[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"content-doc-dcio788525fe":3},{"user":4,"document":8,"mainDocument":27,"columnUrl":29,"subscription":30,"footer":42,"text":80},{"isAuthenticated":5,"isAdmin":5,"displayName":6,"avatarUrl":6,"nid":6,"groupLevel":7},false,"",-10,{"id":9,"fullTitle":10,"subTitle":6,"url":11,"columnId":12,"columnName":13,"columnUrl":14,"summary":6,"contentHtml":15,"mainContentHtml":6,"posterUrl":16,"createDate":17,"displayDate":18,"displayDateSlash":19,"pageviews":20,"tags":21,"hidden":5,"isSubContent":5,"replyDocOrTargetId":6,"contentType":23,"videoId":6,"liveVideoUrl":6,"useContentVideo":5,"duration":24,"price":24,"priceText":25,"priceBadgeText":25,"priceBadgeClass":26,"freeForMinGroupLevel":24,"redirectUrl":6,"readyToStream":5},"dcio788525fe","OpenAI自研芯片，不是终结 Nvidia，而是 AI 算力进入分层时代","\u002Fdoc\u002Fdcio788525fe","col18178739ee","美股资讯","\u002Fcol\u002Fcol18178739ee","\u003Cp>\n\u003C\u002Fp>\u003Cp>\u003Cspan>NVDA \u003Cspan>市场人士对\u003C\u002Fspan> OpenAI \u003Cspan>自研芯片的评价，我觉得最关键的一点是：\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>不要把\u003C\u002Fspan> Jalapeño \u003Cspan>简单理解成“\u003C\u002Fspan>OpenAI \u003Cspan>要替代\u003C\u002Fspan> Nvidia\u003Cspan>”。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>OpenAI \u003Cspan>和\u003C\u002Fspan> Broadcom \u003Cspan>推出的\u003C\u002Fspan> Jalapeño\u003Cspan>，本质上是一颗面向\u003C\u002Fspan> LLM \u003Cspan>推理优化的定制加速器，不是用来覆盖所有\u003C\u002Fspan> AI \u003Cspan>工作负载的通用\u003C\u002Fspan> GPU\u003Cspan>。\u003C\u002Fspan>OpenAI \u003Cspan>官方也明确说，这是其多代计算平台中的第一颗\u003C\u002Fspan> AI \u003Cspan>加速器，目标是让推理更快、更可靠、更容易扩展。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>真正的变化不是“\u003C\u002Fspan>OpenAI \u003Cspan>不买\u003C\u002Fspan> Nvidia \u003Cspan>了”\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>恰恰相反，\u003C\u002Fspan>OpenAI \u003Cspan>后续大概率仍然会大量采购\u003C\u002Fspan> Nvidia\u003Cspan>，因为训练、通用算力、生态软件、集群部署和供应链成熟度，不是一颗自研推理芯片短期能全部替代的。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>OpenAI \u003Cspan>在最新测试说明里也提到，会继续大规模部署来自\u003C\u002Fspan> Nvidia \u003Cspan>和其他合作伙伴的加速器，用于训练和推理工作负载。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>Jalapeño \u003Cspan>的意义在于：\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>OpenAI \u003Cspan>开始把自己最熟悉、最稳定、最重复的推理任务，逐步交给自研芯片来做。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>这和云巨头做\u003C\u002Fspan> TPU\u003Cspan>、\u003C\u002Fspan>Trainium\u003Cspan>、\u003C\u002Fspan>Inferentia \u003Cspan>的逻辑一样，在特定场景里降低成本、提升效率、减少对单一供应商的依赖。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>至于“\u003C\u002Fspan>Jalapeño \u003Cspan>比\u003C\u002Fspan> Blackwell \u003Cspan>更好”这个说法，我反而觉得要谨慎看。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>因为它是定制推理芯片，测试场景、模型类型、功耗、内存配置、软件栈都可能和\u003C\u002Fspan> Nvidia \u003Cspan>的通用平台不完全一致。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>Jalapeño \u003Cspan>赢的是某些特定推理场景，\u003C\u002Fspan>Nvidia \u003Cspan>强的是完整\u003C\u002Fspan> AI factory \u003Cspan>平台。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>一个是为\u003C\u002Fspan> OpenAI \u003Cspan>自己的工作负载量身定做。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>一个是面向全球客户、训练\u003C\u002Fspan>+\u003Cspan>推理\u003C\u002Fspan>+\u003Cspan>网络\u003C\u002Fspan>+\u003Cspan>软件生态的全栈系统。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>美股投资网认为，这件事对\u003C\u002Fspan> NVDA \u003Cspan>的真正影响，不是需求马上消失，而是未来\u003C\u002Fspan> AI \u003Cspan>算力会进一步分层：\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>训练和前沿模型：继续高度依赖\u003C\u002Fspan> Nvidia\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>大规模推理：逐步引入自研\u003C\u002Fspan> ASIC\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>云厂商内部负载：更多定制芯片\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>开放市场客户：仍然需要\u003C\u002Fspan> Nvidia \u003Cspan>平台\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>这才是重点。\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>OpenAI \u003Cspan>自研芯片不是\u003C\u002Fspan> Nvidia \u003Cspan>故事的结束，\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>而是\u003C\u002Fspan> AI \u003Cspan>算力市场从“\u003C\u002Fspan>GPU \u003Cspan>一统天下”，进入“通用\u003C\u002Fspan> GPU + \u003Cspan>定制\u003C\u002Fspan> ASIC \u003Cspan>并存”的阶段。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>对\u003C\u002Fspan> NVDA \u003Cspan>来说，短期最大风险不是\u003C\u002Fspan> OpenAI \u003Cspan>不买芯片，\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>而是市场开始重新评估：未来\u003C\u002Fspan> AI \u003Cspan>推理利润池里，\u003C\u002Fspan>Nvidia \u003Cspan>能拿走多少。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>━━━━━━━━━━━━━━━━━━━━━━\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>【原创声明】\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>本文由美股投资网（\u003C\u002Fspan>TradesMax.com\u003Cspan>）研究团队原创完成。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>原创机构：美股投资网\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>英文品牌：\u003C\u002Fspan>TradesMax\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>本文基于公开财报、\u003C\u002Fspan>SEC\u003Cspan>文件、市场数据、机构资金、期权交易及产业链信息进行独立分析\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>如需引用本文，请注明：\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>\u003Cspan>“美股投资网（\u003C\u002Fspan>TradesMax.com\u003Cspan>）原创研究”。\u003C\u002Fspan>\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003Cspan>━━━━━━━━━━━━━━━━━━━━━━\u003C\u002Fspan>\u003C\u002Fp>\n\u003Cp>\u003C\u002Fp>","https:\u002F\u002Fwww.tradesmax.com\u002Fimages\u002Fa_Stock\u002FN\u002FNVDA\u002FNVDA.jpg","2026-08-28T05:09:48","2026.08.28","2026\u002F08\u002F28",52128,[22],"NVDA","Article",0,"免费","success",{"id":9,"fullTitle":10,"subTitle":6,"url":11,"columnId":12,"columnName":13,"columnUrl":14,"summary":6,"contentHtml":15,"mainContentHtml":6,"posterUrl":16,"createDate":17,"displayDate":18,"displayDateSlash":19,"pageviews":20,"tags":28,"hidden":5,"isSubContent":5,"replyDocOrTargetId":6,"contentType":23,"videoId":6,"liveVideoUrl":6,"useContentVideo":5,"duration":24,"price":24,"priceText":25,"priceBadgeText":25,"priceBadgeClass":26,"freeForMinGroupLevel":24,"redirectUrl":6,"readyToStream":5},[22],"\u002Fcol\u002Fstocknews",{"visible":5,"marketingHtml":31,"services":32,"recentDocuments":41},"\u003Cfigure 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buy@TradesMax.com 美国电话 626-378-3637","公司介绍","\u003Cp class=\"MsoNormal\">美股大数据 \u003Ca href=\"https:\u002F\u002Fstockwe.com\" rel=\"noopener\">StockWe.com\u003C\u002Fa> 是一个美国领先的金融和美股信息大数据提供商，紧盯华尔街金融市场和行情，2008年成立于美国硅谷，创始人是前纽约证券交易所资深分析师Ken，联合多位摩根斯坦利分析师，谷歌 Meta工程师利用AI和大数据，配合十多年美股实战经验和业内量化交易模型，每天处理海量股票数据：挖掘潜力大牛股，捕捉期权异动大单，实时主力资金流向、机构持仓变化、川普突发新闻，美股买卖信号第一时间发到您手机APP。\u003C\u002Fp>","专业美股投资者都在这里",{"loading":81,"search":82,"searchPlaceholder":82,"hotContent":83,"draft":84,"noData":85,"searchNoData":86,"courseContent":87,"more":88,"buyNow":89,"subscribeNow":90,"encoding":91,"paidContent":92},"Loading...","搜索","热门内容","草稿","目前没有任何内容公布","当前检索内容没有数据","课程内容","更多","立即购买后观看","- 立即订阅 -","视频编码中...","付费内容"]