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- leisheng526leisheng526
  ·12-04
  苹果继续这么整花活，他的APPLE AI会永远停滞不前
  苹果采用亚马逊芯片，一个去英伟达化的信号？
  亚马逊宣布推出了未来可能替代英伟达GPU的AI芯片。AWS的Trainium2芯片，将用来构建一个40万卡的集群，训练下一代Claude模型，而且苹果宣布会使用它来训练和部署Apple Intelligence。但随着Trainium2的发布，亚马逊已经做出了重大的调整，正在芯片、系统和软件编译器/框架层面向英伟达产品看齐，提供有竞争力的定制硅芯片解决方案。苹果还一直使用亚马逊的Inferentia和Graviton芯片来支持搜索服务。苹果方面称，亚马逊的这些芯片带来了40%的效率提升。
  苹果采用亚马逊芯片，一个去英伟达化的信号？
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href=\"https://laohu8.com/S/NVDA\">英伟达</a>GPU的AI芯片。</strong>AWS的Trainium2芯片，将用来构建一个40万卡的集群，训练下一代Claude模型，<strong>而且<a href=\"https://laohu8.com/S/AAPL\">苹果</a>宣布会使用它来训练和部署Apple Intelligence。</strong></p><p>正在拉斯维加斯举行的re:Invent大会上，AWS推出的Trn2服务器（16片Trainium2）提供20.8 Pflops性能，可训练数十亿参数模型，试图媲美英伟达和AMD的GPU。Trn2 UltraServers（64片Trainium2）高峰时提供83.2 Pflops算力，完全可以用来训练和部署最大的模型，包括语言、多模态和视觉模型。</p><p>AWS还宣布了下一代AI芯片Trainium3的计划。预计将比Trainium2提升2倍的性能，改善40%的能效，3纳米制程，将于2025年底推出。</p><p>目前，亚马逊基于Trainium1和Inferentia2的实例，在生成式AI前沿模型训练或推理方面还不太具有竞争力，这是由于硬件规格较弱以及软件集成较弱所致。但随着Trainium2的发布，亚马逊已经做出了重大的调整，<strong>正在芯片、系统和软件编译器/框架层面向英伟达产品看齐，提供有竞争力的定制硅芯片解决方案。</strong></p><p><strong>苹果公司也非同寻常地对外介绍了它与云服务商之间的合作细节，并且表达出积极合作构建AI的意愿。</strong>苹果已经使用AWS服务超过十年，用于Siri、Apple Maps和Apple Music。苹果还一直使用亚马逊的Inferentia和Graviton芯片来支持搜索服务。苹果方面称，亚马逊的这些芯片带来了40%的效率提升。</p><p>苹果最近还将用Trainium2进行其自有模型的预训练。苹果对它的初步评估显示，预训练效率提升了50%。苹果在决定开发Apple Intelligence之后，马上就找到了AWS，寻求AI基础设施的支持。苹果也使用过<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>云的TPU服务器。</p><p>苹果正在引领着个人AI的应用方向，即把AI模型向端侧部署，主要用本地化的计算为用户提供定制化与个人化的AI服务，注重保护用户的隐私。</p><p>所以，对于苹果来说，<strong>最重要的不是用十万张卡去训练大模型，而是用AI更好地服务其20多亿设备用户，</strong>即在iPhone、iPad、Mac等设备上，用自研芯片提供本地算力，只有那些较复杂的计算任务才上云端。苹果还需要云服务商能配合它进行隐私计算。</p><p>Apple Intelligence有自己的步调，它先推出内容提炼、起草邮件、生成表情包等最基本的功能，很快会集成OpenAI的大模型服务，明年会加强Siri功能，因为<a href=\"https://laohu8.com/S/5RE.SI\">智能</a>体技术的加持，它更像个能办事的助理，调动手机App完成用户吩咐的任务。</p><p><strong>而且AWS正在与Anthropic合作，打造40万Trainium2卡级的算力集群，用来训练下一代的Claude大模型。</strong>这个项目名称为Project Rainer，将为Anthropic提供的算力5倍于训练现有模型的Eflops。亚马逊对Anthropic最新的40亿美元投资，实际上将用于这个40万卡集群，目前还没有其他主要客户。</p><p>马斯克的xAI已经建好了10万H100算力集群，而且放也豪言要再买30万张B200；扎克伯格正在用一个超过10万H100的集群加班加点地训练Llama4，更不用说微软/OpenAI等，10万H100已经成为参与军备竞赛的起步价。</p><p><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABAQMAAAAl21bKAAAAA1BMVEXy8vJkA4prAAAACklEQVQI12NgAAAAAgAB4iG8MwAAAABJRU5ErkJggg==\"/></p><p>但Trainium算力集群真的要挑战英伟达GPU，还要付出更多努力。据半导体咨询机构semianalysis分析，<strong>40万颗Trainium2的原始浮点运算性能仍少于10万GB200集群。</strong>这意味着由于阿姆达尔定律的限制，Anthropic仍将很难与竞争对手10万卡GB200集群匹敌。在40万颗Trainium2和EFA上执行集合通信将非常困难，因此Anthropic需要在异步训练方面进行一些重大创新。</p><p>*作者注：EFA代表Elastic Fabric Adapter，是AWS提供的一种高性能网络接口技术，主要用于支持高性能计算（HPC）和机器学习工作负载。</p><p>三大云巨头AWS、<a href=\"https://laohu8.com/S/MSFT\">微软</a>Azure和谷歌云，目前数据中心芯片主要来自英伟达、AMD和<a href=\"https://laohu8.com/S/INTC\">英特尔</a>。同时，它们也在积极探索自己研制芯片，会带来成本及定制化服务等方面的好处，既用于通用计算负载，也用于加速计算，如大模型的训练和推理。AWS称，通过Trainium，Anthropic的大模型Claude Haiku 3.5，速度比其他芯片提升了60%。</p><p>随着生成式AI日益进入大规模应用阶段，企业将会寻找更加适合具体应用、为客户定制化、价格更亲民、更具能效的芯片和算力解决方案。</p><p>2025年我们会看到一个趋势，<strong>更多的算力会部署到推理侧进行强化学习，以及AI的大规模应用，这些都对芯片、服务器、工具、架构、服务等提出新的定制化要求，</strong>从而为云服务商的硅技术和初创芯片企业带来新的机会。</p></body></html>","source":"ifeng_tech","collect":0,"html":"<!DOCTYPE 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href=https://tech.ifeng.com/c/8f3026eRYWB><strong>茶饮消息</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>亚马逊宣布推出了未来可能替代英伟达GPU的AI芯片。AWS的Trainium2芯片，将用来构建一个40万卡的集群，训练下一代Claude模型，而且苹果宣布会使用它来训练和部署Apple Intelligence。正在拉斯维加斯举行的re:Invent大会上，AWS推出的Trn2服务器（16片Trainium2）提供20.8 Pflops性能，可训练数十亿参数模型，试图媲美英伟达和AMD的GPU。...</p>\n\n<a href=\"https://tech.ifeng.com/c/8f3026eRYWB\">Web Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"LU0203202063.USD":"AB SICAV I - ALL MARKET INCOME PORTFOLIO \"A2X\" (USD) ACC","3NVD.UK":"LS 3X NVIDIA","SG9999015986.USD":"LIONGLOBAL DISRUPTIVE INNOVATION \"I\" (USD) ACC","SNVD.UK":"LS -1X NVIDIA","AAPL":"苹果","2NVD.UK":"2X NVIDIA ETP","LU2023250330.USD":"ALLIANZ INCOME AND GROWTH \"AMG\" (USD) INC","LU0215105999.USD":"SCHRODER ISF GLOBAL EQUITY \"A\" ACC","NVDS":"Tradr 1.5X Short NVDA Daily ETF","LU0784385170.HKD":"BGF GLOBAL MULTI ASSET INCOME \"A6\" (HKDHGD) INC","LU2211815571.USD":"ALLIANZ POSITIVE CHANGE 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(HKD) ACC","IE00BHPRN162.USD":"BNY MELLON BLOCKCHAIN INNOVATION \"B\" (USD) ACC","LU2213496289.HKD":"ALLIANZ INCOME AND GROWTH \"AT\" (HKD) ACC","LU2461242641.AUD":"WELLINGTON US QUALITY GROWTH \"A\" (AUDHDG) ACC","LU1066051811.HKD":"HSBC GIF GLOBAL EQUITY VOLATILITY FOCUSED \"AM2\" (HKD) INC","BK4566":"资本集团","LU0056508442.USD":"贝莱德世界科技基金A2","LU2265009873.SGD":"Eastspring Investments - Global Growth Equity AS SGD-H","LU1046421795.USD":"富达环球科技A-ACC","SGXZ51526630.SGD":"大华环球创新基金A Acc SGD","NVD":"GraniteShares 2x Short NVDA Daily ETF","IE00BMPRXR70.SGD":"Neuberger Berman 5G Connectivity A Acc SGD-H","LU2023250843.SGD":"Allianz Thematica Cl AT Acc H2-SGD","LU1935042215.USD":"MANULIFE GF GLOBAL MULTI-ASSET DIVERSIFIED INCOME  \"AA\" (USD) INC A","NVDD":"Direxion Daily NVDA Bear 1X Shares","LU1037948541.HKD":"AB LOW VOLATILITY EQUITY PORTFOLIO \"A\" (HKD) ACC","LU2275660780.HKD":"SCHRODER ISF GLOBAL CLIMATE CHANGE EQUITY \"A\" (HKD) ACC","LU1804176565.USD":"EASTSPRING INV GLOBAL GROWTH EQUITY \"A\" (USD) ACC","LU2237957902.USD":"NIKKO AM GLOBAL EQUITY \"F\" (USD) ACC"},"source_url":"https://tech.ifeng.com/c/8f3026eRYWB","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2488917961","content_text":"亚马逊宣布推出了未来可能替代英伟达GPU的AI芯片。AWS的Trainium2芯片，将用来构建一个40万卡的集群，训练下一代Claude模型，而且苹果宣布会使用它来训练和部署Apple Intelligence。正在拉斯维加斯举行的re:Invent大会上，AWS推出的Trn2服务器（16片Trainium2）提供20.8 Pflops性能，可训练数十亿参数模型，试图媲美英伟达和AMD的GPU。Trn2 UltraServers（64片Trainium2）高峰时提供83.2 Pflops算力，完全可以用来训练和部署最大的模型，包括语言、多模态和视觉模型。AWS还宣布了下一代AI芯片Trainium3的计划。预计将比Trainium2提升2倍的性能，改善40%的能效，3纳米制程，将于2025年底推出。目前，亚马逊基于Trainium1和Inferentia2的实例，在生成式AI前沿模型训练或推理方面还不太具有竞争力，这是由于硬件规格较弱以及软件集成较弱所致。但随着Trainium2的发布，亚马逊已经做出了重大的调整，正在芯片、系统和软件编译器/框架层面向英伟达产品看齐，提供有竞争力的定制硅芯片解决方案。苹果公司也非同寻常地对外介绍了它与云服务商之间的合作细节，并且表达出积极合作构建AI的意愿。苹果已经使用AWS服务超过十年，用于Siri、Apple Maps和Apple Music。苹果还一直使用亚马逊的Inferentia和Graviton芯片来支持搜索服务。苹果方面称，亚马逊的这些芯片带来了40%的效率提升。苹果最近还将用Trainium2进行其自有模型的预训练。苹果对它的初步评估显示，预训练效率提升了50%。苹果在决定开发Apple Intelligence之后，马上就找到了AWS，寻求AI基础设施的支持。苹果也使用过谷歌云的TPU服务器。苹果正在引领着个人AI的应用方向，即把AI模型向端侧部署，主要用本地化的计算为用户提供定制化与个人化的AI服务，注重保护用户的隐私。所以，对于苹果来说，最重要的不是用十万张卡去训练大模型，而是用AI更好地服务其20多亿设备用户，即在iPhone、iPad、Mac等设备上，用自研芯片提供本地算力，只有那些较复杂的计算任务才上云端。苹果还需要云服务商能配合它进行隐私计算。Apple Intelligence有自己的步调，它先推出内容提炼、起草邮件、生成表情包等最基本的功能，很快会集成OpenAI的大模型服务，明年会加强Siri功能，因为智能体技术的加持，它更像个能办事的助理，调动手机App完成用户吩咐的任务。而且AWS正在与Anthropic合作，打造40万Trainium2卡级的算力集群，用来训练下一代的Claude大模型。这个项目名称为Project Rainer，将为Anthropic提供的算力5倍于训练现有模型的Eflops。亚马逊对Anthropic最新的40亿美元投资，实际上将用于这个40万卡集群，目前还没有其他主要客户。马斯克的xAI已经建好了10万H100算力集群，而且放也豪言要再买30万张B200；扎克伯格正在用一个超过10万H100的集群加班加点地训练Llama4，更不用说微软/OpenAI等，10万H100已经成为参与军备竞赛的起步价。但Trainium算力集群真的要挑战英伟达GPU，还要付出更多努力。据半导体咨询机构semianalysis分析，40万颗Trainium2的原始浮点运算性能仍少于10万GB200集群。这意味着由于阿姆达尔定律的限制，Anthropic仍将很难与竞争对手10万卡GB200集群匹敌。在40万颗Trainium2和EFA上执行集合通信将非常困难，因此Anthropic需要在异步训练方面进行一些重大创新。*作者注：EFA代表Elastic Fabric Adapter，是AWS提供的一种高性能网络接口技术，主要用于支持高性能计算（HPC）和机器学习工作负载。三大云巨头AWS、微软Azure和谷歌云，目前数据中心芯片主要来自英伟达、AMD和英特尔。同时，它们也在积极探索自己研制芯片，会带来成本及定制化服务等方面的好处，既用于通用计算负载，也用于加速计算，如大模型的训练和推理。AWS称，通过Trainium，Anthropic的大模型Claude Haiku 3.5，速度比其他芯片提升了60%。随着生成式AI日益进入大规模应用阶段，企业将会寻找更加适合具体应用、为客户定制化、价格更亲民、更具能效的芯片和算力解决方案。2025年我们会看到一个趋势，更多的算力会部署到推理侧进行强化学习，以及AI的大规模应用，这些都对芯片、服务器、工具、架构、服务等提出新的定制化要求，从而为云服务商的硅技术和初创芯片企业带来新的机会。","news_type":1},"isVote":1,"tweetType":1,"viewCount":37,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0}],"defaultTab":"following","isTTM":false}