择让各个节点的Mac用TCP-IP或者雷雳5、管线并行还是张量并行，以实现各个场景的利用率最大化。这种同时来自苹果第一方和Exo Labs第三方的提升，是切实有效的。在最新版本的Exo V3中，我们甚至看到了在四台顶配M3 Ultra Mac Studio集群上，纯本地运行的Kimi-K2-Thinking，一个量化后约800GB内存的一万亿参数大语言模型。最终的输出速度达到了约25 token/秒。当然，Kimi-K2-Thinking是一个混合专家（Mixture-of-Experts，MoE）架构模型，生成每个token时并不需要调动全部一万亿的参数，实际部署的压力没有参数量那么恐怖。但这样规模的LLM能够仅靠四台Mac电脑就带动，依然是一件非常了不起的事情。根据估算，目前的主流闭源模型如Gemini 1.5和GPT-4等等，也都是1～2万亿参数的MoE架构，换个角度想想——花四十万或者八十万，组一个Mac Studio的大集群，你就可以自己在家跑一个独享的Gemini或者GPT了，并且没有任何限制、可以微调成你自己想要的任何样子。Mac的AI价值，其实在商业说到这里，macOS 26.2 Beta主要是在Mac的AI能力上进行了一次“提升式”更新。一边让第三方模型能用上M5芯片的新特性，一边让Mac组集群的效率更高，似乎不如老黄搬出DGX Spark那么让人兴奋——然而事实不是这样的。苹果借由macOS 26.2强化Mac的AI能力，实际上是和曾经自研处理器那样，正在看不见的地方悄悄努力、为最终的生态建设积攒能量。如前面所说，四台Mac Studio用Exo V3组成集群，就能本地运行近似GPT-4规模的巨型模型，乍看上去好像不是个很划算的买卖。毕竟能花四十万人民币买Mac的人，一般都有比本地跑模型更重要的事情要做。但对个人没有用，不代表对企业没有用。苹果暗自加强Mac的集群AI性能，正是瞄准了目前相对缺乏关注、规模效应没起来，但潜在用户极多的“企业本地部署”市场。出于商业信息保密和细分化需求的考虑，极少有企业会直接选择订阅AI巨头的在线服务、然后把业务内容、财务报表或者研发数据丢上去跑分析——对于这些高敏感数据，企业的AI功能需求往往是必须“纯本地化”的。然而，一旦要选择本地部署，很多企业用户就发现自己会迅速陷入了一个“水多加面、面多加水”的过程：买了显卡要放服务器，买了服务器要放机房，建了机房要配套地皮通风电力网络基础设施……到了那个阶段，买显卡甚至可能是总成本里最不起眼的那一块。这种时候，集成了CPU、GPU、共用内存、散热和供电，并且集群能力完全不输DIY主机的Mac Studio，就显得极为突出了——不仅Mac集群需要的空间和散热规模比散装服务器低了几个量级，成本最大的用电更是接近腰斩（前面运行Kimi-K2-Thinking时的总功耗仅为500W左右）。用一堆Mac Studio搭一个机房虽然也得上百万，但以三五年的跨度来计算，它的总拥有成本（Total Cost of Ownership，TCO）比起散装服务器，能省出好几个这样的机房。同时，它还保留着所有纯本地运行AI模型的优势：数据私密性、全方位的微调能力、动态负载分配（比如同时跑一个超大模型或者三四个小模型）等等。对于企业部署场景来说，其中的每一项可能都要比“绝对性能”更重要。无心插柳柳成荫用Mac搭集群、本地跑AI的有趣之处在于：这不像是苹果深思熟虑的产品战略，倒像是一个双向奔赴的意外。最初，在苹果设计Apple Silicon时，它追求的是统一架构、电脑能效比、跨端体验的一致性，并没有考虑AI模型会在几年后成为行业的主题。然而运行本地模型、隐私数据管控、硬件成本控制、数据中心节能——这些五年前还怎么不明显的需求，在2025年却逐渐变成了中小规模企业、工作室和开发者对AI业务的核心诉求。正是这两者的碰撞，造就了今天Mac作为“本地AI工作站”的无法替代的价值。此外，macOS 26.2 Beta是一次纯软件更新，后续所有支持雷雳5的Mac机型都能在“搭集群”的场景中受益。那些买Mac Studio的用户，并不是“十万元买了台艺术电脑”，反而会突然发现自己的设备价值倍增——这台当初买来剪视频、做设计的工作站，现在可以运行万亿参数的大模型了。虽然这像是一次意外，但恰恰也是技术储备的意义所在——技术的价值，有时会在意想不到的地方显现。此前苹果选择统一内存架构，是为了让Mac的体验和iPhone保持一致；推雷雳5，是为了支持更高规格的音画输出；在每个GPU核心里塞神经网络加速器，是为了Apple Intelligence。起初，没有人想到这些策略会在AI时代产生如此的化学反应。但当AI模型的行业化、规模化应用真正爆发时，这些“无心”的技术积累，恰好帮助Mac成为了最合适的解决方案之一。无心插柳柳成荫，这可能是对Mac AI能力最贴切的形容。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"productRelease","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":1,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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