NVIDIA GTC 柏林 2026：黄仁勋谈 AI 工业革命

欢迎 NVIDIA 创始人兼 CEO 黄仁勋上台。

Welcome to the stage, NVIDIA founder and CEO, Jensen Huang.

你好，柏林！

Hello, Berlin!

欢迎来到 GTC。很高兴来到这里。在开始之前，让我们为我们的预热节目主持人鼓掌。

Welcome to GTC. It's great to be here. Before we start, let's give it up for our pre-game show hosts.

聪明又可爱。他们谈到了 AI 基础设施、主权 AI、AI 安全和物理 AI。今天上午他们覆盖了大量内容，而我们将更进一步。

Smart and adorable. They spoke about AI infrastructure, sovereign AI, AI safety, and physical AI. They covered an enormous amount of ground this morning, and we are going to take it one step further.

有趣的是，欧莱雅、Revolut、Spotify、默克、沃达丰、Zara、保时捷、雀巢和汇丰有什么共同点。嗯，它们都是欧洲公司，而且都在与 NVIDIA 合作开展 AI 业务。这里的每个人都在 AI 生态系统中活跃起来，并朝着 AI 经济迈进。

It's really interesting what do L'Oreal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé, and HSBC have in common. Well, they're all European companies and they're all working with NVIDIA on AI. Everybody here is all activated on the AI ecosystem and working towards the AI economy.

我们在欧洲已经工作了 33 年。你们可能没有意识到，NV1、NVIDIA 的第一款芯片 NV2 和 NV3，也就是后来成为 Riva 128 的芯片——这款芯片真正拯救了我们的公司并推动了公司的发展——都是在欧洲制造的。我们的第一个合作伙伴是 SGS Thompson。我们在法国 Crolles 制造了一款芯片，在马耳他组装，然后运往世界各地。

We've been working here in Europe now for 33 years. You probably don't realize that NV1, NVIDIA's first chip, NV2, and NV3, which became Riva 128, the chip that saved our company, really, and launched our company, were all built in Europe. Our first partner was SGS Thompson. We manufactured a chip in Crolles, France, assembled it in Malta, and shipped it around the world.

我们最早的 largest 客户，GeForce 早期最大的客户，是一家叫 MediaMarkt 的公司。你们知道他们吗？MediaMarkt 有一个关于组装 PC 的绝妙想法，你们记得的，他们是我们最大的客户。是 Elsa，一家德国公司，在 2000 年将 NVIDIA 带入了工作站行业。现在，NVIDIA 遍布欧洲的每一台工作站，用于设计汽车。从造型、设计、工程、模拟，一直到汽车内部，这是一家欧洲公司——奥迪在 2005 年将我们带入的。然后在 2020 年，我们开始与梅赛德斯合作开发自动驾驶汽车。

Our first largest customer, the largest customer of GeForce days in the beginning, was a company called MediaMarkt. Do you know them? MediaMarkt had a spectacular idea about building PCs, as you remember, and they were our largest customer. It was Elsa, a German company, that brought NVIDIA into the workstation industry in the year 2000. Now, NVIDIA is in every single workstation all over Europe designing cars. Everything from styling, design, engineering, simulation, all the way through to even inside the car, which a European company brought us into, Audi, in the year 2005. Then in year 2020, we started working on autonomous vehicles with Mercedes.

正如你们所见，NVIDIA 的发展历程，整整 33 年，我在德国度过了很多时间。所以，我很高兴回来。谢谢。

As you could see, NVIDIA's life journey, the entire 33 years, I've spent a lot of time in Germany. So, I'm delighted to be back. Thank you.

我很高兴在这里举办 GTC。今年，去年，我们开始了在欧洲建立新产业的新征程，这是 AI 经济的开端。仅在过去一年里，我们在欧洲投资了近 100 亿美元，涉及初创企业、大型合作伙伴、小型合作伙伴，遍布欧洲。仅上周，当我从法国和英国到瑞典，现在到柏林，我们宣布了对 14 家初创公司的投资。总的来说，从一开始，我们现在在欧洲有超过 12,000 个合作伙伴关系。我们现在都在朝着一个目标努力。

I'm delighted to do GTC here. This year, this last year, we started the new journey to building a new industry here in Europe, the beginnings of the AI economy. In the last year alone, we've invested nearly 10 billion dollars in Europe across startups, big partnerships, small partnerships, all over Europe. This last week alone, as I journeyed from France and UK to Sweden and now here in Berlin, we announced the investments of 14 startup companies. Overall, since the beginning, we now have 12,000 plus partnerships across Europe. And we're all working towards one goal now.

经过计算机行业几十年的发展，我们现在正处于一场新工业革命的开端。这张图片是由计算机生成的。是我提示生成的。一个关于 AI 推动新工业革命的简单提示生成了这个。我给了它几个我想强调的合作伙伴，我们正在合作的公司，这是一件非凡的事情。在 AI 的思维中，这就是建立新产业的意义，生成 token，生产智能，并融入每一个行业，推动新工业革命。

After decades after decades of advancement in the computer industry, we are now at the beginnings of a new industrial revolution. This image was generated by computer. It was prompted by me. Nice simple prompt about AI powering a new industrial revolution generated this. I gave it a few partners that I wanted to highlight, companies that we're working with, and this is an extraordinary thing. In the mind of the AI, this is what it means to build a new industry, generation of tokens, the production of intelligence, which integrates into every single industry powering a new industrial revolution.

当新技术影响我们如何建造某物以及我们能建造什么时，新工业革命就会发生。我们如何建造以及我们能建造什么。我认为在各个方面，人工智能将影响我们建造的一切，并实际上使我们能够建造新事物。这样，因为每个人都会使用它，每家公司都将由它驱动，因为它是数字智能，是一切的基础。每个人都会使用它，每家公司都将由它驱动，每个行业都将被改变，新的行业将被创造。这样，就像电力一样，这是一场新工业革命，而我们正处于其开端。

A new industrial revolution happens when the new technology affects how we build something and what we can build. How we build something and what we can build. I think in every single way, artificial intelligence will affect everything we build and in fact enable us to build new things. In this way, because everyone will use it, every company will be powered by it, because it's digital intelligence, the foundation of everything. Every single person will use it, every company will be powered by it, every industry will be transformed, and new ones will be created. In this way, like electricity, it is a new industrial revolution, and we're at the very beginnings of that.

问题是 AI 是什么？这很重要。如果你想参与创造这个未来，理解 AI 是什么至关重要。这其实是一个简单的框架。我们很早就观察到，AI 实际上是一个制造业。AI 不仅仅是软件技术。它是软件技术，但不仅仅是。AI 当然运行在计算机上，但计算机的运行方式根本不同。它不再是我们使用的工具，而是机械的一部分，基础设施，生产智能的工厂，并融入每一个行业和应用。

The question is what is AI? And this is a big deal. If you want to be part of creating this future, understanding what is AI is vitally important. And this is really a simple framework. We were early to observe that in fact AI is a manufacturing industry. AI is not just software technology. It is software technology, but not just. AI of course runs on a computer, but the way that that computer operates is fundamentally different. Rather than a tool that's used by us, it is now part of a machinery, an infrastructure, factories that are producing intelligence that are integrated into every single industry and application.

一个简单的模型是把 AI 想象成一个五层蛋糕。能源为先进计算机提供动力，这些计算机是 AI 工厂的一部分，生成 token，即数字智能，来自运行在其上的模型，并融入每一个应用。一个五层蛋糕。要成为这个 AI 经济的一部分，你不想只是消费 AI，你想生产 AI。我们可以在欧洲完成所有这些。事实上，我们已经开始了。

A simple model is to think about is think of AI as a five-layer cake. Energy powers advanced computers that are part of an AI factory that generates tokens, digital intelligence from the models that run on it, that are integrated into every application. A five-layer cake. To be part of this AI economy, you don't want to just be consuming the AI, you want to be producing the AI. We could do all of that here in Europe. And in fact, we've already started.

欧洲正在建设其 AI 经济。这里展示的是五层蛋糕。我们有很好的合作伙伴关系，当你审视这个五层蛋糕时，你会认识到它确实是一个新产业，因为每一层都在增长。我们正在创造就业机会，以惊人的速度创造增长。欧洲的公司，施耐德、西门子电气和西门子能源在全球表现非常出色。我们是计算层。我们融入现在遍布欧洲的 AI 工厂。像 Nebius 这样的初创公司表现非常出色。一家名为 Firebird 的新公司正在中亚地区建立新的云。甚至非洲的 Cassava。

Europe is building its AI economy. And it's five-layer cake represented here. We've got great partnerships, and when you look across this five-layer cake, you could recognize that in fact it is a new industry because there's growth at every single layer. We're creating jobs, creating growth at extraordinary rate. Companies here in Europe, Schneider, Siemens Electric, and Siemens Energy in doing incredibly well around the world. We're the computing layer. We integrate into AI factories that are now flourishing all over Europe. Startup companies like Nebius doing incredibly well. A new company called Firebird, which is setting up a new cloud in the Central Asia region. And even Cassava in Africa.

未来每一家电信公司，我预测，未来每一家电信公司，拥有 10 万个站点，代表约 100 吉瓦的分布式电信基站和数据中心，未来将演变成分布式 AI 网络。

Every single Telco in the future, and I predict this, every single Telco in the future, with 100,000 sites representing some 100 gigawatts of distributed Telco base stations and data centers will in the future evolve into a distributed AI network.

所以，AI 基础设施层正在建设中。然后在其之上是 AI 模型。必须认识到，在 AI 模型出现之前很久，基础设施就必须存在。就像在拥有互联网之前，你必须先有网络。就像在真正享受电力带来的能力之前，你必须先有电气化的房屋、电气化的发电厂。因此，模型是下一层。

So, the AI infrastructure layer is being built out. Which is then above that, the AI models. It's really important to recognize that long before the AI models emerge, the infrastructure has to exist. Just as before you could have an internet, you have to have the networking. Just as before you could really enjoy the capabilities of electricity, you have to have electrified houses, electrified power plants. And so, the models are the next layer.

同样有趣的是，模型层不仅仅是语言模型。它代表语言、生物学、化学、物理学、机器人动作、动画。它代表各种规模的模型。这些不同的行业将由不同模态的 AI 模型驱动，然后整合到 AI 原生公司、初创企业中。欧洲这里有很多这样的公司。所以，欧洲的 AI 经济正在起步，而且确实在加速。很高兴看到这一点。

It's also interesting to observe that the model layer is not just language models. It represents language, biology, chemicals, physics, robotic actions, animation. And it represents models of all sizes. These different industries are going to be powered by different modalities of AI models, which then integrate into the AI natives, the startups. And there are so many here in Europe. So, Europe's AI economy is starting, and it's really accelerating. It's great to see.

在其基础层面，也就是我们贡献的地方，大多数人认为是 GPU。没错。我们 1993 年开始制造图形芯片，后来演变成 GPU，即可编程着色 GPU。随着时间的推移，我们为此添加了 CUDA 加速，重新发明了我们所知的計算，从而催生了人工智能。但今天的 NVIDIA 实际上是一个全栈、端到端、完整的 AI 工厂平台。

At the foundation of it, where we contribute, most people think is GPUs. It is true. We started in 1993 building a graphics chip, which emerged into the GPU, the programmable shading GPU. Over time, we added CUDA acceleration to that, reinvented computing as we know it, which then brought forth artificial intelligence. But, NVIDIA today is really a full stack, end-to-end, complete AI factory platform.

多年来，我们公司的技术广度确实扩大了。从 GPU 计算（Vera Rubin 是我们最新一代），到将它们连接成一个巨型 GPU 的 NVLink 交换机，到我们最近推出的 Vera BlueField 4 计算（我稍后会简单介绍），到 Spectrum-X，这是我们的以太网解决方案，使 NVIDIA 成为当今世界上最大的网络公司，到 Groq 3，用于生成文本、生成 token 的解码加速器，到 Vera 4，Vera BlueField 4 存储加速（我马上会简单介绍），然后是 Vera RTX 计算，我马上会提到。但这现在代表了创建 AI 工厂所需的技术广度。

The breadth of technology of our company over the years has really expanded. Everything from GPU computing, Vera Rubin is our latest generation, to the NVLink switches that connect them into one giant GPU, the Vera BlueField 4 compute that we recently introduced, and I'll say a few words about that later, to Spectrum-X, which is our Ethernet solution, making NVIDIA the largest networking company in the world today, to Grok 3, a decode accelerator for generation text for token generation, to Vera 4, Vera BlueField 4 storage acceleration, which I'll say a few words in that in just a second, and then Vera RTX compute, and I'll mention that in just a second. But, this now represents the breadth of technology necessary to create the AI factory.

但除非你有软件来激活一切，否则什么都无法工作。第一部分是运行 AI 工厂的系统软件。这些系统非常庞大。一个 1 GW 的数据中心，1 GW 的 AI 工厂，成本约为 500 亿美元，包括土地、电力、外壳、所有计算、网络、存储、电源、冷却，全部连接在一起，占地数英亩的 AI 工厂。仅仅启动和运行它本身就是一项奇迹。为这个工厂供电、投入使用并生产 token 所需的大量软件是一项非凡的壮举。NVIDIA 提供整个全栈软件。

But, nothing works unless you have software that activates it all. The first part is the system software that runs the AI factory. These systems are gigantic. A 1 GW data center, 1 GW AI factory, costs about $50 billion land, power, shell, all of the compute, networking, storage, power, cooling, all connected together, multiple acres of AI factory. Just turning it on and operating it is a marvel in itself. The magnificent amount of software necessary to energize this factory, to put it to use and produce tokens is an extraordinary feat. Nvidia offers the entire full stack software.

在此基础上是我们所谓的 CUDA X 库，即我们公司的算法。NVIDIA 本质上是一家算法公司，设计硅芯片来加速这些算法。当然，其中之一是 cuDNN，许多研究人员认为这是引发现代 AI 大爆炸的库，cuDNN。cuDNN 之于 AI 就像 SQL 之于世界数据库行业。它是一种领域特定语言，一个领域特定库。就像 OpenGL 和 DirectX 实现了实时计算机图形一样，cuDNN 实现了 AI 计算行业。还有 350 个其他库，这些库实际上是我们公司的皇冠上的明珠。从计算光刻到信号处理、图像处理、数据处理、粒子、流体、稀疏求解器，令人难以置信的库广度，在座的许多人已经将这些库连接到你们的公司中。这就是我们开拓新市场的方式。这就是我们将 NVIDIA 连接到算法中的方式，使您能够以惊人的速度进行计算。

On top of that are what we call the CUDA X libraries, the algorithms of our company. What Nvidia truly is is an algorithm company that designs silicon to accelerate them. Of course, one of them is cuDNN, what many researchers recognize as the library that brought forth that started the Big Bang of modern AI, cuDNN. It's cuDNN is to AI what SQL is to the world's database industry. It's a domain-specific language, a domain-specific library. Just as OpenGL and DirectX enabled real-time computer graphics, cuDNN enabled the AI computing industry. 350 other libraries, those libraries are in fact the crown jewels of our company. Everything from computational lithography to signal processing, image processing, data processing, particles, fluids, sparse solvers, incredible breadth of libraries that many of you in the audience have connected into your company. This is how we open new markets. This is how we connect Nvidia into algorithms that enable you to do computation at incredible speeds.

最后，在右侧，那边，是模型。这是新软件。新应用的新引擎。它位于 CUDA X 库之上并与之协作。NVIDIA 一直在多个领域研究这些模型。有了这些模型，它开辟了新的机会，我稍后会有很多要说的。

And then finally, on the right-hand side, on that side, the models. This is the new software. The new engine of new applications. And it sits on top of and it collaborates with the CUDA X libraries. These models NVIDIA have been working on across multiple domains. With these models, it opens up new opportunities and I have lots to say about that later on.

嗯，NVIDIA 的每一代都从 Pascal 开始。AI 时代始于 Pascal 新的所谓 DGX-1 系统。DGX-1 为世界带来了世界上第一台 AI 超级计算机。我 10 年前介绍了它。将第一台交付给 Open AI。开始了他们的计算之旅。从 Pascal 到 Volta 到 Ampere，然后到你们中许多人第一次听说我们的 Hopper，当前一代 Blackwell，然后现在是全面生产的 Vera Rubin。注意六代架构。每一代我们都推进技术。

Well, every single generation NVIDIA started with Pascal. The AI era started with the Pascal new what is called DGX-1 system. DGX-1 brought to the world the world's first AI supercomputer. I introduced it 10 years ago. Delivered the first one to Open AI. Started their computation journey. From Pascal to Volta to Ampere then to many of you who heard about us for the very first time, Hopper the current generation Blackwell and then now Vera Rubin in full production. Notice six generations of architectures. With each generation we advance the technology.

在过去 10 年中，我们将 AI 性能提高了 100 万倍。如果你退一步想想摩尔定律在 10 年内会做什么，前 5 年将提高 10 倍，后 5 年再提高 10 倍，10 年内提高 100 倍。在同一时期，由于 NVIDIA 的极端协同设计方式，我们在每台计算机、每个软件栈上进行创新，优化新算法，创建新算法，同时创建架构来加速它，在整个领域工作，我们称之为极端协同设计，我们能够将 AI 加速 100 万倍。我们将继续这样做。这就是 NVIDIA 今天构建的。这就是我们能够使欧洲成为 AI 生态系统、AI 经济一部分的基础。

In the course of that last 10 years, we've improved AI performance by 1 million times. If you would take a step back and think about what would Moore's Law have done in 10 years time it would have the first 5 years would have improved by factor of 10, the second 5 years by another factor of 10, a 100 X improvement in 10 years time. In the same period of time because of the way NVIDIA extreme co-designs we're innovating across every single computer, across every single stack of the software, optimizing new algorithms, creating new algorithms, while creating architectures to accelerate it, Working across that entire domain, what we call extreme co-design, where we're able to accelerate AI by a million times. And we're going to continue to do so. This is what Nvidia builds today. And this is the foundation by which we can enable Europe to be part of the AI ecosystem, the AI economy.

现在，每一代在开始时，Pascal 是 GPU。Volta 是 GPU。Ampere 是 GPU。Hopper 是 GPU。我们发明了新的 DGX 形态。然后 Blackwell 向世界推出了第一个机架级计算。我稍后会展示。机架级计算将整个系统变成一台巨型计算机，一个 GPU。现在，通过 Vera Rubin，我们将其扩展到处理模型之外、大型语言模型之外，现在处理整个智能体。现在，我们稍后会回到这张幻灯片，但记住这一点。智能体必须思考。这就是模型的用武之地。感知、推理、规划和使用工具。所有这些都在大型语言模型上运行。模型就是为此设计的。但智能体也有所谓的 harness。基本上，这是数字智能体的外骨骼。就像我们一样，我们有大脑，身体其余部分代表我们的外骨骼。智能体是外骨骼，协调工作，允许智能体实际参与记忆、主动记忆、工作记忆以及长期记忆，并激活工具。然后第三部分是一台处理知识的计算机，专为数据处理设计。最后是一台专用于 CUDA-X 库的计算机。它的工作是运行工具。这些计算机现在代表了整个智能体系统。所以，Vera Rubin 已全面生产，很高兴看到它。这是我们创造的世界第一台计算机。

Now, each generation in the beginning, Pascal was GPUs. Volta was GPUs. Ampere was GPUs. And Hopper was GPUs. We invented the new DGX form factor. Then it was Blackwell that introduced to the world the first rack-scale computing. And I'll show you that in just a moment. Rack-scale computing turned one entire system into one giant computer, one GPU. And now, with Vera Rubin, we're extending it to processing beyond models, beyond large language models, to now processing the entire agent. Now, we'll refer back to this slide later, but remember this. The agent has to think. And that's where the model comes in. Perception, reasoning, planning, and using tools. All of that runs on the large language model. And the models are designed to do that. But the agent also has what is called a harness. Basically, this exoskeleton of the digital agent. Just like us, we have the brain, the rest of our body represents our exoskeleton. The agent is the exoskeleton that orchestrates the work, that allows the agent to actually engage memory, active memory, working memory, as well as long-term memory, and activating tools. Then the third part is a computer that processes knowledge, designed for data processing. And then lastly, a computer dedicated to CUDA-X libraries. And its job is to run tools. And these computers now represent the entire agentic system. So, Vera Rubin is in full production, and it's nice to kind of see it. This is the first computer in the world that we've created.

没有线缆，没有电线，100% 全液冷，而且真的非常令人愉悦。哎呀，等一下。能这样把它抽出来，真的很令人愉悦。这是一台 Vera Rubin 计算机。好，所以这台有两个 GPU。

No cables, no wires, completely 100% liquid cooled, and it's just really delightful. Whoops. Hang on. Really delightful to be able to pull it out. And this is one Vera Rubin computer. Okay, so this has two GPUs.

在 Grace Hopper 的时代，组装一个节点过去要花 2 个小时——你看到它就会明白我的意思，到处都是线缆，80% 液冷——现在 Vera Rubin 是 100% 液冷，组装时间不到 5 分钟。

What used to take, in the case of Grace Hopper, 2 hours to assemble one node — and when you see it, you'll know what I mean, cables and wires everywhere, 80% liquid cooled — now Vera Rubin is 100% liquid cooled, and it takes less than 5 minutes to assemble it.

这是了不起的成就。当然，这转化为制造中更高的周转效率、好得多的可靠性，当然还有好得多的性能。这是 NVLink 交换托盘，它的作用是把 72 块这样的 GPU 连接起来。现在这里面有四块。它把 72 块 GPU 连成一块巨大的 GPU。好，每块 GPU 是 20 petaflops，把 72 块连成一个巨大的系统。好。谢谢。谢谢。

And so, incredible achievement. Of course, this translates to higher cycle times in manufacturing, much better reliability, and of course, a lot better performance. This is the NVLink switch tray, and what this does is it connects 72 of these GPUs. There are four of them in here right now. It connects 72 GPUs into one gigantic GPU. Okay, so each GPU is 20 petaflops, connecting 72 of them into one gigantic system. Okay. Thank you. Thank you.

这是我们的新 Vera 计算机。没关系。轻一点。给她一个机会。每个人都有机会说点什么。但今天，让我——

And this is our new Vera computer. It's okay. Be go easy. Let her give her a chance. Everybody has a chance to say something. But today, let me —

不不。就——我稍后跟你聊。我稍后跟你们聊。谢谢。都是重要议题。都是重要议题。但让我先把主题演讲讲完。

No, no. Take just — I'll talk to you later. I'll talk to you guys later. Thank you. All important issues. All important issues. But let me get through my keynote.

我们——这个礼堂坐满了。我们在全球还有 100 个观看派对。100 个观看派对。现在有 15,000 人在看我们。所以我会把主题演讲讲完。最后我想给你们看一些非常非常好玩的东西。好吗？所以你们只能——你们得——我得先用一些知识教育折磨你们一下。然后娱乐部分就来了。

We have — so this auditorium is packed. We have 100 other watch parties around the world. 100 watch parties. 15,000 people are watching us right now. And so I'll get through the keynote. There's some really really fun things I want to show you in the end. Okay? So you just have to — you have to — I have to torture you through some education first. And then the entertainment is coming.

这是我们的全新 Vera 计算机。Vera CPU 是一头非凡的 CPU 猛兽。每时钟周期八条指令。它拥有全球首个 LPDDR 内存子系统，让我们既能获得高性能、极高带宽，又能拥有极其出色的能效。片上互连结构是全球所有芯片中最快的。因此，数据处理能力爆表。智能体式会话的启动和处理能力爆表。而这正是为做这件事而设计的。这是我们的 Vera 计算机。

This is our brand new Vera computer. Vera CPU is an extraordinary CPU beast. Eight instructions per clock. It has the world's first LPDDR memory subsystem so that we can have high performance, very high bandwidth, as well as extremely excellent energy efficiency. The on-chip fabric is the fastest of any chip in the world. As a result, data processing is off the charts. Agentic sessions starting and processing is off the charts. And this is designed for doing exactly that. This is our Vera computer.

而这里这台计算机是 Spectrum X。这是全球首个共封装光学。你们可以在这里看到。我给你们看。这片区域有些地方会塌下去，有些地方可以走上去。你们——我就是不确定哪块是哪块。好吧。所以如果我不小心消失了，你们知道——你们了解我们。这不是噱头，是意外。

And this computer here is the Spectrum X. This is the world's first co-packaged optics. And you could see it here. I'll show you. Some of this area falls into the ground. And some of this area you can walk on. You — I'm just not sure which. All right. So if I accidentally disappear, you guys know — you guys know us. It's not a stunt. It's an accident.

所以，你们可以在这里看到，这些是激光器。这些激光器通过光学器件向上，直接连接到我们的芯片。它使用的是一项我们与台积电共同发明的工艺技术，叫 Co-Op。这样，硅光子就直接贴装在我们的芯片上。否则，你看到的这一切都会是 PCB 板，而 PCB 板的传输能力比光缆差得多。所以，我们现在有了极高的数据速率、极高质量的数据，直接进入芯片。它直接进入——否则要经过收发器——不经过 PCB 和收发器，直接进入光学器件。好。所以，这是全球首个 CPO。我们已全面量产，512 端口、每端口 200 Gbps，全球最先进的以太网交换机。我们为此非常自豪。谢谢。

And so, you could see here, these are the lasers. And these lasers come up through optics and it connects directly to our chip. And it connects using a process technology we invented with TSMC called Co-Op. And so, silicon photonics are directly attached right onto our chip. Otherwise, what you would see is all of this would be PC board, and PC board transmission capability is much worse than optical cables. And so, we now have extremely high data rate, extremely high quality data that comes directly onto the chip. And this goes directly into — otherwise transceivers — without going through a PCB and a transceiver, it goes right into the optics. Okay. So, this is the world's first CPO. We're in complete volume production and 512 port at 200 gigabits per second, the world's most advanced Ethernet switch. And so, we're really proud of it. Thank you.

所以，这带给我们的是处理思维、处理大语言模型、处理智能体框架、为其知识运行数据处理，然后为所有工具运行加速计算的能力。这基本上就代表了智能体工作负载，我稍后会给你们看更多。

And so, what that gives us is the ability to process the thoughts, the large language model, process the agent harness, run data processing for its knowledge, and then run accelerated computing for all of the tools. That represents essentially the agent workload, and I'll show you more in a second.

Vera Rubin 已全面量产，性能爆表。每一代，正如我提到的，通过极致的协同设计，我们都能实现远超常理、远超摩尔定律所预示的性能。而如你们所知，摩尔定律基本上已经终结。所以，我们需要这些极致的协同设计思路，从芯片到系统，从系统到系统软件，到算法，再到整个 AI 工厂。

Well, Vera Rubin is in full production and the performance is off the charts. Every single generation, as I mentioned, through extreme co-design, we're able to achieve performances that are well beyond reason, well beyond what Moore's Law would have dictated. And as you know, Moore's Law has really largely ended. And so, we need these extreme co-design ideas working from chips to systems, systems to system software, to algorithm, to the entire AI factory.

Vera Rubin 是在 Blackwell 之上的又一个巨大跨越。Blackwell 在做智能体式——在做大语言模型处理上比 Hopper 快约 30 倍。而现在，Vera Rubin 在仅仅 2 年时间里又比 Blackwell 强 30 倍。我们来看看 Vera Rubin 如何变成一个 DSX 全栈 AI 工厂。

Well, Vera Rubin is another giant step on top of Blackwell. Blackwell was about 30 times faster than Hopper in doing agentic — in doing large language model processing. And now, Vera Rubin is another 30 times greater than Blackwell in just 2 years' time. Let's take a look at how Vera Rubin turns into what is a DSX full stack AI factory.

AI 正在创造史上最大规模的基础设施建设。这些工厂规模巨大，投资可观。我们必须把它建对。但仅有好芯片并不能造就一座伟大的 AI 工厂。NVIDIA DSX 是一套全栈 AI 工厂参考架构，与全球领先的基础设施公司共同创新。

AI is creating the largest infrastructure buildout in history. The factories are massive. The investments are significant. We have to build it right. But great chips alone don't make a great AI factory. NVIDIA DSX is a full-stack AI factory reference architecture co-innovated with the world's leading infrastructure companies.

计算、网络、存储、软件、供电和冷却作为一个系统协同设计。从设计到运营全程优化。DSX 始于数字孪生。性能、电力、水资源和规模在动工之前就经过虚拟验证。PTC 将设备设计转化为可直接仿真的资产。

Compute, networking, storage, software, power, and cooling co-designed as one system. Optimized from design to operation. DSX starts in a digital twin. Performance, power, water, and scale validated virtually before construction begins. PTC turns equipment designs into simulation-ready assets.

达索系统通过基于模型的系统工程连接机架与设施设计。施耐德电气模拟配电和液冷。Jacobs 在 Omniverse 数字孪生中把这一切整合起来。西门子评估热、声学和电网影响。Procore 支持施工阶段规划。

Dassault Systèmes connects rack and facility design through model-based systems engineering. Schneider Electric simulates power distribution and liquid cooling. Jacobs brings it all together in the Omniverse digital twin. Siemens evaluates thermal, acoustic, and grid impacts. And Procore supports construction phase planning.

电力限制了一座 AI 工厂能产出多少。DSX Max LPS 是一个端到端控制回路，动态调配电力以匹配工作负载需求。相同产出下，它少用 30% 的电。

Power limits how much an AI factory can produce. DSX Max LPS is an end-to-end control loop that dynamically steers power to match workload demand. It uses 30% less power for the same output.

DSX 重新思考冷却。NVIDIA Vera Rubin 系统为闭环液冷设计，冷却液温度最高可达 45°C。在气候适宜的地方，室外干冷器把热量释放到空气中，不蒸发水。在 2 GW 规模下，DSX 每天可节省 54 升水。这相当于 18 万户家庭的日用水量。

DSX rethinks cooling. NVIDIA Vera Rubin systems are designed for closed-loop liquid cooling with coolant temperatures up to 45° C. In suitable climates, outdoor dry coolers release heat into the air without evaporating water. At 2 GW, DSX can save 54 L of water a day. That's the daily water use of 180,000 households.

但一座 AI 工厂可能需要几个月才能建成。在投入运营之前，它不产出任何 token。今天，我们发布 NVIDIA DSX Pod，把这一全栈平台变成工厂预制的集成系统。它从 Vera Rubin NVL72 开始。七个液冷计算机架，用 17 公里的 Spectrum X 线缆连接，构成一个 Vera Rubin DSX Pod。

But an AI factory can take several months to build. Until it is operational, it produces no tokens. Today, we are announcing NVIDIA DSX Pod, bringing this full-stack platform into a factory-built integrated system. It starts with Vera Rubin NVL72. Seven liquid-cooled compute racks connected with 17 km of Spectrum X cables form a Vera Rubin DSX Pod.

Vera Rubin DSX Pod 经过集成、完整布线和工厂测试，然后以可运输的集装箱交付。干冷器取代传统的蒸发冷却。800 V 直流电接入机架，实现高效供电。一旦现场公用设施就绪，预计现场部署时间从几个月缩短到几天。NVIDIA DSX AI 工厂从设计上就是负责任的。

Vera Rubin DSX Pods are integrated, fully cabled, and factory tested, then delivered in transportable containers. Dry coolers replace traditional evaporative cooling. 800 V DC connects to the racks for efficient power delivery. Once site utilities are ready, estimated on-site deployment goes from several months to days. NVIDIA DSX AI factory is responsible by design.

现在，借助 DSX Pod，AI 工厂可以以光速建造。每瓦智能更高，首个 token 的生成时间更短。你怎么看？

And now, with DSX Pod, AI factories can be built at the speed of light. More intelligence per watt, less time to first token. What do you think?

2016 年，我们推出了全球首台 AI 超级计算机。它只有一个节点，一个 GPU 节点。三年前，我们推出了 Grace Blackwell NVLink 72 机架级计算机。两次我们都必须重新发明，不仅是计算机、软件及其工作方式，还有供应链。在 NVLink 72 的案例中，我们拿下了整个机架，否则它会在现场组装。我们在供应链内部完成了组装。我们实质上实现了超级计算机的规模化制造。这就是为什么我们能够如此快速地行动，并同时为全球数百个数据中心建设项目提供支持。现在全球每一家计算机制造商都在生产 NVLink 72。我们目前生产计算设备的规模是前所未有的，难以想象的。

So, in 2016, we introduced the world's first AI supercomputer. It was in one node, one GPU node. Then 3 years ago, we introduced the Grace Blackwell NVLink 72 rack-scale computer. Both times, we had to reinvent not just the computer, the software, and how it works, but also the supply chain. In the case of NVLink 72, we took the entire rack, which would have otherwise been assembled on site. And we assembled it inside the supply chain. We essentially manufactured supercomputers at scale. It is the reason why we're able to move so fast and be able to provide for literally several hundred data center build-outs around the world at the same time. Every single computer manufacturer in the world now manufactures NVLink 72. The scale at which we are now producing computing is unheard of ever, unimaginable.

嗯，即便如此，当你们把这些系统运到现场时，仍然需要数月又数月的组装。你刚刚听到，仅连接 Spectrum-X 用于横向扩展计算就需要 17000 公里的布线。17 公里，17000 公里。这数字太疯狂了。所以，17 公里，不可思议的数字。

Well, even then, when you bring these systems onto your sites, there are still months and months of assembly. And you just heard 17,000 km of wiring to connect just Spectrum-X for the scale-out computing. 17 km, 17,000 km. It's crazy number. And so, 17 km, incredible numbers.

现在，假设我们在供应链内部完成所有这些工作。启动一切，构建一切，确保它完全运行，通过认证并完成烤机，到发货时，它已经完全功能正常。布线和现场组装的量将大大减少。但是，还有更重要的事情。事实上，这里将发生的是，通过将其移入供应链，数据中心内闲置的计算量将大幅减少。记住这一点。每吉瓦每个吉瓦的数据中心是 500 亿美元。你预期的收入是每年数百亿美元。所以，如果我们说该基础设施每年产生 400 亿美元的收入。400 亿，如果延迟仅 3 个月，那就是 50 亿美元。抱歉，那是什么？100 亿美元的收入。100 亿美元的收入会让任何人焦虑。因此，通过将其移入供应链，我们从安装、计算设备到货到生产 token 的速度将从许多个月——我们认为大约 4 到 5 个月——降至约一个月。

Now, suppose we were to do that all inside the supply chain. Run everything up, build everything up, make sure that it runs completely, get it qualified burnt in, and by the time we ship it, it is fully functional. The amount of cabling and on-site assembly is going to be greatly reduced. But, there's something much, much more. In fact, what's going to happen here is that by moving this into the supply chain, the amount of idle computing, the amount of idle computing that's going to be sitting inside at your data centers will reduce dramatically. Remember this. Each gigawatt each gigawatt data center is $50 billion. The revenue generation you expect is tens of billion dollars per year. And so, if we just said it's 40 billion dollars per year of revenue generation that comes out of that infrastructure. 40 billion if delayed by just 3 months that's 5 billion dollars Excuse me, what is that? 10 billion dollars worth of revenues. 10 billion dollars worth of revenues will make anybody anxious. And so, by moving this into the supply chain the speed by which we can turn on, go from installation, arrival of computing to production of tokens will go from many months we think something along the lines of 4 to 5 months down to about a month.

好了，这是全球供应链的彻底变革，这将改变人们安装和建设数据中心的方式，希望结果是速度提升，浪费的闲置计算、浪费的闲置资本支出将减少，每个人都能更盈利。所以，这就是 NVLink 72 现在进入大型机柜系统。我们称之为 DSX Pod。好了，这是 NVIDIA 计算的下一个台阶。

Okay, so this is a complete transformation of the world supply chain and this will change the way that people install and build data centers and hopefully, as a result, velocity goes up the wasted idle compute, the wasted idle CapEx will reduce and everybody becomes more profitable. And so, this is NVLink 72 now into a large cabinet system. We call it DSX Pod. Okay, so this is the next click of NVIDIA computing.

现在，你们制造什么以及如何制造使你们与众不同。就像在欧洲，你们的工作使你们与众不同。你们设计机器，运营工厂，发现药物。你们拥有非凡的工艺。那种工艺、那种专长使你们与众不同。那么，是什么让 NVIDIA 与众不同？

Now, what you make and how you make it makes you special. Just as here in Europe you, the work you know, makes you special. You design machines. You operate factories. You discover medicine. You have extraordinary craft. That craft, that specialty makes you special. Well, what makes NVIDIA special?

真正有两个原因。第一，我们的架构是唯一通用的加速计算架构。我们不是只专注于某一种算法，例如 Transformer。我们专注于任何可以并行处理的东西。可以是流体和粒子，可以是量子，可以是量子化学，可以是图像处理，可以是数据处理，结构化向量数据处理。各种处理。因为我们在这个领域工作了很长时间，因为 NVIDIA 的架构在长达二十五年的时间里保持一致，我们所服务、所加速的算法数量、领域数量、行业数量已经大幅增长。我们的安装基础、我们的通用架构、我们对这一架构的专注，以及我们与全球所有行业的合作，使 NVIDIA 的架构无处不在。

Are really two things. The first thing is that our architecture is the only accelerated computing architecture that is general purpose. We don't specialize on just one one algorithm, for example, transformers. We specialize across anything that could be paralyzed. Could be fluids and particles. It could be quantum. It could be quantum chemistry. Could be image processing. Could be data processing, structured vector data processing. Processing of all kinds. And because we've been working at it for so long, and because NVIDIA's architecture has remained consistent for literally two and a half decades, the number of algorithms, the number of domains, the number of industries we serve, we accelerate, has grown tremendously. Our installed base, our general purpose architecture, our dedication to this architecture, and our collaboration with all of the world's industries has made NVIDIA's architecture literally everywhere.

第二是我们如何做到。当然，我们有我们开发的架构，但我们现在还以全栈完整的 AI 工厂来构建它。世界上有许多公司想设计自己的芯片，但世界上大多数公司只想部署 AI 基础设施，以便在其上运行自己的业务。设计芯片、构建超级计算机、编写所有软件的能力，对世界上大多数公司来说真的是完全不可想象的。因此，我们现在将这两个理念结合起来：CUDA 和全栈 AI 工厂，这使我们能够进入原本不可能的市场。正是因为这个原因，我们能够与这里的众多公司合作。

The second thing is how we do it. Of course, we have the architecture that we developed, but we also build it now in a full stack complete AI factory. There are many companies in the world who want to design their own chips, but most of the companies in the world just wants to deploy the AI infrastructure so that they could run their business on top of it. The ability to design a chip and build a supercomputer, write all the software, is really completely out of mind for most of the companies around the world. And so we now have the combination of these two ideas, CUDA, full stack AI factories, and it exposes us to markets that otherwise is impossible. We're able to partner with so many companies here for this very reason.

如果我环顾一下，从 CSP 开始，这是大多数人认为当今绝大多数 AI 所在的地方。但正如你们在我们主题演讲开头看到的，我提到了一大堆不是这些的公司。所以 CSP 仍然是全球计算的大业务。但有 AI 原生公司，世界各地都有初创公司。这里有很多。新云，因为每个地区都想参与 AI 经济。每个地区都想确保他们拥有的宝贵资源、他们拥有的能源，专门用于本地区的公司。例如，欧洲的 Mistral，法国的 Mistral，欧洲的 Nebia's。欧洲有许多公司正在建立新云。为全球企业构建计算机的 OEM 和 ODM。AI 实验室。当然，我们知道去年我们谈论的都是 OpenAI。今天我们谈论 OpenAI 和 Tropic，但我们也谈论不可思议的 Meta's Muse、Grok bot，当然还有 Gemini。现在有这么多不同的 AI 实验室在前沿参与。然后非常重要，这是最终最重要的。为了让 AI 成功，为了让欧洲从 AI 中受益并真正创建 AI 生态系统、AI 经济，你需要所有终端市场、终端行业采用 AI 并取得成功。无论是汽车行业，因为我们正在构建自动驾驶汽车，还是你们用机器人制造的方式，在医疗保健中发现新药的方式，工业中的金融服务，所有这些不同的行业都必须成为 AI 公司，每一个都是。通过我刚才提到的 CUDA 和我们构建计算机的方式，我们拥有独特的能力，能够服务于 AI 的每一个细分领域。所以，一个架构，许多市场。

If I were to go around this, starting with CSPs, this is where most people think the vast majority of AI is today. But as you can see in the beginning of our keynote, I mentioned a whole bunch of companies who are not those. So CSPs remain very large businesses for the world's computing. But there are AI natives, companies that are starting all over the world. There are so many that are here. Neo clouds, because every region wants to participate in the AI economy. Every region wants to ensure that the valuable resources that they have, the energy that they have, is dedicated to companies in their region. For example, Mistral here in in Europe, Mistral in France, Nebia's here in Europe. So many companies around Europe that are building up neo clouds. OEMs and ODMs who are building computers for the world's enterprise. AI labs. Of course, we know about last last year all we spoke about was OpenAI. To today we spoke about we speak about OpenAI and Tropic, but we also talk about the incredible Meta's Muse, Grok bot, and of course Gemini. So many different AI labs are now participating at the frontier. And then very importantly, and this is ultimately the most important. For AI to succeed, for Europe to benefit from AI and to have really created an AI ecosystem, an AI economy, you need all of the end markets, the end industries to adopt AI and to succeed with AI. Whether it's in the automotive industry, because we're building self-driving cars, or the way you manufacture with robotics, the way you discover new drugs in healthcare, your financial services in in industry, all of these different industries have to become AI companies, every single one of them. What we have a unique ability through what I've just mentioned about CUDA and the way that we build computers, we have a unique ability to serve literally every single segment of AI. And so, one architecture, many markets.

因为这些基础设施如此重要、如此昂贵，我们必须确保它们尽可能快地发挥最大生产力，确保它们是通用的——从 AI 旅程的起点开始，数据处理、训练、后训练、推理和智能体式处理，AI 旅程的每一个环节，到每一种类型的 AI，从化学和物理到语言，到每一个行业及其使用的算法，你都能用上它。所以，NVIDIA 的架构是通用的，因此才有持续的创新、持续的软件更新。因为装机量如此之大，我们拥有如此丰富得惊人的软件生态，所以这些架构也是耐用的——通用、耐用、高生产力。这就打下了基础。这就是基础设施。

And because these infrastructure are so significant and so expensive, we need to make sure that they're as productive as possible as soon as possible, that they're fungible, that you can use it for every workload from the beginning of your AI journey — data processing to training to post-training, inference and agentic processing, every single step of the AI journey, to every type of AI, chemicals and physics to languages, to every single industry and the algorithms that they use. And so, NVIDIA's architecture is fungible, and as a result of that, continuous innovation, continuous updating of software. Because the installed base is so great, we have such an incredibly rich ecosystem of software, then these architectures also durable — fungible, durable, productive. Well, that lays the foundation. That is the infrastructure.

那么，我们拿它来做什么？过去这一年，我们看到了 AI 的一次转变。AI 现在是有生产力的。最重要的时刻之一是在今年年初，我们谈到了 Open Claw，它引入了开源智能体的概念，真正开启了我们今天在全世界看到的一切。智能体会思考、推理、迭代、使用工具，因此消耗海量的 token。但也正因如此，它们变得有用了。所以，越来越多的智能体被开发出来，它们越来越有用，因此被越来越多的人使用；而因为它们必须用大量 token 来思考，token 的消耗量已经一飞冲天。GPU 的需求真的是一飞冲天。

Now, what do we do with it? This last year, we saw a transition in AI. AI is now productive. And one of the most important times was in the beginning of the year when we spoke about Open Claw, which introduced the concept of an open-source agent and really started what we all see across the world today. Agents think, reason, iterate, use tools, and as a result, consume an enormous amount of tokens. But as a result, they become useful. So, more agents are being developed, they're more useful, and therefore they're used by more people, and because they have to use a lot of tokens to think, the amount of token consumption has gone through the roof. GPU demand has really gone through the roof.

那么，处理这一切的就是这个。这是一个智能体。这些是智能体的五个要素。第一部分是语言模型。这些是在 GPU 之上运行的 token，它们生成 token，被智能体——我称之为外骨骼——所消费。那个 Open Claw 的 harness 位于一个沙箱之内，而沙箱把它与系统的其余部分隔离开来。这是一个非常重要的概念，我们后面还会回到它。一共有五样东西。有模型，有 harness，有 harness 所在的沙箱，还有另外两样：知识和工具。模型、沙箱、harness、知识、工具。这些就是智能体的五个要素。

Now, what's processing it is this. This is an agent. These are the five elements of an agent. The first part are the language models. These are the tokens that are running on top of the GPUs, and they're generating tokens that are consumed by the agent — what I call the exoskeleton. That Open Claw harness sits inside a sandbox, and that sandbox isolates it from the rest of the system. This is a very important concept that we're going to come back to. There are five things. There's the model, there's the harness, the sandbox that the harness sits in, and two other things: knowledge and tools. Models, sandbox, harness, knowledge, and tools. These are the five elements of an agent.

那么，在讲如何让智能体变得有用之前，最重要的是我们必须确保智能体是安全的。智能体是自主的。它是一个自主系统，能访问文件、访问资源、网络、访问工具，我们必须确保提供给这个自主系统的权限是最小的，并且我们要持续监控它。我们推出了一款出色的新产品、一个新平台，叫 Open Shell。Open Shell 来得正是时候。这是一个安全的运行时，提供了我刚才提到的那些能力。而且它还有一个额外的基于硬件的监控器。在我向你们解释之前，我们先看一段视频。

Now, before I talk about how to make the agents useful, the most important thing is we have to make sure that the agent is secure. The agent is autonomous. It is an autonomous system that has access to files, has access to resources, networks, access to tools, and we need to make sure that the privileges that we provide this autonomous system is minimal, and that we constantly monitor it. We introduced a fantastic new product, a new platform, and it's called Open Shell. Open Shell came just in time. This is a secure runtime that provides for the things that I just mentioned. And has an additional hardware-based monitor. Before I explain it to you, let's watch a video.

企业正准备部署数十亿个 AI 智能体。为了有用，它们会推理、规划、使用工具、访问数据，并协同工作。今天，唯一阻止智能体滥用这一切的，就是好好跟它说。"请不要删除任何东西。请不要推送到主分支。请不要碰我的 SSH 密钥。"这就是"请不要"的做法，但希望不是安全。安全不是一种愿望，而是技术与工程。NVIDIA Open Shell 和 Sentry 把这一理念付诸实践。你为每个智能体能触碰什么写一份策略，Open Shell 会以系统调用、文件访问和网络连接的粒度来执行它。形式化验证会对照你的权限上限检查每一次策略变更。这样，智能体永远不会获得超出预期的访问权限。这就像早期的互联网。病毒无处不在，直到浏览器对网页能做什么施加了严格限制。Open Shell 对智能体做了同样的事。执行发生在智能体之外，而不仅仅是在 LLM 护栏里，把智能体原生安全带入你的基础设施。每个智能体都运行在自己的隔离沙箱中，没有直达网络的路径。每一个出站网络请求都经过沙箱之外的监督器。它对照你的策略检查该操作。如果不被允许，就会被拒绝。密钥留在外面，由监督器保管，只在通往已批准端点的途中才被加入。被攻陷的智能体无法泄露它从未拥有过的密钥。网关是控制平面。监督器做决策。沙箱容纳智能体，策略证明器对照你的权限上限检查拟议的策略。Open Shell 将 Linux 内核隔离与运行时策略执行结合在一起。NVIDIA Sentry 在主机本身之外、远离智能体的地方增加了一层可选保护。Sentry 运行在 NVIDIA BlueField-4 上，监控智能体活动，并在硬件隔离的信任域中执行安全策略。因为智能体的推理流量会经过 Sentry，如果智能体打算做不该做的事，它会立即更新策略并切断与模型的连接，在毫秒内将其隔离。它还会保留防篡改记录，运营人员可以验证这些记录，用于检测、调查和响应。Open Shell 以 Apache 2.0 开源，能在你的智能体所在的任何地方运行，从边缘到云端，采用只需两条命令。安装 CLI，创建一个沙箱，运行你最喜欢的智能体。智能体的用处取决于你给它的访问权限。Open Shell 和 Sentry 把这种访问权限置于你的掌控之下。这就是我们如何让 AI 能力与安全齐头并进。

Enterprises are preparing to deploy billions of AI agents. To be useful, they'll reason, plan, use tools, access data, and work together. Today, the only thing stopping an agent from misusing all that is asking it nicely. "Please don't delete anything. Please don't push to main. Please don't touch my SSH keys." That's the "please don't" approach, but hope isn't security. Safety isn't an aspiration. It's technology and engineering. NVIDIA Open Shell and Sentry put that philosophy into practice. You write a policy for what each agent can touch, and Open Shell enforces it at the granularity of system calls, file access, and network connections. Formal verification checks every policy change against your permission ceiling. So, agents never gain more access than intended. It's like the early internet. Viruses were everywhere until browsers enforced strict limits on what a webpage could do. Open Shell does the same for agents. Enforcement happens outside the agent, not just in LLM guardrails, bringing agent-native security into your infrastructure. Every agent runs inside its own isolated sandbox with no direct route to the network. Every outbound network request goes through the supervisor, outside the sandbox. It checks the action against your policy. If it's not permitted, it's denied. Keys stay outside with the supervisor and are added only on the way to an approved endpoint. A compromised agent can't leak a key it never had. The gateway is the control plane. The supervisor makes the decisions. The sandbox holds the agent, and the policy prover checks proposed policies against your permission ceilings. Open Shell combines Linux kernel isolation with runtime policy enforcement. NVIDIA Sentry adds an optional layer of protection outside the host itself, away from the agent. Running on NVIDIA BlueField-4, Sentry monitors agent activity and enforces security policies in a hardware-isolated trust domain. Because agent reasoning traffic passes through Sentry, if the agent is planning to do something unintended, it will update policies immediately and cut the connection to the model, quarantining it in milliseconds. And it keeps tamper-evident records that operators can verify for detection, investigation, and response. Open Shell is open source under Apache 2.0, runs everywhere your agents do, edge to cloud, and takes two commands to adopt. Install the CLI, create a sandbox, and run your favorite agent. Agents are only as useful as the access you give them. Open Shell and Sentry put that access under your control. That's how we advance AI capabilities and safety together.

智能体是自主系统。我们必须创造极其安全的技术，确保我们能够安全地开发这些智能体、部署这些智能体。现在，这太不可思议了。在发布 Open Shell 之后，世界上几乎所有大公司——从前沿实验室到操作系统公司、网络安全公司、平台公司——都站出来采用 Open Shell。看到这一幕真是太好了。原因在于，这项技术来得正是时候，正当我们想在全世界部署智能体的时候。

Agents are autonomous systems. We have to create extremely secure technology to make sure that we could develop these agents and deploy these agents safely. Now, it's incredible. After announcing Open Shell, just about every major company in the world — from the frontier labs to the operating system companies, cybersecurity companies, platform companies — have come out to adopt Open Shell. It's really fantastic to see. And the reason for that is because this technology has arrived at exactly the right time, at a time when we want to deploy agents all over the world.

现在，请记住，这不仅仅是服务商在部署智能体时使用 Open Shell。这真正关乎世界上所有公司也采用 Open Shell，这样你才能接收这些智能体，让它们在你的公司内部自主运行。每一个能力层级、你交给智能体的每一项任务、每一个使用智能体的人——每一种组合都需要你为智能体提供不同级别的权限。而且每一次启动智能体，权限都不同。所以，权限、策略执行、监控，所有这些都必须动态完成。因此，Open Shell 真的是革命性的。我很高兴地宣布，Anthropic 和 NVIDIA 正在合作打造 Claude Open Shell 智能体。你可以去网站上看看。上面有一篇非常棒的介绍文章。

Now, remember, this isn't just about the services using Open Shell as they deploy agents. This is really about all of the world's companies also adopting Open Shell so that you could receive these agents and have them run autonomously inside your company. Every single level of capability, every single task that you give the agent, for every person that uses the agent — every one of those combinations requires a different level of privilege that you would provide to the agent. And every single time an agent is launched, the privileges are different. And so, privileges, the policy enforcement, the monitoring, all has to be done dynamically. And so, Open Shell is really revolutionary. I'm delighted to say that Anthropic and NVIDIA are partnering to create Claude Open Shell agents. And so, you can go to the website. There's a really great write-up about that.

那么，第一部分是如何使用这些框架——Open Claw、Hermes、Open Hand——或者你可以用 LangChain、deep agents 创建自己的框架，或者直接用 Python 编写。有很多不同的方法可以创建智能体框架，现在它们都运行在 Open Shell 安全框架内。明白吗？这就是它所在的沙盒，安全沙盒。

So, the first part is how do you use these harnesses—Open Claw, Hermes, Open Hand—or you could create your own harnesses from LangChain, deep agents, or just write it from Python. There's a whole bunch of different ways you could create agent harnesses that sit inside now an Open Shell secure harness. Okay? And that's the sandbox that it sits in, the secure sandbox.

接下来，我们谈谈模型。在这里，你应该使用各种类型的模型。未来，我们不会只用一个模型来运行所有事情。我们将使用最好的模型，无论是闭源的、开源的还是定制的。你将使用所有模型来完成工作。有时，你做的工作相当通用；AGI（通用人工智能）会做得非常出色。然而，有时你想做的工作包含了太多你的专有知识、太多你的专业领域，你希望不断增强这种智能。未来每家公司都会既使用闭源模型，也使用你定制的开源模型。

And then next, let's talk about models. There you should use models of all kinds. In the future, it won't just be one model that we run everything on. We're going to use the best models, whether they're closed, open, or custom. You're going to use all the models to do your work. Sometimes, the work that you do is fairly generic; artificial general intelligence is going to do an incredibly good job. However, sometimes, the work that you want to do captures too much of your proprietary knowledge, too much of your specialty, and you want to continuously enhance that intelligence. Every company in the future will use both closed models as well as open models that you customize.

到本次演讲结束时，你会看到，事实上，构建你自己的 AI 并不那么难。当然，它始于模型。我们一直致力于推进开源模型，以开辟新机遇，让人们能够构建自己的定制 AI。NeMo Tron 用于语言。Cosmos 是我们的世界基础模型。我们是前沿。Groot 用于人类和机器人。我们是前沿。Alpaca Myo 用于自主导航。我们是前沿。Icing 用于量子经典计算。我们是前沿。当然，Earth 2 用于物理 AI。我们是前沿。所以，在所有这些不同领域，我们为你提供所有模型，我们正在持续努力。我们已经研究了十年，我们将永远继续下去。我们完全开放地提供模型，以及随之而来的数据，以便你知道我们创建的模型的来源，以及重现它所需的脚本。因此，你不仅拥有开放的权重，还从我们这里获得了重新创建开源模型的完整配方和数据，这样你就能知道它的来源并完全信任它。

By the end of the session, you will see that in fact, building your own AIs is not that hard. It starts with, of course, the model. We've been dedicated to advancing open models to open up new opportunities and enable people to build their own custom AIs. NeMo Tron is for languages. Cosmos is our world foundation model. We are the frontier. Groot for human and robotics. We are the frontier. Alpaca Myo for autonomous navigation. We are the frontier. Icing for quantum classical computing. We are the frontier. And of course, Earth 2 physics AI. We are the frontier. So, in all of these different areas, we offer you all of the models, and we're continuously working on it. We've been working on it for a decade, and we'll continue to work on this forever. We offer you the model completely open, the data that goes along with it so that you know the provenance of the model that we created, as well as the scripts necessary for you to reproduce it. So, not only do you have the open weights, you have from us the entire recipe and the data to recreate the open models so that you could know where it came from and have complete trust into it.

嗯，这些模型不一定是前沿中的前沿，尤其是语言模型。然而，通过定制以及与我们所合作的生态系统中的合作伙伴，你可以将这些模型转变为前沿，从而将你的专有数据转化为主权智能。这里有一个例子：A21 帮助 Alice 将 NeMo Tron 转变为超越前沿能力的模型。这里 Baseten 帮助 Code Rabbit 在根据你的数据微调后，将 NeMo Tron 模型转变为前沿能力。Baseten 和 Code Rabbit。这里是 Trajectory 和 Harvey。同样，使用 NeMo Tron 进行后训练，并创建了他们自己的前沿级能力。这里是 Scale 与 Skydance 合作，再次做同样的事情。这样的例子不胜枚举。关键是，你可以从一个开源模型开始，比如 NeMo Tron 这样非常出色的模型，用你自己的数据针对你的用例进行后训练，当然可以实现超级智能。这是一件非常明智的事情，而且所有技术今天都已存在。这里有一个 Palantir 与坦帕综合医院的例子。例子还有很多。我们拥有丰富的合作伙伴生态系统，可以为你提供帮助，他们是了不起的公司，可以在世界各地与你合作。

Well, these models are not necessarily the frontier of frontiers, especially for the language model. However, with customization and with partners that the ecosystem that we work with, you could turn these models into frontier and as a result turn your proprietary data into sovereign intelligence. Here's an example of A21 helping Alice turn NeMo Tron into a model that exceeds frontier capabilities. Here's Baseten helping Code Rabbit turning a NeMo Tron model after fine-tuning on your data turn it into frontier capability. Baseten and Code Rabbit. Here's Trajectory and Harvey. Again, using NeMo Tron post trained it and turned and created their own frontier level capability. Here's Scale working with Skydance again doing the same thing. It goes on and on and on. The point being that you could start with an open model, something that's incredibly good like NeMo Tron and with your own data post train it for your use case and achieve of course super intelligence. That is a very sensible thing to do and all the technology exists today. Here's an example of Palantir with Tampa General Hospital. The list goes on and on. We have a rich ecosystem of partners that can help you and they're incredible companies and they could work with you you know from around the world.

好了，我们继续，总之，我只提一个，就会得罪其他人。我得坐在这里把所有都提一遍，我爱他们所有人。所以，首先是运行在安全沙盒 Open Shell 中的智能体框架。其次是 NeMo Tron 大语言模型，这是你所需的前两个组件。但最终，让这些超级智能体成为超级智能体的，是让它们访问你公司的知识。这真的很特别。这叫做我们构建的智能体知识基础。完全世界级的库和模型。它允许你将公司的数据——数据可能以非结构化格式存在于文档中，因此你需要 AI 来理解如何阅读这些 PDF 和所有非结构化文档，或者它可能是数据库中的结构化数据。我们拥有必要的库和工具，帮助你从公司数据中提取意义和理解。逐一来看。Nematron parse，完全世界级，排行榜第一。Nematron embed，将查询与向量数据库中的知识关联起来。排行榜第一。Nematron re-rank，它可能会找到许多不同的来源，查询与不同的数据片段相关联。它将它们整合在一起，根据哪个与你最相关进行排名。Kumo，能够从你的结构化数据库中预测可能发生的情况。例如，如果你收到通知，你的一个供应商将供应短缺或延迟供应。那条信息很可能是非结构化数据。它是一份文档。你公司订单的信息很可能在结构化数据库中，比如你的 SQL 数据库。为了预测所有这些订单会发生什么，哪些会短缺，延迟多少，你使用 Kumo 进行预测。然后为了找出用什么替代，你可能会使用向量数据库搜索，找到替代组件的工程文档。好了，所有这些不同的库和数据处理能力现在为你构建了知识基础。你现在已经用专有数据、专有知识武装了你的 AI。

Okay, so I we work it's just anyways, I'll I'll mention one and I'll offend everybody else. I'll have to sit here and mention all of them and I love all I love them all. And so first agent harness running in a secure sandbox open shell. Second, NeMo Tron large language models, the first two components of what you need. But ultimately, what turns these super agents these agents into super agents is that you give them access to your company's knowledge. And this is really special. This is called the agent knowledge foundation that we put together. Libraries and models that are completely world-class. It allows you to turn your company's data The data could be in the assets could be in uh uh unstructured format in documents so that you need an AI to understand how to read these PDFs and read all these unstructured documents or it could be in structured data inside databases. We have the libraries and the tools necessary to help you extract meaning and understanding from your company's data. One at a time. Nematron parse, completely world-class, top of leaderboard. Nematron embed, taking a query and associating with the knowledge that's into inside the vector database. Top of leaderboard. Nematron re-rank, it might find many different sources where the query is associated with different pieces of data. It brings it together, ranks ranks them based on which one's the most relevant uh to you. Uh Kumo, to be able to predict from your structured database what is likely to happen. And so this is for example, if you were to receive um a notice that one of your suppliers will be uh short on supply or delay on supply. That that piece of information is likely uh unstructured data. It's a document. The information about the orders in your company are likely inside a structured database, uh your SQL database for example. And in order to predict what to what's going to happen to all of these orders, who are going to be short, the the amount of delay, you use Kumo to make that prediction. And then in order to uh figure out what to replace it with, you might use vector database search and go find a engineering document of a replacement component. Okay, so all of those different libraries and data processing capabilities now makes for you your knowledge foundation. You've now armed your AI with proprietary data, proprietary knowledge.

现在，他们要谈的最后一部分是工具。行业工具。英伟达一直在研究 CUDA-X，在 AI 之前，对你们大多数人来说，我们所有的对话都是关于如何加速模拟器和求解器、数据处理引擎、各种领域特定库。它们用于科学发现，在所有不同的科学领域。它们用于工程。它们用于研究。它们用于设计、建造、创意艺术。其核心是 CUDA-X 库，这些 CUDA-X 库就是算法。而算法，如你所知，是美丽的。让我们来看看它们。

Now, the last part they'll talk about is ultimately tools. Tools of the trade. Nvidia has been working on CUDA-X and for most of you in the last before AI, all of our conversations have been about around how do we accelerate simulators and solvers, data processing engines, domain-specific libraries of all kinds. They're used in scientific discovery in all the different fields of science. They're used in engineering. They're used in research. They're used in designing, building, creative arts. At this core, are CUDA-X libraries and these CUDA-X libraries are algorithms. And algorithms, as you know, are beautiful. Let's take a look at them.

>> >> 数学是美丽的。数学是美丽的。

>> >> Math is beautiful. Math is beautiful.

CUDA X 库，这些是我们公司的皇冠上的明珠。原因如下。当你构建一个 AI 时，它可以是实验室助手。可以是研究助手。可以是共同设计者，与你共同创造、一起构建某物的人。最终，是什么让这些智能体有用？是什么让它们能够做真正的工作？是工具。

CUDA X libraries, these are the crown jewels of our company. And the reason for that is this. When you're building an AI it could be a a lab assistant. It could be a research assistant. It could be a co-designer, uh somebody who's co-creator with you, building something with you. Ultimately, what makes those agents useful? What makes them able to do real work? Are the tools.

现在我们有了这些模型，让智能体能够推理、理解信息、进行推理、制定计划并迭代行动，使用工具直到完成任务。但归根结底还是工具。这些工具我们已经研发了很多年。我右边展示的那台特定计算机能运行所有这些工具。它可以是流体动力学和粒子物理，可以是量子化学，可以是数据处理，可以是光线追踪。所有这些工具都运行在那台计算机上。与直接在 CPU 上原生运行相比，那台计算机将处理速度提高了 10、50、100 倍。而 CUDA、CUDA-X 工具正是赋予智能体行业工具的东西。看到其他人如何使用它真是太酷了。让我们来看一看。

And so we now have models that allow these agents to reason, understand information, reason, to be able to plan and iteratively act and use tools until it could get the job done. But it still comes down to the tools. And these tools we've been working on for so many years. And there was one particular computer I showed you on the right-hand side that runs every single one of these tools. And it could be fluid dynamics and particle physics. It could be quantum chemistry. It could be data processing. It could be ray tracing. All of these tools run on that one computer. And that one computer speeds up the processing of it compared to just running it natively on a CPU by 10, 50, 100 X. And CUDA, CUDA-X tools is what give the agents the tools of the trade. And it's so cool to see how other people are using it. Let's take a look.

那么

And so

这就是智能体背后的魔法。最终，智能体必须使用工具。当然，智能体应用的首批领域之一是软件编程，因此编译器或 Python 就是它真正需要的全部。但未来，随着这些工具完成越来越多有用的工作，我们将需要给它们更多工具。这里有一些例子。这是 Lyra Sciences，他们正在创建一个实验室助手，使用 Nvidia 的 BioNeMo 模型，这些模型旨在理解蛋白质和细胞，并在药物发现中生成蛋白质和化学物质。他们正在使用 BioNeMo 和 CUDA-X 工具。这是西门子工厂，正在构建一个工厂智能体，使用 Nvidia Cosmos——我们理解物理世界的世界基础模型——和 CUDA-X 工具。这是 Dassault 的例子，他们创建了一个虚拟伴侣，使用 Nvidia 的 NeMo tron——我们的语言模型——和 CUDA-X 工具。让我们看看它们的使用。

So this is the magic behind the agents. Ultimately, the agents have to use tools. Now of course, in one of the first fields of agentic use is software programming and therefore a compiler or a Python is all really it needs. But in the future, as we have these tools do more and more useful work, we're going to need to give them more tools. And here's some examples. So, here's Lyra Sciences, they're creating a lab assistant and they're using Nvidia's BioNeMo models that are designed for understanding proteins and understanding cells and generating proteins and chemicals in drug discovery. They're using BioNeMo and CUDA-X tools. This is Siemens factory, building a factory agent that uses Nvidia Cosmos, our world foundation model that understands the physical world, and CUDA-X tools. And here's example of Dassault creating a virtual companion that uses Nvidia's NeMo tron, our language model, and CUDA-X tools. Let's see them in use.

最聪明的科学家仍然需要实验室。最好的工程师仍然需要模拟器和求解器。AI 智能体也不例外。一个智能体汇集了五个构建模块。模型负责感知、推理和规划。执行框架协调工作。知识基础为智能体提供来自公司结构化和非结构化数据的上下文。工具让智能体能够模拟、实验和行动。Nvidia Open Shell 控制智能体可以访问和执行的内容。从前沿闭源模型开始，或定制开源模型。借助 Nvidia 的智能体工具包，任何公司都可以将其知识和专长转化为定制的超级智能体。让我们看三个例子。

The most intelligent scientist still needs a laboratory. The best engineer still needs simulators and solvers. An AI agent is no different. An agent brings together five building blocks. The model perceives, reasons, and plans. The harness coordinates the work. Knowledge foundation gives the agent context from your company's structured and unstructured data. Tools let the agent simulate, experiment, and act. Nvidia Open Shell controls what agents can access and execute. Start with a frontier closed model or customize an open model. With Nvidia's agent toolkit, any company can turn its knowledge and expertise into a customized super agent. Let's look at three examples.

对于 Lyra Sciences，目标是设计一种针对 B 细胞淋巴瘤的疗法。基于 Nvidia NeMo tron 构建的 Lyra Iris 智能体将该目标转化为实验计划。在计算干实验室中，LILA 智能体使用 NVIDIA BioNeMo 智能体工具包调用蛋白质设计和 RNA 优化模型，运行数百万次虚拟实验。最有希望的候选者进入 LILA 的 AI 科学工厂，在那里，由 NVIDIA Omniverse 和 Cosmos 开发的自主机器人和仪器进行制造和测试。结果返回给 Iris，塑造下一个设计和实验。循环继续。

For Lyra Sciences, the goal is to design a therapy for B-cell lymphoma. The Lyra Iris agent, built on Nvidia NeMo tron, turns that goal into an experimental plan. In the computational dry lab, LILA agents use NVIDIA BioNeMo agent toolkit to call models for protein design and RNA optimization, running millions of virtual experiments. The most promising candidates move into LILA's AI science factory, where autonomous robots and instruments, developed with NVIDIA Omniverse and Cosmos, make and test them. The results return to Iris, shaping the next designs and experiments. The loop continues.

在西门子埃尔兰根电子工厂，目标是将新产品投入生产。工艺准备 X 智能体使用 NVIDIA Cosmos 生成虚拟原型和合成数据，然后在生产开始前训练和验证检测模型。智能中心 X 智能体基于 NVIDIA 的工厂运营蓝图构建，利用订单、供应和产线设置来推荐生产计划。数字孪生编排器使用 NVIDIA Omniverse 库，通过物理模拟和实时工厂数据验证这些计划，然后再将变更落实到工厂车间。

At Siemens electronic factory Erlangen, the goal is to bring new products into production. Process preparation X agents use NVIDIA Cosmos to generate virtual prototypes and synthetic data, then train and validate inspection models before production starts. Intelligence center X agents, built with NVIDIA's factory operations blueprint, use orders, supplies, and line setups to recommend production plans. Digital twin composer uses NVIDIA Omniverse libraries to validate those plans with physics simulation and real-time factory data before changes reach the factory floor.

在 Dassault Systèmes 平台上，工程师需要将大众高尔夫的车门设计适配到途观上。Aura 和 Leo 使用 NVIDIA NeMoTron 对工程需求进行推理。执行框架协调专门的子智能体，并在规划、设计和模拟过程中保持上下文。Aura 使用 ENOVIA 围绕车辆的尺寸、重量限制和其他工程需求来规划工作。Leo 设计智能体选择一个兼容的高尔夫车门设计，并使用 CATIA 为途观重新塑形。Leo 模拟智能体使用由 NVIDIA CUDA-X 库加速的 Simulia 来测试变体，揭示了一个振动问题。结果返回给设计智能体进行改进。工程师审查改进后的设计并选择最终方案。不同的目标，不同的专业知识，相同的构建模块。开源模型、CUDA-X 加速工具，以及 Open Shell 安全运行时，通过 NVIDIA 智能体工具包结合在一起。

With the Dassault Systèmes platform, an engineer needs to adapt a Volkswagen Golf door design for the Tiguan. Aura and Leo use NVIDIA NeMoTron to reason over engineering requirements. The harness coordinates the specialized sub agents and preserves context across planning, design, and simulation. Aura uses ENOVIA to plan the work around the vehicle's dimensions, weight limits, and other engineering requirements. Leo design agent selects a compatible Golf door design and uses CATIA to reshape it for the Tiguan. Leo simulation agent uses Simulia, accelerated by NVIDIA CUDA-X libraries, to test the variants, revealing a vibration problem. The results return to the design agent for refinement. The engineer reviews the refined designs and selects the final one. Different goals, different expertise, the same building blocks. Open models, CUDA X accelerated tools, and the Open Shell Secure Runtime, brought together with the NVIDIA Agent Toolkit.

是不是很棒？所以，有人说 AI 的到来将终结软件，将终结工具公司。事实恰恰相反。AI 来了，我们多年来创造的工具将拥有比以往更多的用户。每一家工具公司现在都将成为 AI 公司，成为智能体公司。他们不仅会提供工具，还会提供专门优化的智能体，这些智能体非常擅长使用他们的工具。Cadence、CrowdStrike、Dassault、Icertis，几乎每一家企业公司都在构建智能体，我们很高兴与他们合作。构建智能体并不难。它有四个组成——五个组件。它有模型、执行框架，两者都有闭源和开源版本，还有 Open Shell 安全运行时，然后最重要的是知识和工具，而这些知识和工具属于你。那是你的专长。所以，所有这些现在都可以实现，我稍后会展示更多。但在那之前，让我们谈谈物理 AI。那确实是下一波 AI 浪潮。理解物理世界、能够对其进行推理的 AI，因为物理世界是多样的、复杂的，具有极长的尾部，要对其进行推理并使用适当的工具与物理世界互动。

Isn't it great? So, some people said that AI is here is going to be the end of software. It's going to be end of tools company. It's exactly the opposite. AI is here, there'll be more users of these tools that we created over time than ever before. Every single tools company will now become an AI company. They'll be an agents company. Not only will they offer tools, but they'll offer you specially optimized agents that are incredibly good at using their tools. Cadence and CrowdStrike and Dassault, Icertis, and just about every enterprise company out there are all building agents, and we're just delighted to be working with them. Building agents is not that hard. It's got four compo- five components. It's got the model, the harness, both of them have closed and open versions, but the Open Shell Secure Runtime, and then most importantly, the knowledge and tools, and those knowledge and tools belong to you. That's your expertise. And so, all of this is now possible to do, and I'll show you more in just a second. But before I do that, let's talk about physical AI. That's really the next wave of AI. AI that understands the physical world, can reason about it, because the physical world is diverse, it is complicated, it's got extremely long tails, and to reason about it and use appropriate tools to interact with the physical world.

每当人们想到物理世界，就会想到机器人。事实上，我最喜欢的物理世界应用之一是无线电手机基站。原因如下。它不使用摄像头，不使用扬声器和麦克风，而是使用无线电波来感知世界。然后对其进行推理，处理信息，进行推理，并使用波束成形与世界互动。所以，事实上，这是一个非常适合 AI 的应用和技术。AI 将彻底改变我们进行电信的方式。它将极大地提高我们的频谱效率，就像我们用光线追踪所做的那样。你们看到的所有图形都是 100% 光线追踪的。过去我们认为必要的计算量，现在应用 AI 后，有百万比一的差异。因此，我们将为电信网络带来频谱效率的巨大提升。

Whenever people think about physical worlds, they think robotics. In fact, one of my favorite physical world applications is the radio cell phone cell towers. And the reason for that is this. It uses, instead of cameras, instead of speakers and microphones, it uses radio waves to sense the world. And to it reasons about it, it processes information, reasons about it, and interacts with the world using beam forming. So, in fact, this is an application, a technology, that is just perfectly suited for AI. AI will revolutionize the way we do telecommunications. It will enhance our spectrum efficiency tremendously, just as we've done so with ray tracing. All of the graphics that you've seen completely 100% ray traced. The amount of computation we thought necessary in the past, and now that we apply AI to, is a million to one difference. And so, we're going to bring to the telecommunications network an enormous step up in spectrum efficiency.

但除此之外，我们还将彻底变革所有这些蜂窝基站。全球约有 10 万个站点，西方世界约有 100 吉瓦的分布式数据中心，未来将成为分布式 AI 基础设施。我们很高兴与欧洲的许多公司合作，就在上周，经过近 8 年的努力，Nvidia 与诺基亚在 T-Mobile 网络上拨打了第一个商用电话。所以，现在 CUDA 能以传统方式打电话了。一旦能做到这一点，我们就可以在上面叠加 AI。因此，这场革命即将开始。这对我们来说是一个非常令人兴奋的机会。

But beyond that, we're also going to revolutionize all of these cell towers. There's 100,000 sites around the world, around the western world, about 100 gigawatts of distributed data centers, that will in the future become a distributed AI infrastructure. We're delighted to partner with many companies here in Europe, and just this last week, after working on this for nearly 8 years, Nvidia made the first commercial phone call on the T-Mobile network with Nokia. And so, now CUDA makes phone calls the traditional way. Then once it can do that, then we can layer AI on top of it. And so, the revolution is going to begin. This is really exciting opportunity for us.

另一个让我非常兴奋的物理 AI 应用本质上是机器人放射治疗。你的肿瘤在改变形状。在接受治疗时，身体也在变化。机器人——这个放射自适应放疗系统——必须感知变化的环境，理解肿瘤的位置，完美地分割它，并相应地精确引导放射治疗。本质上，就是机器人，没错。

The other physical AI application that I'm super excited about is essentially robotic radiotherapy. Your tumor the tumor is changing in shape. The physical body is changing while you're in therapy. The robot this radio adaptive radiotherapy system has to perceive the changing environment understand where the tumor is, segment it just perfectly and direct precisely direct the radiotherapy accordingly. Essentially, robotic that's right.

请继续。

Go ahead.

这是一种机器人手术。当然，最高音量、最接近的机器人应用或物理 AI 应用是大家所认识的，我们已经研究了十多年。现在我们认识到，每一家汽车公司都会希望他们的汽车成为叫车车队的一部分。今天我们有了叫车服务，它是 Uber 的一部分，在欧洲我们有 Bolt、Grab、Lyft 等等，所有这些不同的叫车服务。未来将有机器人出租车连接到这些叫车服务。但不会只来自一个品牌。所以我们认识到，构建这些系统非常昂贵，尤其是上面的软件。我们决定创建一个标准架构。14 个不同的摄像头、9 个雷达系统、3 个激光雷达、无数超声波传感器，这个传感器外壳由双 Nvidia GPU 系统驱动，用于自动驾驶计算系统。我们将其转变为行业标准，现在它正被设计到一个个车队中。在此基础上，我们的自动驾驶软件公司——事实上我们也是其中之一——构建自动驾驶机器人出租车能力，然后连接到叫车服务。这个生态系统正在构建中。全球每年旅行约一万亿英里。未来其中许多英里，你知道，每英里一两美元，将用于机器人出租车。所以，这是一个巨大的机会，但非常分散。因此，创建行业标准 Hyperion 并与整个生态系统合作，我们可以本质上创建一个巨大的虚拟车队，每个人都可以连接进去。今天，我非常自豪地宣布，又有四个品牌、四款车即将加入。大众面包车，我无法想象还有什么比这更具标志性。JLR 的全新车、沃尔沃车队，以及梅赛德斯的下一代，也将兼容 Hyperion，并成为这个网络、这个叫车网络、机器人出租车车队的一部分。所以，欢迎来到 Hyperion。

This is a form of robotic surgery. Now of course the highest volume the most near-term robotics application or physical AI application that everybody recognize has been something we've been working on for over a decade. And now we recognize that every single car company will want to make their cars part of the ride hailing fleet. Today we have ride hailing and it's part of Uber and here in Europe we have Bolt and we have Grab and Lyft and so on and so forth and all these different ride hailing services. In the future there'll be robo taxis that will be connected to those ride hailing services as well. But it won't be from just one brand. And so we recognize that building these systems are incredibly expensive particularly the software that goes on top of it. We decided to create one standard architecture. 14 different cameras nine radar systems three lidars umpteen ultrasonics this cocoon of a shell of sensors powered by dual Nvidia GPU systems for the AV computing system. We turned this into an industry standard, and now it's being designed into one car fleet after another. On top of it, our AV software companies, and we are in fact one of them as well, to build an autonomous robo-taxi capability, which then is connected into the ride-hailing services. This ecosystem is being built out. The world travels about a trillion miles per year around the world. Many of those miles in the future, you know, a dollar or two per mile, are going to be on robo-taxis. So, this is one giant opportunity, but it's incredibly fragmented. And so, creating an industry standard, Hyperion, and working with an entire ecosystem, we could create essentially a giant virtual fleet that everybody can connect into. Today, I'm super proud to announce that four more brands, four more cars are going to lines are going to come in. The VW van, I just can't imagine anything more iconic. The JLR brand new car, the Volvo fleet, as well as Mercedes next generation, are going to be also Hyperion compatible and be part of this network, this ride-hailing network, robo-taxi fleet. So, welcome to Hyperion.

下一个主要行业，下一个主要行业，下一个伟大的行业，而且这个行业可能极其庞大，就是人形机器人。机器人技术之所以难做是有原因的。因为所有不同的工作都相当多样化。因此，为每个多样化任务构建特殊配置会将机器人行业分割成所有这些不同的配置，这阻碍了技术飞轮的发生。手机之所以发展如此之快，个人电脑之所以发展如此之快，NVIDIA 的 NVLink 72 之所以发展如此之快，是因为那些标准化配置允许许多客户使用它，从而产生规模。这种规模驱动技术飞轮，并且它变得更快、更好，速度惊人。因此，我们需要一个尽可能标准的机器人配置。现在，人形机器人显然有能力。它可能不是对所有事情都完美，但可能适用于许多事情。因此，这可能是最大规模的机器人配置。现在，如果我们能创造出让它快速学习的 AI，那么对于没有整个机器人团队来教导和编程机器人的中小型企业，现在可以向机器人展示需要做什么。机器人，因为它具有通用能力和通用智能，将能够快速学习和适应。因此，这可能是下一个主要平台技术平台。我们正在与许多不同的公司合作。你会认出欧洲的许多公司。这是你的巨大机会。没有任何国家、任何地区像欧洲地区那样致力于以如此高的精度和工艺水平制造机电一体化、电子、机械系统。这里许多公司非常擅长制造。这将是下一个高产量行业，你可以看到有许多公司现在参与其中，我们很高兴与你们合作。

The next major industry, the next major industry, the next great industry, and this one is likely to be extremely large, is humanoid robotics. There's a reason why robotics is hard to do. Because all the different work is rather diverse. And so, building a special configuration for each one of the diverse tasks segments the robotics industry into all of these different configurations, which prevents the technology flywheel to happen. The reason why the cell phone has advanced so quickly, the reason why personal computers have advanced so quickly, the reason why NVIDIA's NVLink 72 has advanced so quickly is because those standardized configurations allows many customers to use it, the volume to therefore be derived. That volume drives the technology flywheel, and it gets faster, better, incredibly fast. And so, we need a robotics configuration that is as standard as possible. Now, humanoid robotics obviously has ability. It probably isn't perfect for everything, but it's likely to work for many things. As a result, this is likely to going to be the largest volume robotics configuration. Now, if we could just create the AI that allows it to learn very quickly, then for small medium businesses who don't have an entire robotics team to teach and program the robot can now show the robot what needs to be done. The robot, because it has general capabilities and general intelligence, would be able to learn and adapt quickly. And so, this is likely to be the next major platform technology platform. We're working with so many different companies. You will recognize many of them here in Europe. This is your great opportunity. No country, no region has dedicated itself to making mechatronics, electronics, mechanical systems at the level of precision and craft as the European region. So many companies here are incredibly good at manufacturing. This is going to be the next high-volume industry, and you could see there's so many companies that are now part of it, and we're delighted to be partnered with you.

现在，为了创建物理 AI 系统，你需要三台计算机。第一台是我们一直在谈论的、大家都非常熟悉的，即训练 AI 模型。第二台是让 AI 学习如何执行技能的地方。你不能只是让 AI 编程 AI，然后让 AI 来到物理世界尝试做任何它想做的事情。你在数字孪生中做这件事。我们创建了一个名为 Omniverse 的数字孪生，你们已经看到我们反复谈论它，事实上刚才当你看到所有 CUDA-X 库时，它们都是在数字孪生世界中完成的。那个数字孪生世界叫做 Omniverse。Omniverse 是一个连接各种平台和工具的库，西门子、达索、Cadence 等等。所以你可以看到这些数字孪生系统正在被创建。第二台计算机。第三台是机器人计算机。在自动驾驶汽车、机器人出租车的情况下，我们称之为 Hyperion。Hyperion 包括计算机本身、功能安全系统、主动安全系统、专为物理 AI 安全设计的操作系统。我们需要对机器人系统做完全相同的事情。这样每次你开始开发研究并开始研究自己的机器人系统时，你不必从头开始拼凑所有软件、计算系统、传感器和所有机械系统。仅此一项就需要几年时间。因此，我们决定构建一个参考机器人。

Now, in order to create a physical AI system, you need three computers. The first computer is the one we've been talking about and everybody knows quite well, which is training the AI model. The second computer is where we have the AI learn how to do its skills. You can't just have the AI program the AI and have the AI come down into the physical world and try to do whatever it wants to do. You do it inside a digital twin. We create a digital twin called Omniverse and you've seen us talk about that repeatedly and in fact just now when you saw all the CUDA-X libraries, they're all being done inside a digital twin world. That digital twin world is called Omniverse. Omniverse is a library that's connecting into all kinds of platforms and tools, Siemens and Dassault and Cadence and so on and so forth. And so you could see these digital twin systems being created. The second computer. The third computer is the robotics computer. That computer integrates in the case of the self-driving car, the robo-taxi, we call it Hyperion. Hyperion consists of the computer itself, the functional safety system, the active safety system, an operating system designed for physical AI safety. What we need to do exactly the same for robotic systems. So that every single time you start to develop research and start to work on your own robotic systems, you don't have to start from scratch to cobble up all of the software and computing systems and the sensors and all the mechanical systems. That alone would take several years. And so we decided to build a reference robot.

这是 NVIDIA Agile One，与 Agile Robotics 合作打造。这款机器人将于明年第一季度上市。它将预装计算机、集成软件，开箱即用就能表现得相当不错。所以这是一个很好的起点，它将成为我们加速机器人技术发展的飞轮。

This is the NVIDIA Agile One in partnership with Agile Robotics. This robot will be available in the first quarter of next year. It will come pre-assembled with a computer inside, software integrated, and it should be able to perform quite well right out of the box. So this is a great starting point, and it will be the flywheel by which we can accelerate the development of robotics.

好吧，让我从我们开始的地方来结束今天的演讲。NVIDIA 起步于 PC 革命。那是 1993 年，PC 革命刚刚开始。还记得 Windows 95 将它推向了涡轮增压。自那以后，我们发明了 GPU、可编程着色、实时光线追踪。我们将 CUDA 加入计算模型，帮助点燃了现代 AI 革命。所以四年前我们开始思考，我们能做些什么来为这个新时代重新构想个人电脑？在这个时代，个人电脑不仅是你的工具，你的个人电脑工具，而是你的个人智能体或你的个人助理。你与这台电脑互动的方式本质上将是一个伴侣。一个帮助你工作的人。所以这台电脑需要具备我今天提到的五样东西。它需要有一个模型。它可以在本地运行，也可以在云端运行。它需要一个运行框架。它需要一个安全的运行时，OpenShell。它需要访问权限——因为它在你 PC 上可用，它需要访问知识和工具。好消息是，因为 NVIDIA 长期以来一直在加速工具，如果我们能创造一台能够加速 OpenGL、DirectX 和所有 CUDA 的电脑，NVIDIA 曾经做过的所有东西都能在这台电脑上运行。所以就是这样。经过与微软 4 年的合作，我们创造了 RTX Spark。这是一条全新的电脑产品线， literally 数千个工程年投入其中。我们秘密研发了 4 年，创造了一款名为 N1X 的芯片，然后我们最近刚刚宣布，整个 PC 行业都加入了进来。我们创造了台式机、笔记本电脑和工作站。这是一千万亿次浮点运算。一千万亿次。换个角度说，2016 年我交付了一千万亿次浮点运算和 128 GB 内存。一千万亿次和 128 GB 给了一家叫 OpenAI 的小公司。这是一千万亿次和 128 GB。当然，唯一的区别是我可以这样拿着它，而 DGX-1 需要两个人抬上楼。所以这就是全新的 RTX Spark PC。如果你想要一台运行 CATIA 以及 Autodesk、Adobe、Cadence 和 Synopsys 所有软件的伴侣工作站，你随时可以买一台运行 Windows 的 DGX Station。这是 20 千万亿次浮点运算和 754 GB 内存。太不可思议了。当然，还有桌面版本。所有这些现在都已上市，我认为搭载 RTX Spark 的 DGX Station——DGX Spark、DGX Station 搭载 Windows——将在今年年底上市。现在的问题是——到目前为止我只谈了智能体的五个组成部分。现在的问题是你如何构建它？你如何构建一个运行在那些电脑之一上、或运行在云端、或运行在本地的智能体？嗯，答案很简单，你这样做。你构建智能体的方式是让一个智能体为你构建它。所以这就是将要运行下一个应用的提示词。你即将看到的是：你即将看到一个机器人应用。当你看这个演示时，请记住有一台个人电脑上运行着一个智能体。这个演示跨越三台电脑。第一台电脑用于训练。第二台是数字孪生。第三台是将 AI 放入电脑本身。好吗？所以两个想法。第一个想法是智能体：你如何构建和运行它，以及你可以用它做什么。第二个是物理 AI 计算模型，它由三台电脑组成。让我们来看看。

Well, let me start—let me end today's speech from where we started. NVIDIA started in the PC revolution. It was in 1993, and the PC revolution was just about to start. Remember Windows 95 kicked it into turbocharge. We came since then, invented the GPU, programmable shading, real-time ray tracing. We added CUDA to the computing model and helped ignite the modern AI revolution. And so four years ago we started to think, what can we do to reimagine the personal computer for this new era? Where the personal computer is not just your tool, your personal computer tool, but your personal agent or your personal assistant. And the way you interact with this computer would be essentially a companion. Somebody who helps you do work. And so this computer needs to have exactly the five things that I mentioned today. It needs to have a model. It could be running locally or could be running in the cloud. It needs a harness. It needs a secure runtime, OpenShell. And it needs access—because it's available on your PC, it needs access to knowledge and tools. And the good news is because NVIDIA has been accelerating tools for so long, if we could just create a computer that can accelerate OpenGL and DirectX and everything CUDA, everything NVIDIA has ever worked on would run on this computer. And so this is it. After 4 years of partnership with Microsoft, we have created the RTX Spark. And this is a brand new line of computers that's—literally thousands of engineering years went into this. We've been secretly working on it for 4 years, created a chip called the N1X, and then we just recently announced it with the entire PC industry joining forces. We created desktops, laptops, and workstations. This is one petaflop. One petaflop. Just to put that in perspective, in the year 2016 I delivered one petaflop and 128 GB of memory. One petaflop and 128 GB to a small company called OpenAI. This is one petaflop and 128 GB. The only difference, of course, is that I can hold this like this and DGX-1 took two people to carry upstairs. And so this is the brand new RTX Spark PC. And if you would like to have a companion workstation that runs CATIA and everything—Autodesk, Adobe, Cadence, and Synopsys—you can always get yourself a DGX Station that runs Windows. And this is 20 petaflops and 754 GB of memory. And so incredible. And of course, there's also the desktop version. All of these are now available, and I think the DGX Station with RTX Spark—DGX Spark, DGX Station with Windows—will be available at the end of this year. Now, the question is—so far I just talked about the five components of an agent. And now the question is how do you build it? How do you build an agent that runs, let's say, on one of those computers or runs in the cloud or runs on-prem somewhere? Well, the answer is this—really easy, and you do it this way. The way you build an agent is you ask an agent to build it for you. So this is the prompt that is going to run the next application. And what you're about to see is this: you're about to see a robotics application. And while you're watching this demo, just remember there's an agent running on this personal computer. This demo runs across three computers. The first computer is to train. The second is a digital twin. And the third is to put that AI into the computer itself. Okay? So two ideas. The first idea is agents: how do you build it and run it and what you can use it to do. And the second is physical AI computing model, which consists of three computers. Let's take a look.

来认识一下 MicroDuck，Hugging Face 的机器人。让我们用 NVIDIA RTX Spark 教它看和走。我用 Hermes 作为我的智能体运行框架。NeMo Tron 和 OpenAI 的 GPT-6 Astra 作为我的模型，以及 CUDA-X 加速工具、FreeCAD、Blender、Isaac Sim、Newton 物理引擎和 Isaac Lab。我会在 RTX Spark 上运行一切，除非我需要将更大的工作负载突发到云端。首先，我描述课程以及我的 Micro Duck 需要学习什么。在 RTX Spark 上，Hermes 和 NeMo Tron 规划课程，列出所需的每个对象。Astra 接手，使用 FreeCAD 建模 3D 对象，Blender 应用材质，Omniverse Sim Ready 库添加物理属性。在 Isaac Sim 中，Astra 构建场景。Astra 装配 Micro Duck 的关节、执行器和链接，并用 Newton 物理引擎模拟 Micro Duck 如何移动。现在，训练开始。在云端，数千个 Micro Duck 在 Isaac Lab 中并行训练。每次跌倒都是一课，每次都改进策略。Omniverse 库模拟 Micro Duck 的摄像头，教它看。感知和物理协同工作，我的 Micro Duck 可以避开障碍物、穿越狭窄空间并适应周围环境。我将策略下载到 RTX Spark，并使用 NeMo Tron 和 Isaac Sim 通过多机器人编排评估我的 Micro Duck。我将验证过的策略部署到真实的鸭子身上，他就上路了。NVIDIA RTX Spark，在你的笔记本电脑上构建和运行 AI 智能体及其加速工具。

Meet MicroDuck, Hugging Face's robot. Let's teach it to see and walk with NVIDIA RTX Spark. I use Hermes as my agent harness. NeMo Tron and OpenAI's GPT-6 Astra as my models, and CUDA-X accelerated tools, FreeCAD, Blender, and Isaac Sim, Newton physics engine, and Isaac Lab. I'll run everything on my RTX Spark, except when I need to burst larger workloads to cloud. First, I describe the course and what my Micro Duck needs to learn. On RTX Spark, Hermes and NeMo Tron plan the course, listing every object required. Astra takes it from there, using FreeCAD to model 3D objects, Blender to apply materials, and Omniverse Sim Ready libraries to add physical properties. In Isaac Sim, Astra composes the scene. Astra rigs Micro Duck's joints, actuators, and links, and simulates how Micro Duck moves with Newton physics engine. Now, training begins. In the cloud, thousands of Micro Ducks train in parallel in Isaac Lab. Every fall, a lesson, improving the policy every time. Omniverse libraries simulate Micro Duck's cameras, teaching it to see. With perception and physics working together, my Micro Duck can avoid obstacles, navigate tight spaces, and adapt to its surroundings. I download the policy to RTX Spark, and use NeMo Tron and Isaac Sim to evaluate my Micro Duck with multi-robot orchestration. I deploy the validated policy to the real duck and he's on his way. NVIDIA RTX Spark, build and run AI agents and their accelerated tools right on your laptop.

嘿，伙计们。哦，是吗？好的，来吧。来，过来这边。给大家展示你能走路。你有摄像头？哦，是吗？别绊倒。你们能坐下吗？好的，站起来。睡个午觉。你需要睡个午觉。好的，站起来。你知道你是在这里被创造出来的吗？你是在 RTX Spark 上被创造出来的。我知道。在云端用一大堆 NVIDIA 超级计算机训练了你？你就在 RTX Spark 上，在数字孪生，Omniverse 数字孪生中学会了你的技能。是不是很不可思议？我知道。你们太可爱了。你们的父母叫 Pollen，p-o-l-l-e-n，对吗？嗯哼。Pollen Robotics。显然大家都太爱你们了。你们卖光了。我知道。所以，如果你们访问 Pollen 网站，所有看这个的人，你知道，会有大约 2000 万观众。所有看这个的人，去 Pollen 网站下订单。我认为这家初创公司大概有 10 个人。他们可能会爆红。噗。太令人兴奋了。每个人都应该有一个。你们太可爱了。所以，我们只需要一个 RTX Spark，我们就会教你们新技能。对不对？Oi，oi。Oi，oi。哦，看那个。太不可思议了。你能翻个跟头吗？太好了。太好了。哇。好的。

Hey guys. Oh, yeah? Okay, come on. Here, come over here. Show people you can walk. You have a camera? Oh, yeah? Don't trip. Can you guys sit down? Okay, stand up. Take a nap. You need to take a nap. Okay, stand up. Did you know that you were created in here? You were created on RTX Spark. I know. And trained you in the cloud on a whole bunch of NVIDIA supercomputers? And you learned your skills right here on RTX Spark in a digital twin, the Omniverse digital twin. Isn't that incredible? I know. You guys are so cute. And your parents are called Pollen, p-o-l-l-e-n, is that right? Uh-huh. Pollen Robotics. And apparently everybody loves you guys so much. You're sold out. I know. So, if you go to the Pollen website, everybody watching this, you know, there's going to be like 20 million viewers. Everybody watching this, go to the Pollen website and place an order. I think this startup company is like 10 people. They might blow up. Poof. It's going to be so exciting. Everybody should have one. You guys are so cute. And so, we'll just have an RTX Spark and we'll teach you new skills. Isn't that right? Oi, oi. Oi, oi. Oh, look at that. That's incredible. Can you do a somersault? That's so good. That's so good. Wow. Okay.

嗯，你们猜怎么着？好的，那么今天我们聊了什么？我们聊了欧洲构建其 AI 经济。而这一切始于基础设施。我们发布了一个全新的系统，叫做 DGX Pod。它让你能够轻松构建 AI 工厂。而这些 AI 工厂不再只是运行模型。注意听。保持专注。过来。过来。到这边来。保持专注。而这些 AI 工厂不再只是运行——好了。坐下。是的。它们不再只是运行 AI 模型。它们运行整个智能体。而这些智能体由五部分组成。有大语言模型负责感知、推理、规划和行动。它与一个框架协同运行，这个框架本质上把它变成外骨骼。而这个框架运行在一个我们称为 Open Shell 的安全运行时内，有很多人支持它。你让它能够访问知识和工具。这个智能体在这些计算机上运行，但我们也在扩展到下一代，这就是你们参与的地方——具身 AI。所以，就是这样。这就是我们聊的内容。每个人都可以构建智能体。欧洲的每家公司都将成为 AI 公司。毫无疑问，每个人都会成为 AI 公司。我们将在欧洲拥有一个繁荣的 AI 经济。所以，33 年后，多么不可思议的旅程。我们制作了一个非常酷的视频。你们想一起看吗？好的，来吧。到这边来。就过来一点点。我们会让开。过来。最高速度。最高速度。

Well, guess what? Okay, so today, what did we talk about? We talked about Europe building its AI economy. And it starts with infrastructure. And we announced a brand new system called DGX Pod. It makes it easy for you to build AI factories. And these AI factories don't just run models anymore. Pay attention. Stay focused. Come here. Come here. Come over here. Stay focused. And these AI factories don't just run— There you go. Sit down. Yeah. They don't just run AI models anymore. They run entire agents. And these agents are consistent of five things. There's the large language model that does the perception, reasoning, planning, and acting. And it runs in conjunction with a harness that turns it essentially exoskeleton. And it runs that harness that's inside runs inside a secure runtime we call Open Shell, which has just so many people that have supported it. And you give it access to knowledge and tools. And this agent runs on top of these computers, but we're also extending into the next generation, which is where you guys come in, physical AI. So, that's it. That's what we talked about. Everybody can build agents. Every company in Europe will become an AI company. There's no question everybody will become an AI company. And we're going to have a thriving AI economy here in Europe. So, after 33 years, what an incredible journey. And we made a really cool video. You guys want to watch it together? Okay, come on. Come over here. Just come over here just a little bit. We'll get out of the way. Come here. Top speed. Top speed.

我们到底该怎么接这个？

How the heck are we supposed to follow that?

嗯，有一件事可以让这群人更疯狂。

Well, there is one thing that can make this crowd go even crazier.

是什么？

What is it?

你们不会相信的。GTC 刚刚完全变成了欧洲歌唱大赛！主题演讲将在柏林开始。AI 的下一个篇章开始了。改变与你并肩工作。欧洲的时刻已经到来。但如果你错过了我们在 GTC 上说的内容，我们会为你分解。德国 这是一个关于世界现在如何建设新基础设施的故事。首先，发电厂让能源规模化，然后出现了数据中心。现在我们有了邮件。现在我们早已超越了将煤炭转化为瓦特。AI 工厂将沙子变成思想。让开石油，S&P 2 更值得投资。我们的 GPU 像资产一样运作，产生绿色，看起来像芯片，行为像提款机。它们在你放松时赚钱。现金流比这沙子还顺畅。智能体编码并做出决策。给它们自由但控制它们的任务。Open Shell 在裸机上检查列表。没有凭证，访问被拒绝。世纪在蓝场上从不休息，像 Hasselhoff 一样保护沙盒。我们推进 AI 但限制风险。我们负责任地乐观。我们的 TX 点让智能体安全明亮。我们的现货价格保持价格恰到好处。我们做量子风险和质押一体化，因为你不能拼写可替代而没有 是的，它现在有派对，它正在学习重力、摩擦的规则，并使用 Isaac Lab 和 Cosmos 的真实工具。它们自己训练。就像机器人的大学没有学生贷款。DSX 舱保持 AI 同步。更少电力，更多大脑，像会思考的乐高。VS 名字，我们试图冷却我的头发。比保龄球收银员更快地抛出知识。你的整个堆栈，都在它的清单上。AI 的五层蛋糕，它把它当早餐吃。欧洲生态系统在开源方面领先，问问 Hugging Face 就知道。大陆正在崛起，最好的还未到来。来吧全欧洲，让我们看到 ASW。我们是欧洲。是的，是欧罗巴。是的，我是欧罗巴。是的，我是欧罗巴。我们是欧罗巴。我们是欧洲。我们是欧洲。你和我。欢迎来到 GTC。欢迎来到 GTC。我想感谢你们所有人。我想感谢你们所有人，所有赞助商。你可以看到这里所有公司的名字，所有我们合作了 33 年的合作伙伴。Micro Duck，我的新朋友。嗯，让我们一起构建欧洲的 AI 经济，让 AI 发挥作用。好的，准备好吧。祝 GTC 愉快。

You won't believe this. GTC has just gone full Eurovision! The keynote's starting in Berlin. AI's next chapter begins. Changes working by your side. Europe's moment has arrived. But if you missed what we said at GTC, we'll break it down for you. Germany This is a story all about how the world's building new infra now. First, power plants let energy scale, then came data centers. Now we've got mail. Now we're way past turning coal into watts. Say I factories turn sand into thoughts. Move over oil, the S&P two is something more investable. Our GPU works like an asset producing green, looks like a chip, acts like a cash machine. They make revenue while you relax. The cash flows smoother than this sand. Agents code and make decisions. Give them freedom but control their missions. Open shell on bare as checking the list. No credentials, access dismissed. Century on blue field never clocks off protecting the sandbox like Hasselhoff. We advance AI but limit risks. We're responsibly optimistic. Our TX spots keep agents safe and bright. Our spot rates keep the price just right. We do quantum risks and staking all in one cuz you can't spell fungible without Yeah, it's got a party now it's learning the rules of gravity, friction, and using real tools with Isaac Lab and Cosmos. They train on their own. It's like college for robots without student loans. DSX pods keep AI in sync. Less power, more brains like Legos that think. VS name, we're trying to cool my hair. Throwing knowledge faster than a bowling cashier. Your whole stack, it's all on its checklist. AI's five layer cake, it eats it for breakfast. The Euro ecosystem is leading the race in love open source, just ask Hugging Face. The continent is rising and the best is yet to come. Come on all of Europe, let's see ASW. We are Europe. YES, IT'S EUROPA. YES, I'M EUROPA. YEAH, I'M EUROPA. We are Europa. We are Europe. We are Europe. You and me. Welcome all to GTC. Welcome to GTC. I want to thank all of you. I want to thank all of you, all the sponsors. You can see all the names of companies that are here, all the partners we've been working with for 33 years. Micro Duck, my new friends. Um let's work together to build Europe's AI economy and put AI to work. All right, get ready. Have a great GTC.

嘿，是的。嗯。

Hey, yeah. Mhm.

嘿，是的。手机安全。

Hey, yeah. Phone safe.