黄仁勋：物理AI的ChatGPT时刻，快到了

极客公园01-12

英伟达这家公司，正在经历一种很矛盾的时刻。它看起来像所有泡沫叙事里最危险的那一个，毕竟市值已经冲到史无前例的高度，股价每一次波动都能牵动市场。但它又同时像所有 AI 概念股里最强的那一个，一个季度营收、净利润数百亿美元，毛利率能达到 70%。这也解释了为什么在拉斯维加斯 CES 上，英伟达的任何消息仍然吸睛，其 CEO 兼创始人黄仁勋演讲开头就称，“我们有 15 公斤的内容要塞进今天这场演讲里，...

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万亿美元的传统产业，会把研发预算里“几个百分点”整体转向 AI。钱不是凭空冒出来的，它来自整个世界把研发从“经典方法”搬到“AI 方法”，来自旧世界的预算向新世界的迁移，这就是为什么你会觉得这行业突然忙到离谱。他还把过去十年的 AI 历史重新串了一遍。2015 年 BERT 让语言模型第一次“有用”，2017 年 Transformer 奠定了基础，2022 年 ChatGPT 让世界第一次直观感受到 AI 的力量。可他认为真正关键的拐点发生在一年后，第一个 o1 推理模型出现了，让模型在推理时真的“思考”。紧接着，2024 年 Agentic AI 这个新物种开始出现，2025 年它迅速蔓延。这类系统不只是生成文本，它能查资料、用工具、规划步骤、模拟未来、拆解问题。它可以做“从未被明确训练过”的事情。这种能力，正在通过开源模型，向整个世界扩散。过去一年，真正让黄仁勋感到“兴奋”的，不是某一家闭源模型的进步，正是开源模型的集体跃迁。黄仁勋称，开放模型现在离前沿模型大约“落后六个月”，但每过六个月就会冒出更聪明的新模型，所以下载量爆炸，因为创业公司想参与，大公司想参与，研究者想参与，学生想参与，几乎每个国家都想参与。很多人误解英伟达，说它只是“卖 GPU”，而黄仁勋在 CES 台上反复强调一件事，即英伟达正在成为一个前沿开源模型构建者，而且是完全开放的那种。他在 CES 演讲上一口气报了一堆英伟达开源模型的工作，从混合 Transformer-SSM 的 Nemotron，世界模型 Cosmos，到人形机器人 Groot，黄仁勋称这些模型全部开放。连自动驾驶领域的 Alpamayo，不仅开源模型，还开源训练数据。黄仁勋认为，未来的 AI 不只是多模态，而是“多模型”。最聪明的系统，应该在不同任务中调用最合适的模型，最真实的系统，天然是多云、混合云、边缘计算的。这意味着什么？意味着 AI 应用的本质，其实是一套调度与推理的架构。一个能判断意图、选择模型、调用工具、组合结果的智能体。黄仁勋认为， 这种智能体正在成为新的“用户界面”，不再是 Excel，不再是表单，不再是命令行 。02 物理 AI 的真正到来如果说 Agentic AI 改变了软件，那么物理 AI，正在改变现实。黄仁勋在台上花了极长时间讲一件事，让 AI 理解世界的常识，比语言困难得多。物体恒存、因果关系、惯性、摩擦、重力，对人类幼儿来说是直觉，对 AI 来说却完全陌生。而现实世界的数据，永远不够。他说要做 physical AI，需要“三台计算机”：训练模型的、在车/机器人/工厂边缘推理的、以及用于仿真的。Omniverse 是数字孪生仿真世界，Cosmos 是世界基础模型，机器人模型则有 Groot 和他接下来要讲的 Alpamayo。训练数据从哪来？语言模型有大量文本，物理世界的真实视频很多，但远远不够覆盖多样交互。于是他们现在准备做的是，用符合物理定律的合成数据生成，选择性地制造训练样本。比如，交通模拟器输出本来不够丰富，他们把它喂给 Cosmos，让它生成物理可信的环视视频，拿来训练。Cosmos 还能从单张图生成逼真视频，从 3D 场景描述生成连贯运动，从传感器日志生成环视视频，从场景提示把“边缘案例”制造出来。还能闭环仿真，动作一做，世界响应，Cosmos 再推理下一步。黄仁勋甚至说“ physical AI 的 ChatGPT 时刻快到了 ”。他在台上宣布了 Alpamayo，称之为“世界第一个会思考、会推理的自动驾驶 AI”。它端到端从摄像头到执行器，既学了大量真实里程的“人类示范”，也学了 Cosmos 生成的里程，再加上“几十万”极其仔细的标注样本。关键是它不只输出方向盘和刹车油门，还会告诉你它将采取什么动作、为什么这样做、轨迹是什么。因为驾驶的长尾案例几乎不可能穷举采集，但长尾可以拆成很多更普通的子情境，让车用推理去组合应对。黄仁勋称，它能说清楚自己要做什么，为什么这么做，接下来会发生什么。这也是安全的前提。他解释他们八年前就开始做自动驾驶，是因为很早就判断深度学习会重做整个计算栈。如果要引导行业走向未来，必须亲手把整个栈做一遍。他搬出“AI 是五层蛋糕”：最底层是能源和壳，在机器人里比如车；上一层是芯片、网络；再上一层是基础设施如 Omniverse、Cosmos；再上是模型如 Alpamayo；最上层是应用，例如奔驰。Alpamayo 宣布“今天开源”，这套工程规模巨大，黄仁勋说他们的 AV 团队“几千人”，而奔驰五年前就与他们合作。他预测，未来可能有十亿辆车自动驾驶，“每一辆车都会有会思考的能力，每一辆车都会由 AI 驱动”。他还给出时间表：第一批在 Q1 上路，Q2 去欧洲，之后再去亚洲，Q3、Q4 逐步铺开，而且会持续更新 Alpamayo 的新版本。黄仁勋把自动驾驶定义为 physical AI 的第一个“大规模主流市场”，并断言“拐点就在现在这段时间”，未来十年世界上很大比例的车会高度自动化。更重要的是，这套“三台计算机 + 合成数据 + 仿真”的方法会迁移到所有机器人，包括机械臂、移动机器人、乃至全人形。03 Vera Rubin 的硬件秀讲完产业，黄仁勋最后把话题拉回硬件，介绍 Vera Rubin。他花了几十秒讲 Vera Rubin 的命名来源。Vera Rubin 这个名字本来是是 20 世纪的天文学家，她观察到星系边缘的旋转速度与中心差不多，这按牛顿物理学说不通，除非存在看不见的物质——暗物质。英伟达把下一代计算平台命名为 Vera Rubin，因为他们面临的“看不见的东西”也在膨胀：计算需求。黄仁勋描述了算力需求的疯狂，模型规模每年 10 倍增长，o1 之后推理变成“思考过程”，后训练引入强化学习，计算量暴增；test-time scaling 让每次推理 token 量再涨 5 倍；而且每次冲到新前沿，上一代 token 成本会以每年 10 倍速度下跌，这反过来说明竞赛极其激烈，他说因此英伟达必须“每年推进一次计算的 state-of-the-art，一年都不能落下”。他们已经在出货 GB200，在全规模量产 GB300，而 Vera Rubin“今天可以告诉你，已经全面投产”。Vera Rubin 的系统是一套“六芯协同”的架构，Vera 是定制 CPU，性能翻倍；Vera 与 Rubin GPU 从一开始就双向一致性共享数据，更快更低延迟；一块计算板上有 17000 个组件；每块板可以做到 100 PFLOPS 的 AI 算力，是上一代的五倍。网络侧 ConnectX-9 为每 GPU 提供 1.6 Tbps 的横向带宽；BlueField-4 DPU 把存储与安全卸载出去；计算托盘被重新设计到“没有线、没有水管、没有风扇”的形态；第六代 NVLink 交换把 18 个节点连成一体，扩展到 72 个 Rubin GPU 像一个 GPU 一样工作；再往外是 Spectrum-X 以太网光子学，512 通道、200G、共封装光学，把成千上万机柜组成“AI 工厂”。黄仁勋还提到现实的行业难题。英伟达内部原本有条规则，新一代产品最多换一两个芯片，别把供应链折腾死。可摩尔定律放缓后，晶体管增长跟不上模型十倍、token 五倍、成本十倍下滑的速度，你不做“co-design（协同设计）”就不可能追上，所以这一代不得不把每一颗芯片都重新设计。Rubin GPU 浮点性能是 Blackwell 的 5 倍，但晶体管只有 1.6 倍，这意味着单靠制程堆晶体管已经到天花板了，必须靠架构与系统级协同设计才能搞出性能。另外，英伟达还宣布推出针对 AI 上下文记忆的存储平台。模型每生成一个 token，都要读模型、读上下文、写回 cache，对话越长，上下文越大。黄仁勋称，你希望 AI 记住“一生的所有对话”，HBM 装不下了，Grace 直连 GPU 做“快速上下文内存”也不够了，继续往外走到数据中心存储又会被网络堵死。于是他们做了“机柜内的超高速 KV cache 存储”，由 BlueField 跑整套上下文管理系统，把它放得离 GPU 极近。在一个节点里，每 GPU 本来大约 1TB，而通过这套机柜内存储，每 GPU 还能多拿到 16TB 的上下文空间；每块 BlueField 后面挂着 150TB 级别的上下文存储，走同一套东西向高速织网，这不是“再加点盘”，而是“新增一类存储平台”。黄仁勋还提到了新硬件的三种系统级能力。第一，45 摄氏度水就能冷却，整体能效提升巨大，他称这能省掉全球数据中心约 6% 的电力；第二，机密计算，传输中、静止时、计算中都加密，PCIe、NVLink、CPU-GPU 互联全部加密，让企业敢把模型交给别人跑；第三，电力平滑，因为 AI 工作负载在 all-reduce 等阶段会瞬时拉高电流，可能尖峰 25%，过去不得不多预留 25% 供电预算，现在通过电力平滑能把预算用满，不必浪费。在他们模拟的 10 万亿参数、100 万亿 token 训练任务里，Rubin 的吞吐高到只需要 Blackwell 四分之一的系统规模就能在同样一个月窗口里训完。Rubin 相对 Hopper 的提升、相对 Blackwell 的再提升，被他形容成一轮接一轮的数量级跃迁，而 token 成本又能再降一个数量级，这就是他们所谓“把所有人推到下一前沿”的方式。黄仁勋认为，英伟达今天不是只做芯片，而是做从芯片、基础设施、模型到应用的“全栈”，而 AI 正在重做全世界的计算栈，他们的工作是把整套栈造出来，让所有人去造应用。在 CES 这种偏消费电子的舞台上，英伟达占据高光位置，可以说它也是一种情绪资产。乐观时，它是 AI 的石油，悲观时，有人直接说它像 2000 年的思科。市场不仅在乎它赚了多少钱，还在乎“AI 还会不会继续烧钱”。它的波动甚至开始影响指数本身，因为它太大了，大到一个公司的涨跌可以改变整个市场的体感温度。当一家公司同时掌握技术叙事、产业链重要位置、以及金融市场注意力时，它就会变成图腾。图腾的好处是信仰带来溢价，坏处是溢价意味着你不能犯错。所以问题来了，英伟达会不会掉下去？当然可能。任何站在山顶的公司都可能犯错，尤其当它大到一定程度，增长本身就变成一种负担。但同样必须承认的是，英伟达也在用尽一切办法降低这些风险。其目前主要战略仍是努力把自己从卖芯片变成卖生态。当硬件、网络、调度、软件工具链都握在手里时，英伟达就不再仅仅领先于竞争对手一代 GPU，而是在领先系统复杂度。目标至少是，让对手即便追上算力，也很难追上生态摩擦成本。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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