苹果的硬件综合能力还_老虎社区_美港股上老虎 - 老虎社区

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黑眼圈太重了邓文
07-15 13:08

苹果的硬件综合能力还是排第一梯队头部的，也只能运行270亿参数的压缩模型，要是以后运行27万亿参数的模型手机、那硬件要求不得到天上去，

苹果评估AI模型压缩技术：270亿参数大模型可直接运行于iPhone，为Siri升级铺路

苹果正与初创公司PrismML展开早期洽谈，后者周二发布压缩模型，将54GB大模型压缩至不足4GB，令270亿参数大模型可以直接跑在iPhone 15或更新机型上。本地处理意味着更低延迟、更强隐私保护，以及潜在的更低授权和云计算成本。

苹果评估AI模型压缩技术：270亿参数大模型可直接运行于iPhone，为Siri升级铺路

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href=\"https://laohu8.com/S/GOOG\">谷歌</a>的开源模型Gemma是下一个压缩目标，此后还将处理需要数据中心硬件才能运行的更大规模前沿模型。</p>\n<p>今年3月，该公司完成由Khosla Ventures领投的1625万美元种子轮融资，加州理工学院拥有底层专利并向其独家授权。</p>\n<div>\n<h2>苹果本地AI布局：隐私与效率的双重驱动</h2>\n</div>\n<p>苹果目前已在设备端本地运行部分AI功能，包括翻译、部分摘要生成及与个人信息密切相关的特性，较复杂的请求则路由至其私有云基础设施或外部模型。</p>\n<p>Asymco创始人Horace Dediu表示，<strong>苹果的目标很可能是将绝大多数日常Siri交互保留在设备端，仅将最高需求的任务交付云端处理。</strong>本地处理意味着更低延迟、更强隐私保护，以及潜在的更低授权和云计算成本。</p>\n<p>Creative Strategies总裁兼首席分析师Carolina Milanesi指出，更小的模型可让苹果将更多高需求功能移至iPhone本地，包括计算摄影、视频生成，以及依赖健康、药物等敏感数据的健康与健身工具。&#34;设备端能做的越多越好，&#34;她说。</p>\n<p>苹果在推进本地AI方面具备潜在优势——其芯片与软件协同设计，对AI在设备上的运行拥有更精密的控制权。</p>\n<div>\n<h2>分析师警示：规模化验证是关键门槛</h2>\n</div>\n<p>尽管技术指标亮眼，分析师普遍提示，PrismML的声明尚需在受控演示之外得到验证。</p>\n<p>Counterpoint Research研究总监Tarun Pathak表示，模型在长提示下的表现、多任务处理时的电池消耗，以及跨数百万次请求和数千种设备组合的可靠性，将是关键考量。他说：</p>\n<blockquote>\n<p><strong>&#34;最终考验将是数百万次查询、数千种设备组合以及大规模稳健测试。&#34;</strong></p>\n</blockquote>\n<p>IDC客户端处理器研究负责人Phil Solis则指出，功耗可能是最大的未知变量。一个足够强大、被频繁使用甚至在后台持续运行以执行智能体任务的模型，即便内存占用较低，<strong>也可能对手机电量造成显著消耗。</strong></p>\n<div>\n<h2>对芯片需求的影响：需求转移而非消失</h2>\n</div>\n<p>PrismML的发布也触及了市场对AI效率提升能否最终压缩芯片需求这一持续争论。</p>\n<p><a href=\"https://laohu8.com/S/MS\">摩根士丹利</a>估计，苹果在2027财年的每比特动态随机存取内存平均成本可能同比上涨约190%，NAND存储成本上涨约180%。该机构预计苹果将把iPhone 18同等起售机型的价格提高约200美元以保护利润率。</p>\n<p>PrismML表示，其压缩方案可使原本需要8块GPU运行的云端模型缩减至仅需1块，并让原本依赖服务器的模型迁移至手机和笔记本电脑。<strong>这可能降低单一AI任务所需的内存或算力，但并不必然导致整体芯片需求下滑。</strong></p>\n<p>D.A. Davidson分析师Gil Luria表示，模型瘦身并不会消除对处理器或内存的需求，<strong>只是将更多芯片从数据中心转移至手机等终端设备。</strong>&#34;你仍然需要GPU，仍然需要内存，&#34;他说。</p>\n<p>他还补充，在单一设备上运行AI实际上可能比共享数据中心基础设施效率更低，因为手机芯片大多数时候处于闲置状态。此外，<strong>效率突破往往催生更多使用需求，而非带来支出下降。</strong></p>\n<p>市场对&#34;AI所需内存少于预期&#34;的信号向来反应灵敏。今年3月，<a href=\"https://laohu8.com/S/GOOGL\">谷歌</a>发布TurboQuant论文——涉及在不损害模型性能的前提下削减内存使用——<a href=\"https://laohu8.com/S/MU\">美光科技</a>股价随即大幅下跌，尽管此后有所回升。</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\" />\n<title>苹果评估AI模型压缩技术：270亿参数大模型可直接运行于iPhone，为Siri升级铺路</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; 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}\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #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苹果评估AI模型压缩技术：270亿参数大模型可直接运行于iPhone，为Siri升级铺路\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\">2026-07-15 07:27</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>正与硅谷初创公司PrismML展开早期洽谈，后者声称可将大型AI模型压缩至能在iPhone上本地运行。若技术验证通过，此举将强化苹果的隐私主张，并为其迟滞已久的Siri升级提供关键支撑。</p>\n<p><strong>PrismML CEO表示，苹果已开始评估其技术。洽谈目前处于非常早期阶段，走向尚不明朗，但&#34;进展顺利&#34;。</strong></p>\n<p>此次消息发布恰在苹果推出iOS 27公测版的次日——这是苹果对Siri进行大幅改版后向公众开放的首次广泛测试，旨在与OpenAI和Anthropic的AI助手展开竞争。</p>\n<p>对于苹果而言，将更多AI运算留在设备本地，意味着更低的响应延迟、更少的云计算成本，以及无需联网即可使用部分功能的能力——这些与其核心隐私定位高度契合。</p>\n<p>PrismML的公开发布，为普通用户和投资者提供了在实验室之外检验其技术声明的机会。Counterpoint的Pathak总结道：</p>\n<blockquote>\n<p><strong>&#34;云端与设备端AI的组合，能够提供更完整、更高效、更注重隐私的AI体验——复杂任务交由云端处理，敏感、时延敏感及隐私相关的任务则在设备端执行。&#34;</strong></p>\n</blockquote>\n<div>\n<h2>压缩技术突破：54GB模型缩至4GB以内</h2>\n</div>\n<p><strong>PrismML由加州理工学院研究团队孵化而来，背后有<a href=\"https://laohu8.com/S/KVSA\">Khosla Ventures</a>支持。</strong>该公司周二公开发布了<a href=\"https://laohu8.com/S/BABA\">阿里巴巴</a>开源模型Qwen的压缩版本，将约54GB的原始模型压缩至不足4GB，使全部270亿参数能够在iPhone 15或更新机型上直接运行。</p>\n<p>PrismML CEO Babak Hassibi向CNBC表示，公司通过大幅简化模型内部信息的存储方式实现压缩——将每个数值从16位降至仅1至3个可能值，从而显著削减存储和运行模型所需的内存。</p>\n<p><strong>据该公司称，压缩后的模型内存占用减少10至15倍，响应速度提升6至8倍，能耗降低3至6倍。</strong>Hassibi坦承存在一定取舍：模型整体性能通常下降数个百分点，事实性记忆的衰减早于推理、数学和编程等能力。</p>\n<p><strong>目前，PrismML免费发布了两个压缩版本，适配iPhone、MacBook及搭载<a href=\"https://laohu8.com/S/NVDA\">英伟达</a>芯片的PC。</strong>公司表示，<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>的开源模型Gemma是下一个压缩目标，此后还将处理需要数据中心硬件才能运行的更大规模前沿模型。</p>\n<p>今年3月，该公司完成由Khosla Ventures领投的1625万美元种子轮融资，加州理工学院拥有底层专利并向其独家授权。</p>\n<div>\n<h2>苹果本地AI布局：隐私与效率的双重驱动</h2>\n</div>\n<p>苹果目前已在设备端本地运行部分AI功能，包括翻译、部分摘要生成及与个人信息密切相关的特性，较复杂的请求则路由至其私有云基础设施或外部模型。</p>\n<p>Asymco创始人Horace Dediu表示，<strong>苹果的目标很可能是将绝大多数日常Siri交互保留在设备端，仅将最高需求的任务交付云端处理。</strong>本地处理意味着更低延迟、更强隐私保护，以及潜在的更低授权和云计算成本。</p>\n<p>Creative Strategies总裁兼首席分析师Carolina Milanesi指出，更小的模型可让苹果将更多高需求功能移至iPhone本地，包括计算摄影、视频生成，以及依赖健康、药物等敏感数据的健康与健身工具。&#34;设备端能做的越多越好，&#34;她说。</p>\n<p>苹果在推进本地AI方面具备潜在优势——其芯片与软件协同设计，对AI在设备上的运行拥有更精密的控制权。</p>\n<div>\n<h2>分析师警示：规模化验证是关键门槛</h2>\n</div>\n<p>尽管技术指标亮眼，分析师普遍提示，PrismML的声明尚需在受控演示之外得到验证。</p>\n<p>Counterpoint Research研究总监Tarun Pathak表示，模型在长提示下的表现、多任务处理时的电池消耗，以及跨数百万次请求和数千种设备组合的可靠性，将是关键考量。他说：</p>\n<blockquote>\n<p><strong>&#34;最终考验将是数百万次查询、数千种设备组合以及大规模稳健测试。&#34;</strong></p>\n</blockquote>\n<p>IDC客户端处理器研究负责人Phil Solis则指出，功耗可能是最大的未知变量。一个足够强大、被频繁使用甚至在后台持续运行以执行智能体任务的模型，即便内存占用较低，<strong>也可能对手机电量造成显著消耗。</strong></p>\n<div>\n<h2>对芯片需求的影响：需求转移而非消失</h2>\n</div>\n<p>PrismML的发布也触及了市场对AI效率提升能否最终压缩芯片需求这一持续争论。</p>\n<p><a href=\"https://laohu8.com/S/MS\">摩根士丹利</a>估计，苹果在2027财年的每比特动态随机存取内存平均成本可能同比上涨约190%，NAND存储成本上涨约180%。该机构预计苹果将把iPhone 18同等起售机型的价格提高约200美元以保护利润率。</p>\n<p>PrismML表示，其压缩方案可使原本需要8块GPU运行的云端模型缩减至仅需1块，并让原本依赖服务器的模型迁移至手机和笔记本电脑。<strong>这可能降低单一AI任务所需的内存或算力，但并不必然导致整体芯片需求下滑。</strong></p>\n<p>D.A. Davidson分析师Gil Luria表示，模型瘦身并不会消除对处理器或内存的需求，<strong>只是将更多芯片从数据中心转移至手机等终端设备。</strong>&#34;你仍然需要GPU，仍然需要内存，&#34;他说。</p>\n<p>他还补充，在单一设备上运行AI实际上可能比共享数据中心基础设施效率更低，因为手机芯片大多数时候处于闲置状态。此外，<strong>效率突破往往催生更多使用需求，而非带来支出下降。</strong></p>\n<p>市场对&#34;AI所需内存少于预期&#34;的信号向来反应灵敏。今年3月，<a href=\"https://laohu8.com/S/GOOGL\">谷歌</a>发布TurboQuant论文——涉及在不损害模型性能的前提下削减内存使用——<a href=\"https://laohu8.com/S/MU\">美光科技</a>股价随即大幅下跌，尽管此后有所回升。</p></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/0d8ada6a43017d400bcffc8d2d50c562","relate_stocks":{"LU2083900584.USD":"HSBC GIF US INCOME FOCUSED \"AM2\" (USD) INC","LU0265550946.USD":"BGF SYSTEMATIC GLOBAL ENHANCED EQUITY YIELD \"A5\" (USD) INC","IE00B3SWFQ91.USD":"PIMCO BALANCED INCOME AND GROWTH \"E\" (USD) INC","LU1127390331.HKD":"AB SICAV I - ALL MARKET INCOME PORTFOLIO \"A\" (HKD) ACC","LU0472753341.HKD":"SUSTAINABLE GLOBAL THEMATIC PORTFOLIO \"A\" (HKD) ACC","LU2361044949.HKD":"WELLINGTON US QUALITY GROWTH \"A\" (HKD) ACC","IE00BK4W5L77.USD":"HSBC GLOBAL FUNDS ICAV US EQUITY INDEX \"HC\" (USD) ACC","LU2462157665.USD":"ALLIANZ GLOBAL INCOME \"A\" (USD) INC","LU2361044865.SGD":"WELLINGTON US QUALITY GROWTH \"A\" (SGDHDG) ACC","IE00BK4W5M84.HKD":"HSBC GLOBAL FUNDS ICAV US EQUITY INDEX \"HC\" (HKD) ACC","LU2417539215.USD":"ALLIANZ GLOBAL INCOME \"AMF\" (USD) INC","LU2361045086.USD":"WELLINGTON US QUALITY GROWTH \"A\" (USD) ACC","IE00BFSS8Q28.SGD":"Janus Henderson Balanced A Inc SGD-H","IE0004445239.USD":"JANUS HENDERSON US FORTY \"A2\" (USD) ACC","IXN":"iShares Trust S&P Global Informa","LU2237443382.USD":"Aberdeen Standard SICAV I - 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Davidson分析师Gil Luria表示，模型瘦身并不会消除对处理器或内存的需求，只是将更多芯片从数据中心转移至手机等终端设备。\"你仍然需要GPU，仍然需要内存，\"他说。\n他还补充，在单一设备上运行AI实际上可能比共享数据中心基础设施效率更低，因为手机芯片大多数时候处于闲置状态。此外，效率突破往往催生更多使用需求，而非带来支出下降。\n市场对\"AI所需内存少于预期\"的信号向来反应灵敏。今年3月，谷歌发布TurboQuant论文——涉及在不损害模型性能的前提下削减内存使用——美光科技股价随即大幅下跌，尽管此后有所回升。","news_type":1,"symbols_score_info":{"ABFL":0.6,"VT":0.6,"VGT":0.6,"TYLG":0.6,"AAPD":0.6,"AAPY":0.6,"XLK":0.6,"AAPU":0.6,"APLY":0.6,"FTEC":0.6,"AAPB":0.6,"AAPW":0.6,"JGRO":0.6,"METV":0.6,"IXN":0.6,"SPTE":0.6,"ROM":0.6,"AAPX":0.6,"AAPL":1.95}},"isVote":1,"tweetType":1,"viewCount":85,"commentLimit":10,"likeStatus":false,"favoriteStatus":false,"reportStatus":false,"symbols":[],"verified":2,"subType":0,"readableState":1,"langContent":"CN","currentLanguage":"CN","warmUpFlag":false,"orderFlag":false,"shareable":true,"causeOfNotShareable":"","featuresForAnalytics":[],"commentAndTweetFlag":false,"andRepostAutoSelectedFlag":false,"upFlag":false,"length":125,"optionInvolvedFlag":false,"xxTargetLangEnum":"ZH_CN"},"commentList":[],"hasMoreComment":false,"orderType":2}