1年多以前就在搞上手_老虎社区_美港股上老虎 - 老虎社区

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szymon
07-09

1年多以前就在搞上手机了。现在看P意义都没有

苹果设备端AI迎关键拼图？iPhone首次塞进270亿参数大模型

PrismML宣布，已成功将一个拥有270亿参数的AI大模型压缩至可在iPhone 17 Pro上本地运行，创下移动端AI模型规模的新纪录。消息称苹果已与其接触，该技术有望成为苹果强化设备端AI、减少对云端依赖的关键突破。

苹果设备端AI迎关键拼图？iPhone首次塞进270亿参数大模型

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3.6，参数量达270亿。相比之下，目前主流移动端模型每次仅有数十亿参数处于激活状态。</p>\n<p>苹果在今年6月的全球开发者大会上发布的新款设备端模型拥有200亿参数，但采用稀疏架构，每次仅有10亿至40亿参数处于激活状态。PrismML的模型则在运行时保持全部270亿参数同时激活，这一差异被该公司视为核心竞争优势。</p>\n<p>PrismML称，该模型能够胜任复杂对话、推理、全自主智能体及软件编程等任务。</p>\n<h2>数学压缩技术源自加州理工，专利独家授权</h2>\n<p>PrismML是加州理工学院（Caltech）的衍生公司。其CEO Babak Hassibi是该校电气工程学教授，与联合创始人在校期间完成了支撑该技术的数学研究。Caltech持有相关专利，并将其独家授权给PrismML。</p>\n<p>该公司的核心技术在于通过一种数学方法，将Qwen 3.6模型的体积从约54GB压缩至不足4GB，压缩比超过90%，且公司声称性能不受影响。</p>\n<p>PrismML今年早些时候完成了1625万美元的种子轮融资，Khosla Ventures参与其中。Khosla Ventures创始人Vinod Khosla在接受采访时表示，他对PrismML感兴趣，是因为该公司提供了一种&#34;根本性的突破&#34;。&#34;我们在2018年投资OpenAI时重注押注了Transformer模型，但构建AI的新方式是什么？我们的团队始终在寻找新的路径。&#34;他说。</p>\n<h2>苹果的设备端AI战略与潜在收购逻辑</h2>\n<p>苹果长期以来将设备端AI作为其隐私与安全承诺的核心支柱，并在很大程度上回避了<a href=\"https://laohu8.com/S/MSFT\">微软</a>、<a href=\"https://laohu8.com/S/AMZN\">亚马逊</a>、<a href=\"https://laohu8.com/S/META\">Meta</a>等科技巨头耗资数千亿美元的数据中心军备竞赛。</p>\n<p>然而，苹果在今年6月宣布的迟来已久的Siri重大升级，仍依赖<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>的Gemini模型，其最先进的功能需要调用运行在<a href=\"https://laohu8.com/S/GOOGL\">谷歌</a>云上的<a href=\"https://laohu8.com/S/NVDA\">英伟达</a>芯片。这一现状与苹果的设备端AI愿景存在明显落差，也使PrismML的技术对苹果具有潜在的战略价值。</p>\n<p>Hassibi预测，未来三年内，用户所需的绝大多数AI计算将在本地完成。&#34;想象一下，也许三年后，你所需要的95%的智能都可以在本地获得——在你的手机、笔记本电脑、家电上——真正需要去云端的，可能只剩最后5%的高端需求，&#34;他说，&#34;我认为这就是人们所看到的前进方向。&#34;</p>\n<h2>混合架构派提出挑战</h2>\n<p>并非所有业内人士都认同纯设备端AI的路线。初创公司Argmax等采用混合架构，将语音和图像等处理任务在设备端完成，再将信息上传至云端进行更复杂的推理。</p>\n<p>混合架构的支持者指出，云端大模型目前仍在以每周更新的速度快速迭代，完全运行在设备端的AI模型将难以享受到最新、最先进云端模型带来的性能红利。这一挑战也是PrismML在商业化路径上需要持续应对的核心问题之一。</p>\n<p>PrismML表示，公司计划继续将更大规模的模型——包括万亿参数级别的模型——压缩至设备端运行，届时将进入与OpenAI GPT及Anthropic Claude同台竞技的领域。</p></body></html>","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" 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#eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\n苹果设备端AI迎关键拼图？iPhone首次塞进270亿参数大模型\n</h2>\n\n<h4 class=\"meta\">\n\n\n<a class=\"head\" href=\"https://laohu8.com/wemedia/1084101182\">\n\n\n<div class=\"h-thumb\" style=\"background-image:url(https://static.tigerbbs.com/66809d1f5c2e43e2bdf15820c6d6897e);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">华尔街见闻 </p>\n<p class=\"h-time\">2026-07-09 21:50</p>\n</div>\n\n</a>\n\n\n</h4>\n\n</header>\n<article>\n<html><head></head><body><p><a href=\"https://laohu8.com/S/AAPL\">苹果</a>正寻求将更强大的AI能力留在设备端，一家由<a href=\"https://laohu8.com/S/KVSA\">Khosla Ventures</a>支持的初创公司或许提供了关键拼图。</p>\n<p>获Khosla Ventures投资的初创公司PrismML宣称，已成功将一个拥有270亿参数的AI大模型压缩至可在iPhone 17 Pro上本地运行，创下移动端AI模型规模的新纪录。该公司表示，其压缩技术不会造成性能损失，相关开源模型将于下周二正式发布。</p>\n<p>据知情人士透露，苹果已与PrismML就如何使用其技术举行了会谈。此前据The Information报道，苹果正积极寻求收购能够帮助其在设备端运行更多AI功能的公司。消息人士称，苹果去年在尝试将内部AI模型压缩至适配iPhone时，曾遭遇性能大幅下滑的困境。</p>\n<h2>270亿参数全量激活，刷新移动端AI纪录</h2>\n<p>PrismML表示，其压缩的模型为<a href=\"https://laohu8.com/S/BABA\">阿里巴巴</a>开发的开源大语言模型Qwen 3.6，参数量达270亿。相比之下，目前主流移动端模型每次仅有数十亿参数处于激活状态。</p>\n<p>苹果在今年6月的全球开发者大会上发布的新款设备端模型拥有200亿参数，但采用稀疏架构，每次仅有10亿至40亿参数处于激活状态。PrismML的模型则在运行时保持全部270亿参数同时激活，这一差异被该公司视为核心竞争优势。</p>\n<p>PrismML称，该模型能够胜任复杂对话、推理、全自主智能体及软件编程等任务。</p>\n<h2>数学压缩技术源自加州理工，专利独家授权</h2>\n<p>PrismML是加州理工学院（Caltech）的衍生公司。其CEO Babak Hassibi是该校电气工程学教授，与联合创始人在校期间完成了支撑该技术的数学研究。Caltech持有相关专利，并将其独家授权给PrismML。</p>\n<p>该公司的核心技术在于通过一种数学方法，将Qwen 3.6模型的体积从约54GB压缩至不足4GB，压缩比超过90%，且公司声称性能不受影响。</p>\n<p>PrismML今年早些时候完成了1625万美元的种子轮融资，Khosla Ventures参与其中。Khosla Ventures创始人Vinod Khosla在接受采访时表示，他对PrismML感兴趣，是因为该公司提供了一种&#34;根本性的突破&#34;。&#34;我们在2018年投资OpenAI时重注押注了Transformer模型，但构建AI的新方式是什么？我们的团队始终在寻找新的路径。&#34;他说。</p>\n<h2>苹果的设备端AI战略与潜在收购逻辑</h2>\n<p>苹果长期以来将设备端AI作为其隐私与安全承诺的核心支柱，并在很大程度上回避了<a href=\"https://laohu8.com/S/MSFT\">微软</a>、<a href=\"https://laohu8.com/S/AMZN\">亚马逊</a>、<a href=\"https://laohu8.com/S/META\">Meta</a>等科技巨头耗资数千亿美元的数据中心军备竞赛。</p>\n<p>然而，苹果在今年6月宣布的迟来已久的Siri重大升级，仍依赖<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>的Gemini模型，其最先进的功能需要调用运行在<a href=\"https://laohu8.com/S/GOOGL\">谷歌</a>云上的<a href=\"https://laohu8.com/S/NVDA\">英伟达</a>芯片。这一现状与苹果的设备端AI愿景存在明显落差，也使PrismML的技术对苹果具有潜在的战略价值。</p>\n<p>Hassibi预测，未来三年内，用户所需的绝大多数AI计算将在本地完成。&#34;想象一下，也许三年后，你所需要的95%的智能都可以在本地获得——在你的手机、笔记本电脑、家电上——真正需要去云端的，可能只剩最后5%的高端需求，&#34;他说，&#34;我认为这就是人们所看到的前进方向。&#34;</p>\n<h2>混合架构派提出挑战</h2>\n<p>并非所有业内人士都认同纯设备端AI的路线。初创公司Argmax等采用混合架构，将语音和图像等处理任务在设备端完成，再将信息上传至云端进行更复杂的推理。</p>\n<p>混合架构的支持者指出，云端大模型目前仍在以每周更新的速度快速迭代，完全运行在设备端的AI模型将难以享受到最新、最先进云端模型带来的性能红利。这一挑战也是PrismML在商业化路径上需要持续应对的核心问题之一。</p>\n<p>PrismML表示，公司计划继续将更大规模的模型——包括万亿参数级别的模型——压缩至设备端运行，届时将进入与OpenAI GPT及Anthropic Claude同台竞技的领域。</p></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/19d6eeb2b2e34cc336477cc4cf513d0e","relate_stocks":{"BK4566":"资本集团","LU2111349929.HKD":"ALLIANZ GLOBAL SUSTAINABILITY \"AM\" (HKD) INC","BK4505":"高瓴资本持仓","LU0310800379.SGD":"FTIF - Templeton Global A Acc SGD","LU2236285917.USD":"ALLIANZ GLOBAL INCOME \"AMG\" (USD) INC","LU1674673691.USD":"HSBC GIF GLOBAL LOWER CARBON EQUITY 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Pro上本地运行，创下移动端AI模型规模的新纪录。该公司表示，其压缩技术不会造成性能损失，相关开源模型将于下周二正式发布。\n据知情人士透露，苹果已与PrismML就如何使用其技术举行了会谈。此前据The Information报道，苹果正积极寻求收购能够帮助其在设备端运行更多AI功能的公司。消息人士称，苹果去年在尝试将内部AI模型压缩至适配iPhone时，曾遭遇性能大幅下滑的困境。\n270亿参数全量激活，刷新移动端AI纪录\nPrismML表示，其压缩的模型为阿里巴巴开发的开源大语言模型Qwen 3.6，参数量达270亿。相比之下，目前主流移动端模型每次仅有数十亿参数处于激活状态。\n苹果在今年6月的全球开发者大会上发布的新款设备端模型拥有200亿参数，但采用稀疏架构，每次仅有10亿至40亿参数处于激活状态。PrismML的模型则在运行时保持全部270亿参数同时激活，这一差异被该公司视为核心竞争优势。\nPrismML称，该模型能够胜任复杂对话、推理、全自主智能体及软件编程等任务。\n数学压缩技术源自加州理工，专利独家授权\nPrismML是加州理工学院（Caltech）的衍生公司。其CEO Babak Hassibi是该校电气工程学教授，与联合创始人在校期间完成了支撑该技术的数学研究。Caltech持有相关专利，并将其独家授权给PrismML。\n该公司的核心技术在于通过一种数学方法，将Qwen 3.6模型的体积从约54GB压缩至不足4GB，压缩比超过90%，且公司声称性能不受影响。\nPrismML今年早些时候完成了1625万美元的种子轮融资，Khosla Ventures参与其中。Khosla Ventures创始人Vinod Khosla在接受采访时表示，他对PrismML感兴趣，是因为该公司提供了一种\"根本性的突破\"。\"我们在2018年投资OpenAI时重注押注了Transformer模型，但构建AI的新方式是什么？我们的团队始终在寻找新的路径。\"他说。\n苹果的设备端AI战略与潜在收购逻辑\n苹果长期以来将设备端AI作为其隐私与安全承诺的核心支柱，并在很大程度上回避了微软、亚马逊、Meta等科技巨头耗资数千亿美元的数据中心军备竞赛。\n然而，苹果在今年6月宣布的迟来已久的Siri重大升级，仍依赖谷歌的Gemini模型，其最先进的功能需要调用运行在谷歌云上的英伟达芯片。这一现状与苹果的设备端AI愿景存在明显落差，也使PrismML的技术对苹果具有潜在的战略价值。\nHassibi预测，未来三年内，用户所需的绝大多数AI计算将在本地完成。\"想象一下，也许三年后，你所需要的95%的智能都可以在本地获得——在你的手机、笔记本电脑、家电上——真正需要去云端的，可能只剩最后5%的高端需求，\"他说，\"我认为这就是人们所看到的前进方向。\"\n混合架构派提出挑战\n并非所有业内人士都认同纯设备端AI的路线。初创公司Argmax等采用混合架构，将语音和图像等处理任务在设备端完成，再将信息上传至云端进行更复杂的推理。\n混合架构的支持者指出，云端大模型目前仍在以每周更新的速度快速迭代，完全运行在设备端的AI模型将难以享受到最新、最先进云端模型带来的性能红利。这一挑战也是PrismML在商业化路径上需要持续应对的核心问题之一。\nPrismML表示，公司计划继续将更大规模的模型——包括万亿参数级别的模型——压缩至设备端运行，届时将进入与OpenAI GPT及Anthropic 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