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"title": "AI亲自设计!OpenAI首款自研AI晶片「Jalapeño」亮相",
"shortTitle": "AI亲自设计!OpenAI首款自研AI晶片「Jalapeño",
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"description": "OpenAI 掏出了史上第一颗自研晶片,名字叫 Jalapeño,墨西哥辣椒,专为大模型推理设计。",
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"content": "<p>OpenAI 掏出了史上第一颗自研晶片,名字叫 Jalapeño,墨西哥辣椒,专为大模型推理设计。</p>\n<p style=\"text-align:center;\"><img src=\"https://cimg.cnyes.cool/prod/news/6511282/l/4d893d58e693172fff72640feb564855.jpg\" alt=\"AI亲自设计!OpenAI首款自研AI晶片「Jalapeño」亮相\" style=\"width:50%;height:auto;border-radius:6px;margin:15px auto;display:block;\"></p>\n\n\n<p>9 个月前,白纸一张。9 个月后,工程样片不仅跑通了 GPT-5.3-Codex-Spark,而且频率和功耗均已达到量产目标。</p>\n\n<p>更离谱的是,这颗晶片是 OpenAI 用自己的 AI 帮着画的。</p>\n\n<p>Jalapeño,墨西哥辣椒里辣度最温和的品种之一。OpenAI 拿它给第一颗晶片命名,潜台词很可能是:这只是入门级,后面可能还有更辣的。</p>\n\n<p>官方定位叫「Intelligence Processor」,一颗专为大模型推理设计的定制 ASIC。</p>\n\n<p>由 OpenAI 主导架构设计,Broadcom 负责晶片实现和网路互联,Celestica 做板卡和机架整合。</p>\n\n<p>通常来说,先进半导体产业,设计一颗高性能 ASIC 要 18 到 24 个月。Google TPU 两年一代,Amazon Trainium 差不多也是这个节奏。</p>\n\n<p>而 OpenAI 只用了 9 个月,一举刷新了先进半导体领域最快的 ASIC 开发周期。</p>\n\n<p>这颗晶片从第一根线开始就只为一件事优化:LLM 推理。而且,Jalapeño 不只跑 OpenAI 自家模型,架构上兼容全产业的 LLM。</p>\n\n<p>比性能更值得关注的,是这颗晶片怎么被造出来的。OpenAI 自家 AI 模型参与了这颗晶片的设计和优化。也就是说——AI 设计了一颗晶片,晶片反过来跑 AI,跑在上面的更强 AI 会设计下一代更强的晶片。</p>\n\n<p>AI,帮自己造了一副新身体。</p>\n\n<p>AI 设计晶片并不新鲜。Google 2021 年在 Nature 发表论文,用强化学习做晶片布局,速度比人类快几个数量级。</p>\n\n<p>此后 AlphaChip 连续优化了三代 TPU 布局方案。OpenAI 硬体团队的掌门人 Richard Ho,正是从这个圈子里走出来的。</p>\n\n<p>Google 近九年,TPU 高级工程总监,参与发明了 ML 设计晶片架构的方法,多个 TPU 专案首次流片即成功。</p>\n\n<p>之后去了光子计算公司 Lightmatter 担任高级副总裁,更早联合创办过 EDA 公司 0-In Design Automation。学术界、晶片设计、AI 硬体,整条路走了一遍。</p>\n\n<p>OpenAI 挖他来,大概率就是为了一件事:把「AI 辅助晶片设计」嫁接到自家模型上。</p>\n\n<p>晶片设计最磨人的不是「想方案」,而是无数次的设计—验证—修改—再验证循环。一颗先进晶片的验证要跑成千上万次,占掉整个周期的大半时间。</p>\n\n<p>AI 恰恰擅长做这种事——读取历史设计资料、生成 RTL 程式码、辅助验证与除错、优化布局布线。</p>\n\n<p>能实现 9 个月流片,靠的是 AI 替人扛掉了那「18 到 24 个月」里最磨人的一大块。</p>\n\n<p>用最会用锤子的人,来设计新锤子。用最懂 LLM 运行规律的模型,来设计专门跑 LLM 的硬体。</p>\n\n<p><em><strong>你的</strong><strong> ChatGPT</strong><strong> 帐单正在被重写</strong></em></p>\n\n<p>OpenAI 每年算力支出高达百亿美元级别,这些钱最终都要从你身上赚回来——20 美元月费、API 调用、Plus 和 Pro 订阅。</p>\n\n<p>推理成本砍半,普通用户最先感受到的是:回应速度变快。</p>\n\n<p>现在 ChatGPT 高峰期排队、转圈、长对话变慢,本质都是算力不够分。</p>\n\n<p>推理效率提升后,同等伺服器能服务更多用户,使用体验有望改善。</p>\n\n<p>其次:免费版能力有望开放更多功能。现在很多功能锁在 Plus 里——深度研究、高级资料分析、完整版语音对话。</p>\n\n<p>成本降低后,这些功能下放给免费用户的可能性就更高。而 OpenAI 需要的,正是让更多人用上 ChatGPT,而且能用得更久。</p>\n\n<p>再往远看:20 美元的月费可能还是 20 美元,但你拿到的东西将完全不同。</p>\n\n<p>去年这个价格买到的是 GPT-4o,明年同样的钱,可能买到的是一个能跑完整工作流程的 Agent。</p>\n\n<p>月费没变,能力却可能翻好几倍——推理成本下降带来的是一种「隐性降价」。</p>\n\n<p>但 Jalapeño 瞄准的不只是今天的 ChatGPT。</p>\n\n<p>Richard Ho 的思路是:为模型未来要去的方向设计硬体,而不是为它现在的样子设计。</p>\n\n<p>OpenAI 内部能看到未来 6 到 9 个月模型的演进方向,因此晶片也沿着这条路线提前打造。</p>\n\n<p>等 Agent 真正普及后,算力需求和今天的一问一答将完全不是同一个量级。</p>\n\n<p>说白了,推理成本降下来,受益的不只是 Pro 用户。学生、小企业、独立开发者,都可能用上今天只有大型客户才负担得起的 AI 能力。</p>\n\n<p>模型自己训练,晶片自己设计,推理自己优化,部署自己控制。换句话说,OpenAI 要做的是一家全栈 AI 公司。</p>\n\n<p>之前和 Cerebras 合作推理部署,OpenAI 已经摸清专用推理硬体的价值。现在自己下场打造,把经验变成可自主掌控的平台。</p>\n\n<p>Apple 和 Google 都走过这条路。但 OpenAI 的全栈策略又多了一层:用 AI 加速 AI 基础设施建设,再用更好的基础设施跑更强的 AI。如果这个循环成立,飞轮就会自己推动自己加速。</p>\n\n<p>首批 GW 级资料中心将与 Microsoft 等合作伙伴自 2026 年底开始部署。</p>\n\n<p>Jalapeño 只是第一代,下一代叫什么还不知道。但打造它的,高机率不再只是人类。</p>\n<div class=\"related-articles seo-links\">\n <h4>相关阅读</h4>\n <ul>\n <li><a href=\"/new/mg/article-11229.html\" title=\"〈美股早盘〉Fed升息警报降温!主要指数开高 标普再攻历史新高\">〈美股早盘〉Fed升息警报降温!主要指数开高 标普再攻历史新高</a></li>\n <li><a href=\"/new/mg/article-9833.html\" title=\"〈美股盘后〉SpaceX财报后狂泻逾13% 道琼改写新高 标普、那指终止连四涨\">〈美股盘后〉SpaceX财报后狂泻逾13% 道琼改写新高 标普、那指终止连四涨</a></li>\n <li><a href=\"/new/mg/article-898.html\" title=\"AI投资烧太大?祖克柏:当然会考虑出租AI算力 进军云端运算市场\">AI投资烧太大?祖克柏:当然会考虑出租AI算力 进军云端运算市场</a></li>\n <li><a href=\"/new/mg/article-13487.html\" title=\"美通膨预期会失控吗?高盛研究揭三大关键结论\">美通膨预期会失控吗?高盛研究揭三大关键结论</a></li>\n <li><a href=\"/new/mg/article-15099.html\" title=\"最差时机已过!高盛转多点火 美股记忆体族群普涨 SK海力士ADR创新高\">最差时机已过!高盛转多点火 美股记忆体族群普涨 SK海力士ADR创新高</a></li>\n <li><a href=\"/new/mg/article-8123.html\" title=\"SemiAnalysis:Kimi K3碾压Nemotron 3 Ultra 辉达「委员会模式」遭警讯\">SemiAnalysis:Kimi K3碾压Nemotron 3 Ultra 辉达「委员会模式」遭警讯</a></li>\n </ul>\n</div>",
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