# (77) NVIDIA GTC Keynote 2026 - YouTube
- Transcript ID: `jw_o0xr8mwu`
- Source tier: `youtube_creator`
- Original language: `en_or_source_language_unlabeled`
- Translation status: `agent_translated_reviewed` (239/239 chunks)
- Original source: https://www.youtube.com/watch?v=jw_o0xr8MWU
- Raw evidence: `data/transcripts/youtube/jw_o0xr8MWU.json`
> 中文是派生翻译，英文是留存的源文本。中文未复核时，以英文和原始音视频为准。
## 00:00:08
**中文**
【开场视频】智能就是这样炼成的：一种生产 token 的新型工厂，制造 AI 的基本构件。Token 开辟了新边疆，把数据转化为知识，并汲取人类积累的一切经验；它们正利用新一波清洁能源，揭开群星的奥秘。
**English / Original**
[INTRO VIDEO]: This is how intelligence is made. A new kind of factory generator of tokens. The building blocks of AI. Tokens have opened a new frontier, turning data into knowledge and drawing on all we have learned. Tokens are harnessing a new wave of clean energy. And unlocking the secrets of the stars.
## 00:00:53
**中文**
在虚拟世界中，它们帮助机器人学习；在物理世界中，它们精益求精，开辟新道路，为丰收扫清障碍。在重要时刻，token 已经在场；在漫长路途中，它们从不停歇。
**English / Original**
In virtual worlds. They help robots learn. And in the physical world, perfect. Forging new paths. And clearing the way for a Bountiful harvest. In the moments that matter. Tokens are already there. And in the miles between they never stop. They work
## 00:01:34
**中文**
它们在人手无法抵达之处工作，让所有人呼吸更轻松，也让最幼小的心脏跳得更有力。
**English / Original**
where human hands cannot. So we may all breathe easier. And the smallest hearts beat stronger.
## 00:02:05
**中文**
Token 正帮助我们以前所未有的规模破土开拓、赋能世界，使我们能够抵达 Star Cloud One，并确认远远超越它的分离。
**English / Original**
Tokens are helping us break new ground. On a scale never attempted. To empower the world. So we can reach Star Cloud one. Separation confirmed well beyond it. Together we take the
## 00:02:43
**中文**
我们共同迈向下一个伟大飞跃，走进为全人类建设的光明未来。而一切从这里开始。
**English / Original**
next great leap into a bright new future. Built for all mankind. And here is where it all begins.
## 00:03:18
**中文**
【黄仁勋】欢迎参加 GTC！先提醒各位，这是一场技术大会。大家一大早就排队，很高兴见到满场观众。我们将讨论技术、平台。NVIDIA 有三个平台。
**English / Original**
[JENSEN HUANG] Welcome to GTC! I just want to remind you this is a tech conference. All these people lining up so early in the morning. All of you in here. It's great to see you, GTC. We're going to talk about technology. We're going to talk about platforms. NVIDIA has three platforms.
## 00:04:01
**中文**
大家常听我们谈的一个是 CUDA-X，系统是另一个，如今又有名为“AI 工厂”的新平台。今天都会讲到，更重要的是生态系统。开始前先感谢赛前节目主持人，他们表现很棒。
**English / Original**
You think that we mostly talk about. One of them is related to CUDA-X. Our systems is another platform. And now we have a new platform called AI factories. We're going to talk about all of them. And most importantly we're going to talk about ecosystems. But before I start. Let me thank our pre game show hosts. I thought they did a great job.
## 00:04:25
**中文**
感谢 Conviction 的 Sarah Guo、红杉资本的 Alfred Lin——NVIDIA 第一位风险投资人，以及 NVIDIA 第一位重要机构投资者 Gavin Baker。他们深谙技术和当下趋势，也覆盖广阔技术生态。还要感谢今天精心邀请的所有贵宾，堪称全明星阵容。
**English / Original**
Sarah Guo of conviction. Alfred Lin. Sequoia capital. NVIDIA's first venture capitalist. Gavin Baker NVIDIA's first major institutional investor. These three people are deep in technology, deep in what's going on. And of course, they have just a really broad range of technology ecosystem. And then, of course, all of the VIPs that are hand-selected to join us today, all star team, I want to thank all of you for that.
## 00:05:03
**中文**
也感谢所有到场企业。NVIDIA 是一家拥有技术、平台和丰富生态的平台公司。今天，价值 100 万亿美元的产业可能百分之百都在现场。450 家企业赞助本次活动，有 1000 场技术会议、2000 位讲者。大会覆盖 AI 五层架构的每一层：从土地、
**English / Original**
I also want to thank all the companies that are here. NVIDIA, as you know, is a platform company. We have technology. We have our platforms. We have rich ecosystem. And today there are probably 100% of the $100 trillion of industry here. 450 companies sponsored this event. I want to thank you. A thousand technical sessions, 2000 speakers. This is this conference is going to cover every single layer of the five layer cake of artificial intelligence from land,
## 00:05:43
**中文**
电力和基础设施外壳，到基础设施、芯片、平台与模型。
**English / Original**
power, and shell to infrastructure to chips to the platforms, the models.
## 00:05:48
**中文**
最终让产业真正起飞的当然是应用，而一切始于这里。今年是 CUDA 诞生 20 周年。二十年来我们专注于这一革命性架构：SIMT（单指令多线程），让标量代码可扩展为多线程应用，比 SIMD 容易编程得多。
**English / Original**
And of course, the most important and ultimately what's going to take get this industry taken off is all of the applications. But it all began. It all began here. This is the 20th anniversary of CUDA. We've been working on CUDA for 20 years. For 20 years we've been dedicated to this architecture, this revolutionary invention. SIMT single instruction, multithreaded writing scalar code could spun off into multithreaded application. Much, much easier
## 00:06:29
**中文**
最近我们加入 tile，帮助开发者为 Tensor Core 和当今 AI 的基础数学结构编程。开源生态中有数千种工具、编译器、框架和库，以及约二十万个公开项目。CUDA 已真正融入每一个生态系统。
**English / Original**
to program than SIMD. We recently added tiles so that we could help people program tensor cores and the structures of mathematics that are so foundational to artificial intelligence today. Thousands of tools and compilers and frameworks and libraries in open source. There's a couple of hundred thousand public projects. CUDA literally is integrated into every single ecosystem.
## 00:07:01
**中文**
这张图概括了 NVIDIA 百分之百的战略，我从一开始就在讲它。最难构建的是底部的装机基础。二十年后，全球已有数亿块运行 CUDA 的 GPU 和计算系统；我们遍布每朵云、每家计算机公司，服务几乎所有行业。
**English / Original**
This chart basically describes 100% of NVIDIA's strategies. You've been watching me talk about this slide from the very beginning. And ultimately, the single hardest thing to achieve is the thing on the bottom installed base. It has taken us 20 years to now have built up hundreds of millions of GPUs and computing systems around the world that run CUDA. We are in every cloud. We're in every computer company. We serve just about every single industry.
## 00:07:36
**中文**
CUDA 装机基础让飞轮加速：它吸引开发者，开发者创造新算法并取得深度学习等突破；突破催生新市场，形成吸引更多公司加入的新生态，又扩大装机基础。这个飞轮正在加速，NVIDIA 库的下载量规模巨大、增速空前。
**English / Original**
The installed base of CUDA is the reason why the flywheel is accelerating. The installed base is what attracts developers, who then creates new algorithms that achieves a breakthrough, for example, deep learning. There are so many others. Those breakthroughs lead to entirely new markets, which builds new ecosystems around them with other companies that join, which creates a larger installed base. This flywheel. This flywheel is now accelerating. The number of downloads of NVIDIA libraries is incredibly accelerating. It's at a very large scale and
## 00:08:14
**中文**
飞轮让计算平台支撑众多应用与突破，也使基础设施拥有极长使用寿命。原因很直观：CUDA 可运行海量应用，覆盖 AI 生命周期每个阶段、所有数据处理平台，并加速各种科学第一性原理解算器。因此 GPU 一经安装，
**English / Original**
growing faster than ever. This flywheel is what makes this computing platform able to sustain so much applications, so many new breakthroughs. But most importantly, it also enables these infrastructures to have extraordinarily useful life. And the reason for that is very obvious. There are so many applications that you can run on NVIDIA CUDA. We support the entire every single phase of the AI lifecycle. We address every single data processing platform. We accelerate scientific principal solvers of all different kinds. And so the application reach is so great that once you
## 00:08:57
**中文**
使用寿命就非常长。这也是六年前发布的 Ampere 在云端价格仍上涨的原因。装机基础、飞轮和开发者覆盖共同作用，而持续更新软件又不断降低计算成本。加速计算首次带来巨大提速，后续软件优化还会持续
**English / Original**
install NVIDIA GPUs, the useful life of it is incredibly high. It is also one of the reasons why Ampere that we shipped some six years ago. The pricing of Ampere in the cloud is going up. And so all of that is made possible fundamentally because the installed base is high, the flywheel is high, the developer reach is great. And when all of that happens and we continuously update our software, the computing cost declines, the combination of accelerated computing speeding up applications tremendously. Meanwhile, as we continue to nurture and continue to update software over its life, not only do you get the first time pop, you get the continuous
## 00:09:42
**中文**
降低成本。全球 GPU 架构兼容，因此我们愿意长期支持每一块；一次新优化就能惠及数百万设备。这种动力让 NVIDIA 架构扩大覆盖、加速增长，同时压低计算成本并催生新增长，CUDA 位于这一切的中心。
**English / Original**
cost reduction of accelerated computing over time, and we're willing to nurture willing to support every single one of these GPUs in the world because they're all architecturally compatible. We're willing to do so because the installed base is so large. If we release a new optimization, it benefits millions. This applies to everybody in the world. This combination of dynamics is what makes the NVIDIA architecture expand its reach, accelerating its growth. At the same time driving down computing costs, which ultimately encourages new growth. So CUDA is at the center of it.
## 00:10:23
**中文**
但通往 CUDA 的旅程始于 25 年前的 GeForce。多少人伴随 GeForce 长大？它是 NVIDIA 最成功的营销活动：在未来客户还付不起钱时就吸引他们——先由父母年复一年替你付款，直到有一天
**English / Original**
But our journey to CUDA actually started 25 years ago. GeForce. I know how many of you grew up with GeForce. GeForce is NVIDIA's greatest marketing campaign. We attract future customers starting long before you could afford to pay for it yourself. Your parents paid. Your parents paid your parents. Your parents paid for you to be NVIDIA customers. And every single year they paid up year after year after year until someday
## 00:11:05
**中文**
你成为出色的计算机科学家，变成真正的客户和开发者。
**English / Original**
you became an amazing computer scientist and became a proper customer, a proper developer.
## 00:11:12
**中文**
这就是 GeForce 建造的基业。25 年前，我们发明可编程着色器，把加速器变成可编程设备——全球首个可编程加速器像素着色器。这项并不显然的发明开启旅程，并在五年后通向 CUDA。
**English / Original**
But this is this is the house that GeForce made. 25 years ago, we started our journey, which led to CUDA. 25 years ago, we invented the programmable shader, a perfectly unobvious invention to make an accelerator programmable the world's first programmable accelerator, the pixel shader, 25 years ago. That led us to explore further and further 20 years later.
## 00:11:43
**中文**
CUDA 是我们最大的投资之一，当时公司负担不起，耗费了绝大部分利润。我们借助 GeForce 把 CUDA 带到每台计算机，坚信其潜力，尽管早期困难仍每日坚持。历经二十年、十三代产品，CUDA 如今无处不在；像素着色器也引发了 GeForce 革命。
**English / Original**
Five years later, the invention of CUDA, one of the biggest investments that we made, and we couldn't afford it at the time, and it consumed the vast majority of our company's profits, was to take CUDA on the backs of GeForce to every single computer We dedicated ourselves to creating this platform because we felt so much—we felt so strongly about its potential. But ultimately, the company's dedication to it, despite the hardships in the beginning, believing in every single day for. For 13 generations of 20 years, we now have CUDA installed everywhere. The pixel shader led to, of course, the revolution of GeForce.
## 00:12:28
**中文**
约八年前，我们推出 RTX，为现代计算机图形学彻底重构架构。GeForce GTX 把 CUDA 带向世界，使 Alex Krizhevsky、Ilya Sutskever、Geoff Hinton、Andrew Ng 等发现 GPU 能加速深度学习，开启十年前 AI 大爆炸。随后我们把可编程着色与两个新理念融合，其中之一是光线追踪。
**English / Original**
And then ten years ago we introduced about ten years ago. What is it? Eight years ago, we introduced RTX, a complete redesign of our architecture for the modern era of computer graphics. GeForce GTX CUDA to the world. GeForce therefore enabled Alex Krizhevsky and Ilya Sutskever and Geoff Hinton, Andrew Ng, and so many others to discover that the GPU could be their friend in accelerating deep learning. It started the big bang of AI ten years ago. We decided that we would fuse programmable shading and introduced two new ideas ray tracing,
## 00:13:12
**中文**
硬件光线追踪极难实现，在当时也是全新理念。
**English / Original**
hardware, ray tracing, which is incredibly hard to do, and a new idea at the time.
## 00:13:17
**中文**
十年前，我们设想 AI 将革新计算机图形学：GeForce 把 AI 带给世界，AI 又会反过来彻底重塑图形学。今天将展示未来技术——下一代图形技术“神经渲染”，也就是
**English / Original**
Imagine about ten years ago, we thought that AI would revolutionize computer graphics. Just as GeForce brought AI to the world, AI is now going to go back and revolutionize how computer graphics is done all together. Well, today I'm going to show you something of the future. This is our next generation of graphics technology. We call it neural rendering, the fusion,
## 00:13:42
**中文**
3D 图形与人工智能的融合。这就是 DLSS 5，请看。
**English / Original**
the fusion of 3D graphics and artificial intelligence. This is DLSS 5. Take a look at it.
## 00:14:19
**中文**
【视频——无旁白】
**English / Original**
[VIDEO - NO NARRATION] [JENSEN]
## 00:14:59
**中文**
是不是不可思议？图形世界活了起来。我们融合了可控制的 3D 图形——虚拟世界的真实基准和结构化数据——与生成式 AI 的概率计算。前者完全可预测，后者具有概率性却高度逼真。我们把这两个理念结合起来，
**English / Original**
Is that incredible? Computer graphics comes to life. Now what did we do? We fused controllable 3D graphics. The ground truth of virtual worlds. The structured data. Remember this word? The structured data of virtual worlds of generative generated worlds. We combine 3D graphics, structured data with generative AI probabilistic computing. One of them is completely predictive, the other one probabilistic, yet highly realistic. We combine these two ideas, combine these two
## 00:15:39
**中文**
借助结构化数据精确控制，同时进行生成，内容因此既精美惊艳又可控。结构化信息与生成式 AI 的融合将逐个重演于各行各业。结构化数据是可信 AI 的基础。
**English / Original**
ideas, control through structured data controlled perfectly and yet generating at the same time. And as a result, the content is beautiful. amazing as well as controllable. This concept of fusing structured information and generative AI will repeat itself in one industry after another, industry after another industry. Structured data is the foundation of trustworthy AI.
## 00:16:11
**中文**
接下来可能有点吓人，我要翻到下一页，请别倒吸凉气。余下时间都要讲这张示意图，这是我最好的幻灯片。团队反复劝我别用，我说不行——有些座位免费，这就是各位的入场代价。
**English / Original**
Well. This is going to scare you a little bit. I'm going to flip the slide and don't gasp. ________________ So we're going to go through this schematic for the rest of the time. This is my best slide. Every time I asked my I asked the team, what's my best slide? Repeatedly. This was it. They said, don't do it, Jensen. Don't do it. I said, no. These seats are free, for some of you. So this is your price of admission. So
## 00:16:48
**中文**
这里是结构化数据：SQL、Spark、Pandas、Velox，以及 Snowflake、Databricks、Amazon EMR、Azure Fabric、Google Cloud BigQuery 等重要大型平台。它们处理的 DataFrame 就像巨型电子表格，承载生活中的全部信息，是商业和企业计算的事实基准。
**English / Original**
this is this is structured data. You've heard of it. SQL, Spark. Pandas, velox. Some of these really really important very large platforms. Snow. Snow. Snowflake. Databricks EMR, Amazon EMR, um Azure fabric. Google cloud BigQuery. All of these platforms are processing data frames. These data frames are giant spreadsheets and they hold all of life's information. This is the structured data, the ground truth of business. This is the ground truth of enterprise computing.
## 00:17:30
**中文**
如今 AI 将使用结构化数据，我们必须大幅加速它。过去加速数据是为提高处理量、降低成本、增加每日频次，使企业更同步；未来 AI 和智能体会使用这些数据结构，而且速度远超人类。另一边则是非结构化数据库，
**English / Original**
Well, now we're going to have AI use structured data and we better accelerate the living daylights out of it. It used to be okay. And we would, you know, of course, we would accelerate structured data so that we could do more. We could do it more cheaply. We could do it more frequently per day and keep the company running at a much more synchronized way. However, in the future, what's going to happen is these data structures are going to be used by AI, and AI is going to be much, much faster than us. Future agents are going to use structured databases as well. And then of course the unstructured database, the
## 00:18:08
**中文**
也就是生成式数据库，代表世界绝大多数信息：向量数据库、PDF、视频、语音等非结构化数据。每年约 90% 的新数据都是非结构化的。过去我们只能读取并存入文件系统，难以查询和搜索，因为它没有简单索引，必须先理解其含义与目的。
**English / Original**
generative database this database is represents the vast majority of the world vector databases, unstructured data, PDFs, videos, speeches, all of the world's information. About 90% of what's generated every single year is unstructured data. Until now, this data has been completely useless to the world. We read it, we put it into our file system, and that's it. Unfortunately, we can't query it. We can't search for it. It's hard to do that. And the reason for that is because there's no easy indexing of unstructured data. You have to understand its meaning, its purpose. And so
## 00:18:46
**中文**
如今 AI 可以完成这件事。解决多模态感知与理解的同一技术可以阅读 PDF、理解含义，再把语义嵌入可搜索、可查询的更大结构。正如为 3D 图形创建 RTX，NVIDIA 为结构化 DataFrame 创建 cuDF，为语义和非结构化向量存储创建 cuVS。
**English / Original**
now we have AI do that. Just as AI was able to solve multi-modality perception, you can and understanding you can use that same technology multi-modality perception and understanding to go read a PDF to understand its meaning, and from that meaning embedded into a larger structure that we can search into, we can query into. NVIDIA created two foundational libraries, just like we created RTX for 3D graphics. We created cuDF for DataFrames, structured data, we created cuVS for vector stores, semantic data, unstructured data,
## 00:19:27
**中文**
也就是 AI 数据。这两个平台将成为未来最重要的平台之一。
**English / Original**
AI data. These two platforms are going to be two of the most important platforms in the future.
## 00:19:34
**中文**
看到它们逐步被全球复杂的数据处理网络采用，令人振奋。数据处理历史悠久，企业、平台和服务众多，我们花很久才深入融入生态，我为这项工作非常自豪。
**English / Original**
Super excited to see its adoption throughout the network. That's complicated network of the world's data processing systems. And the reason for that is because data processing has been around a long time and therefore so many different companies and platforms and services. It has taken us a long time to integrate deeply into this ecosystem. I'm super proud of the work that we're doing here.
## 00:19:57
**中文**
今天我们宣布几项合作。SQL 的发明者 IBM 正用 cuDF 加速 Watsonx.data。60 年前 IBM 推出首个现代通用计算平台 System/360，开启计算时代；随后又创造 SQL——无需逐步指示计算机即可查询数据的声明式语言——以及数据仓库，它们都是
**English / Original**
And then today we're announcing several of them. IBM, the inventor of SQL, one of the most important domain specific languages of all time, of all time is accelerating Watsonx.data with cuDF. Let's take a look at it. [VIDEO NARRATION - JENSEN] 60 years ago, IBM introduced the system 360, the first modern platform for general purpose computing. Launching the computing era, then SQL, a declarative language to query data without requiring the computer to be instructed step by step and the data warehouse. Each. The foundations
## 00:20:40
**中文**
现代企业计算的基础。如今 IBM 与 NVIDIA 用 GPU 计算库加速 Watsonx.data SQL 引擎，重塑 AI 时代的数据处理。数据为 AI 提供语境与意义，但 AI 需要快速访问海量数据，CPU 系统已跟不上。雀巢每天作出数千项供应链决策，其订单到现金数据集市汇总全球 185 个国家的每笔订单与交付事件。
**English / Original**
of modern enterprise computing today. IBM and NVIDIA are reinventing data processing for the era of AI by accelerating IBM Watsonx.data data SQL engines with NVIDIA GPU computing libraries. Data is the ground truth that gives AI context and meaning. AI needs rapid access to massive data sets. Today's CPU data processing systems can't keep up. Nestle makes thousands of supply chain decisions every day. Their order to cash Data Mart aggregates every supply order and delivery
## 00:21:18
**中文**
CPU 上每天只能刷新几次；在 NVIDIA GPU 加速的 Watsonx.data 上，同一负载快五倍、成本低 83%。下一代计算平台已经到来。NVIDIA 不仅加速云端数据处理，也加速
**English / Original**
event across global operations in 185 countries. On CPUs, Nestle refreshed the Data Mart a few times a day with accelerated Watsonx.data data running on NVIDIA GPUs. Nestle can run the same workload five times faster at 83% lower cost. The next computing platform has arrived. Accelerated computing for the era of AI. [JENSEN] NVIDIA accelerates data processing in the cloud. We also accelerate data processing on
## 00:21:59
**中文**
本地部署。戴尔既是全球领先计算系统厂商，也是重要存储供应商。双方创建 Dell AI Data Platform，整合 cuDF 与 cuVS，为 AI 时代提供加速数据平台；与 NTT DATA 的案例实现巨大提速。
**English / Original**
prem. As you know, Dell is the world's leading computer systems maker. And they also are one of the world's leading storage providers. And they worked with us to create the Dell AI data platform that integrates cuDF and cuVS to create an accelerated data platform. Well, for the era of AI, and this is an example of what they did with NTT data. Huge speed up.
## 00:22:25
**中文**
在云端，我们与 Google Cloud 长期合作，既加速 Vertex AI，如今也加速重要平台 BigQuery。与 Snapchat 的合作把计算成本降低近 80%。加速数据处理和计算，会同时获得速度、规模和成本优势。过去这被称为摩尔定律。
**English / Original**
This is cloud, Google Cloud and Google Cloud. As you know we've been working with Google Cloud for a very long time. We accelerate Google's Vertex AI. We now accelerate BigQuery. really important framework and really important platform. And this is an example of our work together with Snapchat, where we reduce their cost of computing by nearly 80%. When you accelerate data processing, when you accelerate computing, you get the benefit of speed, you get the benefit of scale, but most importantly, you also get the benefit of cost. And so all of those come together as one. It was originally called Moore's
## 00:23:08
**中文**
摩尔定律意味着每隔几年性能翻倍：若价格和计算机规模不变，性能提高、成本逐年下降。但摩尔定律已失去动力，需要新方法。加速计算能实现巨大跃升；NVIDIA 也是算法公司，会持续优化算法，依托广泛覆盖和庞大装机基础，
**English / Original**
Law. Moore's law was about getting performance doubling every couple of years. That's another way of saying so long as the price remains about the same and most computers remained about the same. You're also getting twice the performance every year, or you're reducing the cost of computing every single year. Well, Moore's Law has run out of steam. We need a new approach. Accelerated computing allows us to take these giant leaps forward. And as you will see later, because we continue to optimize the algorithms. And NVIDIA is an algorithm company as we continue to optimize the algorithms. And because our reach is so large and our installed base is so large, we can
## 00:23:49
**中文**
为所有人持续降低计算成本、扩大规模并提高速度。
**English / Original**
reduce the computing cost, increasing the scale, increasing the speed for everybody continuously.
## 00:23:55
**中文**
这是 Google Cloud。这个模式会反复出现：NVIDIA 构建加速计算平台，上面运行 RTX、cuDF、cuVS 等库，再与全球云服务、OEM 和其他平台集成，共同触达世界。
**English / Original**
This is Google Cloud. You could see this pattern I just mentioned. I just wanted to show you three versions of it. NVIDIA built the accelerated computing platform, has a bunch of libraries on top. I gave you three examples. RTX is one of them. cuDF is another cuVS and we'll show you a few more. These libraries sit on top of our platform, but ultimately we integrate into the world's cloud services, into the world's OEMs and together and other platforms that I'll show you. Together, we're able to reach the world.
## 00:24:33
**中文**
NVIDIA、Google Cloud 和 Snapchat 的模式会不断复制。双方加速 Vertex AI、BigQuery，并在 JAX/XLA 上取得出色成果；NVIDIA 是全球唯一同时在 PyTorch 与 JAX/XLA 上表现卓越的加速器。我们把技术集成进 Baseten、CrowdStrike、Puma、Salesforce 等生态开发者的产品，再让它们落地云端。
**English / Original**
This pattern. NVIDIA, Google Cloud. Snapchat will repeat over and over again and it kind of looks like this. And so this is one example. NVIDIA with Google Cloud we accelerate vertex AI. We accelerate BigQuery. We accelerate. I'm super proud of the work that we've done with Jackson XLA. We are incredible on PyTorch. We're the only accelerator in the world that's incredible on PyTorch and incredible on Jackson, XLA and the customers that we support, the Basetens, the CrowdStrike, Puma, Salesforce, they're not our customers, but their customers, developers of ours
## 00:25:11
**中文**
我们与云服务商合作，本质上是把客户带给它们：集成库、加速负载，再让客户落地云端。因此云厂商乐于合作，总请我们把下一位客户带到它们的云。客户很多，我们会加速每个人，也会把许多客户带来，请耐心等待。
**English / Original**
that we've integrated the NVIDIA technologies into that we can then land on the clouds. Our relationship with cloud service providers are essentially us bringing customers to them. We integrate our libraries, we accelerate workloads, and we land those customers in the clouds. And so as you could see, most of our cloud service providers love working with us, and they're always asking us to land the next customer on their cloud. And I just want to let you know, there are a lot of customers. We're going to accelerate everybody. And so there'll be lots and lots of customers. We'll be able to land in your cloud. Just be patient with us.
## 00:25:54
**中文**
这是 Google Cloud，这是 AWS。双方合作已久。今年我尤其兴奋的是，我们将把 OpenAI 带到 AWS，推动巨大云计算用量，同时扩大 OpenAI 的覆盖和算力。
**English / Original**
And so this is Google Cloud. This is AWS. We've been working with AWS a long time. And one of the areas, one of the, one of the things I'm super excited about this year is we're going to bring Open AI to AWS. And so it's going to drive enormous consumption of cloud computing at AWS. It's going to expand the reach and expand the compute of Open AI.
## 00:26:15
**中文**
OpenAI 完全受算力制约。我们在 AWS 加速 EMR、SageMaker 和 Bedrock，深度融入 AWS——它是我们的第一家云伙伴。再看 Microsoft Azure：我们建造的第一台 A100 超级计算机用于 NVIDIA，首台对外安装在 Azure，由此促成与 OpenAI 的成功合作。
**English / Original**
And as you know, they are completely compute constrained. And so AWS, we accelerate EMR, we accelerate SageMaker, we accelerate bedrock. NVIDIA's integrated really deeply into AWS. They were our first cloud partner. ________________ Microsoft Azure. NVIDIA's A100 supercomputer was the first one we built was for NVIDIA. The first one we installed was at Azure, and that led to the the the big successful partnership with OpenAI. But we've been working with Azure for quite a long time.
## 00:26:53
**中文**
我们长期加速 Azure 云、AI Foundry 和 Bing Search，并合作建设 Azure 区域。全球扩展 AI 时，机密计算极为重要：要确保连运营方也无法查看数据或接触模型。NVIDIA GPU 是全球首个提供这项能力的 GPU，如今能够
**English / Original**
We accelerate Azure Cloud. Now their AI foundry, we partner deeply with we accelerate Bing search. We work with them on Azure regions. This is one of the areas that is incredibly important as we continue to expand AI throughout the world. One of the capabilities that we offer is confidential computing that in confidential computing, you want to make sure that even the operator cannot see your data, even the operator cannot touch or see your model's confidential computing NVIDIA's GPUs as the first ones in the world to do that. It's now able
## 00:27:37
**中文**
支持机密计算，安全部署极具价值的 OpenAI 模型。
**English / Original**
to support confidential computing and protected deployment of these very valuable OpenAI models.
## 00:27:43
**中文**
Anthropic 模型也能借助机密计算部署到不同云和区域。这里还有合作伙伴 Synopsys，我们加速其所有 EDA 与 C 工作流，再落地 Microsoft Azure。我们既是 Oracle 的首个 AI 客户，也是首批供应商。
**English / Original**
And and Anthropic models throughout clouds and different regions, and all because of our confidential computing. Confidential computing is super important. And here's an example where we have different customers that we work with. Synopsis. A great partner of ours, we're accelerating all of their EDA and C workflows. And then we landed at Microsoft Azure. ________________ We were Oracle's first AI customer. Most people would have thought we were their first supplier.
## 00:28:12
**中文**
我很自豪最早向 Oracle 解释 AI 云，并成为其首位客户。此后业务迅速起飞，CoHere、Fireworks 和著名的 OpenAI 等众多伙伴都被我们带到那里。
**English / Original**
We were their first supplier also, but we were their first AI customer. I'm quite proud of the fact that I explained AI clouds to Oracle for the first time, and we were their first customer. Since then, they've really taken off. We've landed a whole bunch of our partners there. Coherent fireworks. And of course, very famously, Open AI.
## 00:28:35
**中文**
我们与 CoreWeave 关系紧密。它是全球首个 AI 原生云，从创立起唯一目标就是配置和托管 GPU，迎接加速计算与 AI 云时代。它拥有优秀客户，增长惊人。
**English / Original**
________________ A great partnership with CoreWeave. They're the world's first AI native cloud, a company that was built with only one singular purpose to provision to host GPUs. As the era of accelerated computing showed up and the host for AI clouds. They've got some fantastic customers and they're growing incredibly.
## 00:29:00
**中文**
我很看好 Palantir 与 Dell 的平台。三家公司可建立全新 AI 平台——Palantir 本体平台与 AI 平台，并部署于任何国家、隔离区域、本地、现场乃至野外。没有机密计算、端到端系统能力，以及从数据处理开始的完整加速计算与 AI 栈，
**English / Original**
________________ One of the platforms that I'm quite excited about is Palantir and Dell. The three of our companies have made it possible to stand up a brand new type of AI platform, the Palantir ontology platform and AI platform. And we could stand up these platforms in any country, in any air gapped region, completely on prem, completely on site, completely in the field. AI could be deployed literally everywhere without our confidential computing capability, without our ability to build the end to end system, as well as offer the entire accelerated computing and AI stack from data processing, whether it's
## 00:29:40
**中文**
无论向量还是结构化数据一路连接到 AI，这都不可能实现。这些例子展示了我们与全球云服务商的特殊合作关系。很高兴在展区见到大家，感谢所有人的辛勤工作。
**English / Original**
vectors or structures all the way to AI. It wouldn't have been possible. I wanted to show you these examples. This is our special working relationship with the world's cloud service providers. And while all of them are here and I get the benefit of seeing them during booth tour, and it's just so incredibly exciting, I just want to thank all of you for the hard work.
## 00:30:03
**中文**
NVIDIA 的做法将反复出现：我们是全球首家“纵向整合、横向开放”的公司。原因很简单，加速计算不只是芯片问题，也不只是系统问题。
**English / Original**
What NVIDIA has done is this, and you're going to see this theme over and over again. NVIDIA is vertically integrated the world's first vertically integrated but horizontally open company. And the reason that's necessary is very simple. Accelerated computing is not a chip problem. Accelerated computing is not a systems problem.
## 00:30:31
**中文**
“加速计算”省略了一个词：应用加速。若一台计算机能让一切都更快，那就是 CPU，但这条路已失去动力。未来只有通过针对应用或领域的专用加速，才能继续大幅提速和降本。
**English / Original**
Accelerated computing has a missing word. We just never say it anymore. Application acceleration. If I could make a computer run everything faster, that's called a CPU, but that's run out of steam. The only way for us to accelerate applications going forward and continue to bring tremendous speed up, tremendous cost reduction is through application or domain specific acceleration.
## 00:31:00
**中文**
我们省略了前面的限定语，简称“加速计算”。因此 NVIDIA 必须逐个库、逐个领域、逐个垂直行业深入，是一家纵向整合的计算公司；别无他法，我们必须理解应用。
**English / Original**
I dropped that phrase in the in the front. And therefore it just became “accelerated computing.” And that is the reason why NVIDIA has to be library after library, domain after domain, vertical after vertical. We are a vertically integrated computing company. There is no other way. We have to understand the applications.
## 00:31:20
**中文**
必须理解领域和底层算法，并找到算法在数据中心、云、本地、边缘或机器人等不同系统中的部署方式。我们在算法、系统和芯片上纵向整合；同时横向开放，可把 NVIDIA 技术集成到任何平台，提供软件和库，
**English / Original**
We have to understand the domain. We have to understand fundamentally the algorithms, and we have to figure out how to deploy the algorithm in whatever scenario it wants to be deployed, whether it's a data center, cloud on prem at the edge, or in a robotic system. All of those computing systems are different. And finally, the systems and chips, we are vertically integrated. What makes it incredibly powerful, and the reason why you saw all the slides is because NVIDIA's horizontally open will work and integrate NVIDIA's technology into whatever platform you would like us to integrate into. We offer you the software, we offer you libraries,
## 00:32:00
**中文**
并与客户技术集成，让全球所有人获得加速计算。GTC 正是这种模式的展示。汽车等每个垂直行业都有案例；本届 GTC 参会人数占比最高的是金融服务业——希望来的是开发者，不是交易员。
**English / Original**
we integrate with your technology so that we can bring accelerated computing to everybody in the world. ________________ Well, this GTC is really a great demonstration of that. You know, most of the time, most of the time you'll see me talk about these verticals and I'll use them some examples, but in every single case, whether it's automotive, by the way, financial services, the largest percentage of attendees at this GTC is from the financial services industry. I know. I'm hoping it's developers, not traders,
## 00:32:37
**中文**
现场代表了 NVIDIA 上下游完整生态。无论已有 50 年、70 年还是 150 年历史的公司，过去一年都成为我们的上下游供应链伙伴。
**English / Original**
guys. Here's, here's, here's one thing I wanted to say. And so. In the audience represents NVIDIA's ecosystem upstream of our supply chain and downstream of our supply chain. And we work, we think about our supply chain upstream and downstream. And it's just so exciting that our entire upstream supply chain this last year, irrespective of whether you're a 50 year old company, we have 70 year old companies. We have 150 year old company who are now part of NVIDIA supply chain and partnering with us either upstream or downstream. And last
## 00:33:19
**中文**
去年大家都创下纪录，对吧？恭喜，我们显然找对方向了。
**English / Original**
year you had your record year, did you not? Congratulations. We're on to something here.
## 00:33:33
**中文**
这是一件大事的开端。加速计算平台已经奠定，但要激活它，还需要解决各垂直行业关键问题的领域专用库。自动驾驶只是其中之一，我们影响广泛。金融服务中的算法交易正在从人工特征工程的经典机器学习——也就是量化分析——转向超级计算机
**English / Original**
This is the beginning of something very, very big. And so if you look at accelerated computing, we've now set the computing platform. But in order for us to activate those computing platforms, we need to have domain specific libraries that solve very important problems in each one of the verticals that we address. You see us addressing every single one of these autonomous vehicles, our reach, our breadth, our impact. Incredible. We have a track on that. Financial services I just mentioned, algorithmic trading is going from classical machine learning with human feature engineering called quant. The quants did that too. Now, supercomputers
## 00:34:17
**中文**
自行研究海量数据、发现洞察和模式，迎来深度学习与 Transformer 时刻。医疗也正迎来 ChatGPT 时刻：AI 物理或 AI 生物用于药物发现，AI 智能体用于客户服务和辅助诊断，物理 AI 则用于机器人系统。
**English / Original**
studying massive amounts of data, discovering insight and discovering patterns by itself. And so this is going through its deep learning and its transformer moment. Healthcare is going is going through their ChatGPT moment. Some really exciting work that we're there we have we have a great keynote track here. We have a great keynote track, Kimberly, a great keynote track. For healthcare, we're talking about AI physics or AI biology for drug discovery, AI agents for customer service and support of diagnosis, diagnosis, and of course, physical AI, robotic systems. All
## 00:34:56
**中文**
这些不同 AI 方向都对应 NVIDIA 提供的不同平台。
**English / Original**
these different vectors of AI have different platforms that NVIDIA provides industrial.
## 00:35:02
**中文**
工业领域也一样。我们正全面重启，展开人类历史上最大规模的建设：全球多数行业都在建设 AI 工厂、芯片厂和计算机工厂。媒体娱乐和游戏将用实时 AI 做翻译、直播支持、实时游戏与视频，大量内容都会由 AI 增强。量子领域有 Holoscan 平台，35 家公司正与我们共建下一代量子—GPU 混合系统。
**English / Original**
We are completely resetting and starting the largest build out of human history and most of the world's industries. Building AI factories. Building chip plants, building computer plants are represented here today. Media and entertainment. Gaming, of course. Real time AI platform so that we could translation and broadcast support and live live games and live video. Enormous amount of it will be augmented with AI. We have a we have a platform called Holoscan quantum. There are 35 different companies here building with us the next generation of quantum GPU hybrid systems. Retail
## 00:35:45
**中文**
零售与消费品行业用 NVIDIA 优化供应链、创建生成式购物系统和客服智能体。机器人是 35 万亿美元产业，制造业是 50 万亿美元产业。NVIDIA 十年来一直构建机器人所需的三类基础计算机，并与几乎所有已知机器人公司合作。
**English / Original**
and CPG using NVIDIA for supply chain using. Creating a genetic shopping systems. AI agents for customer support. A lot of work being done here. $35 trillion industry robotics, $50 trillion industry in manufacturing. NVIDIA has been working in this area for a decade now, building three computers, the fundamental computers necessary to build robotic systems. We are integrated with working with literally every single company that we know of building robots.
## 00:36:18
**中文**
展会有 110 台机器人。电信业规模约 2 万亿美元，与全球 IT 业相当。遍布各地的基站是上一代计算基础设施，但它将被彻底重塑：从单一功能基站变成边缘 AI 基础设施平台。我们的平台名为 Aerial。
**English / Original**
We have 110 robots here at the show. And then telecommunications, about as large as the world's IT industry, about $2 trillion. We see. Of course, base stations everywhere. It's one of the world's infrastructures. It was the infrastructure of the last generation of computing. That infrastructure is going to get completely reinvented. And the reason for that is very simple. That base station, which is. It does one thing, which is base station. It's going to be an AI infrastructure platform in the future. AI will run at the edge. And so lots of, lots of great, um, great, uh, discussion there. And our platform there is called Ariel. Our AI
## 00:37:03
**中文**
我们在 AI-RAN 上与 Nokia、T-Mobile 等深度合作。业务核心不仅是计算平台，更是 CUDA 库——NVIDIA 发明的算法。我们是一家算法公司，因此能深入每个行业，设想未来，让顶尖计算机科学家描述、求解、重构问题并把成果封装成库。
**English / Original**
ran big partnership with Nokia. Big partnership with T-Mobile and many others. At the core of our business, everything that I just mentioned, computing platforms, but very importantly, our CUDA libraries, our CUDA libraries is the algorithm, the algorithms that NVIDIA invents. We are an algorithm company. That's what makes us special. That's what that's what makes it possible for me to be able to go into every single one of these industries, imagine the future and have the world's best computer scientists describe and solve problems, refactor it, re-express it, and turn it into a library. We have so many. I think we have.
## 00:37:46
**中文**
本届大会宣布约 70 至 100 个库和约 40 个模型，而它们还在持续更新。库是公司的皇冠明珠，让计算平台真正解决问题、产生影响。最重要的库之一 cuDNN（CUDA 深度神经网络）彻底革新 AI，引发现代 AI 大爆炸。
**English / Original**
At this show, we are announcing 100 100 libraries, 70 libraries, maybe 40 models, and that's just at the show. We're updating these all the time. We're updating them all the time. The libraries is the crown jewels of our company. It is what makes it possible for that platform, the computing platform, to be activated in service of solving a problem making impact. One of the biggest, one of the most important libraries that we ever created, cuDNN CUDA deep neural networks, it completely revolutionized artificial intelligence, caused a big bang of modern AI.
## 00:38:26
**中文**
请看 CUDA-X 短片。20 年前我们构建统一加速计算架构 CUDA；如今上千个 CUDA-X 库帮助各科学工程领域突破：决策优化 cuOpt、计算光刻 cuLitho、直接稀疏求解器 cuDSS、几何感知神经网络 cuEquivariance，以及 AI-RAN 的 Aerial。
**English / Original**
Let me show you a short video about CUDA-X. ________________ [VIDEO NARRATION - JENSEN] 20 years ago we built CUDA, a single architecture for accelerated computing. Today we've reinvented computing a thousand CUDA-X libraries helped developers make breakthroughs in every field of science and engineering. cuOpt opt for decision optimization. cuLitho for computational lithography, cuDSS for direct sparse solvers to cuEquivariance for geometry aware neural networks. Aerial for AI
## 00:39:09
**中文**
还有可微物理 Warp、基因组学 Parabricks。它们的根基都是算法。
**English / Original**
ran. Warp for differentiable physics. Parabricks for genomics. At their foundation are algorithms.
## 00:39:21
**中文**
而这些算法非常美。
**English / Original**
And they are beautiful. [JENSEN]
## 00:41:56
**中文**
刚才看到的一切都是模拟：有第一性原理解算器、AI 替代模型、AI 物理模型和物理 AI 机器人模型。没有动画或预设动作，全是完整模拟。这正是 NVIDIA 的根本工作：把算法理解与计算平台连接，解锁机会。NVIDIA 是纵向整合的计算公司，
**English / Original**
Everything you saw was a simulation. Some of it was principal solvers, fundamental physics solvers. Some of it was AI surrogates, AI physical models, and some of it was physical AI robotics models. Everything was simulated. Nothing was animated. Nothing was articulated. Everything was completely simulated. That is what fundamentally NVIDIA does. It is through the connection of understanding of the algorithms with our computing platforms that we're able to open up to unlock these opportunities. NVIDIA is a vertically integrated computing
## 00:42:39
**中文**
同时横向开放地与世界集成。这就是 CUDA-X。视频中有 Walmart、L'Oréal、JPMorgan、Roche、Toyota 等塑造当今社会的全球巨头。
**English / Original**
company with open horizontal integration with the world. So that's CUDA x. Well, just now you saw a whole bunch of companies. You saw Walmart. And you know there's L'Oreal and incredible companies, established companies JP Morgan and Roche. And these are companies in companies that have defined society to today. Toyota is here. These are some of the largest companies in the world.
## 00:43:09
**中文**
也有大量大家从未听过的小公司，我们称之为 AI 原生企业。名单极长，这里只是极小部分；我无法决定展示多少，索性缩到谁也看不清，免得伤感情。名单里有 OpenAI、Anthropic 等新公司，也有大量其他企业，
**English / Original**
________________ It is also true that there's a whole bunch of companies you've never heard of. These are companies. We call them AI natives. A whole bunch of small companies. It's the list is gigantic. I couldn't this is just a little tiny, tiny bit of it. And, um, I couldn't decide whether to show you more or show you less. And so I made it so that you couldn't see any. And nobody's feelings are hurt. However, inside this list are a bunch of brand new companies. They're companies. Like, for example, you might have heard a couple of them, OpenAI, Anthropic, but there's a whole bunch of others. There's a whole bunch of others and they
## 00:43:50
**中文**
分别服务不同垂直行业。过去两年、尤其去年，发生了重大变化。
**English / Original**
serve different verticals. Something happened in the last two years, particularly this last year.
## 00:43:57
**中文**
我们长期与 AI 原生公司合作，去年数量突然飙升。AI 初创企业获得 1500 亿美元风险投资，创人类历史之最；单笔投资也从数百万、数千万跃升至数亿乃至数十亿美元。
**English / Original**
We've been working with the AI natives for a long time, and this last year it just skyrocketed. I'll explain to you why it happened. This this industry has skyrocketed $150 billion of investment into venture investment into startups, the largest in human history. This is also the first time that the scale of the investments went from millions of dollars, tens of millions of dollars to hundreds of millions of dollars and billions of dollars.
## 00:44:26
**中文**
原因是这些公司首次全部需要海量算力和 token。它们要么自行创造、生成 token，要么在 Anthropic、OpenAI 等生成的 token 上集成并增加价值。这个产业在许多方面都与以往不同。
**English / Original**
And the reason for that is this is the first time in history that every single one of these companies needs compute, and lots and lots of it. They need tokens, lots and lots of it. They're either they're either going to create and build and create tokens and generate tokens, or they're going to Integrate. Add value to tokens that are available created by Anthropic and OpenAI and others. And so this industry is different in so many different ways.
## 00:44:59
**中文**
但其影响和已经交付的价值清晰可见。计算被重新发明，催生 AI 原生企业，正如 PC、互联网、移动与云革命都曾创造一批新公司，而每一代都有自己的标准。
**English / Original**
But the one thing that is very clear, the impact that they're making this, the incredible value that they're delivering already is quite tangible. AI natives, all because we reinvented computing, just like during the PC revolution, a whole bunch of new companies were created just as just as during the internet revolution, a whole bunch of companies were created and mobile cloud, a whole bunch of companies were created. Each one of them had their own standards.
## 00:45:26
**中文**
这一代同样会诞生许多特殊且影响深远的公司，类似上次平台迁移催生的 Google、Amazon、Meta。我们正处于新平台迁移的起点。为什么偏偏是最近两年？我们长期推动深度学习和 AI，
**English / Original**
And we're talking about one of the major standards that just happened. Incredibly important. And this generation, we also have our own large number of very, very special companies. We reinvented computing. It stands to reason there's going to be a whole new crop of really important companies Consequential companies for the future of the world the Googles, the Amazons, the Meta's consequential companies that have come as a result of the last computing platform shift. We are now at the beginning of a new platform shift. But what happened in the last couple of years? Well, we've been watching as you know, we've been working on deep learning and working on AI.
## 00:46:08
**中文**
也亲历现代 AI 大爆炸。最近两年出现三件事。第一，ChatGPT 开启生成式 AI 时代：不仅能感知理解，还能翻译和生成独特内容。刚才图形学与生成式 AI 的融合就让图形活了起来。每个人都应使用 ChatGPT，我每天早晨都用。
**English / Original**
The big bang of modern AI. We were right there at the spot, and we've been advancing this field for quite some time. But why the last two years? What happened in the last two years? Well, three things. ________________ ChatGPT, of course, started the generative AI era. It's able to not just understand, perceive and understand. It's able to also translate and generate generation of unique content. I showed you the fusion of generative AI with computer graphics and brought computer graphics to life. You guys just everybody in the world should be using ChatGPT. I know I use it every single morning. Used it plenty this morning. And so ChatGPT was
## 00:46:49
**中文**
生成式计算与过去的检索式计算完全不同。生成 AI 不只是软件能力，还深刻改变计算方式。牢记这一点，就会理解为什么计算机架构、供给和建设方式乃至“计算”的定义都会变化。
**English / Original**
the generative AI, the era, the second. By the way, generative generative computing versus the way we used to do computing. It's not. It's generative. AI is a capability of software, but it has profoundly changed how computing is done. Computing used to be retrieval based. Now it's generative. Keep that thought in mind when I talk about certain things, and you'll realize why it is that everything that we do is going to change how computers are architected, how computers are provided, how computers are going to be built out, and what is the meaning of computing altogether.
## 00:47:28
**中文**
生成式 AI 始于 2022 年末至 2023 年；随后 O1、O3 带来推理 AI，使模型能反思、自我思考、规划并把无法理解的问题分解成可理解步骤，还能以研究资料为依据。O1 让生成 AI 更可信、更有事实根据，推动 ChatGPT 爆发。
**English / Original**
Generative AI 2023. End of 22, 2023. The next reasoning AI O1, which and then took off with O3. Reasoning allowed it to reflect, allows it to think to itself, allows it to plan, break down, break down problems, and decompose a problem it couldn't understand into steps or parts that it could understand. It could ground itself on research O1 made generative AI trustworthy and grounded on truth that caused ChatGPT to simply took off. And that was a very,
## 00:48:09
**中文**
这是重大时刻。O1 模型本身只稍大，但用于上下文的输入 token 和思考的输出 token 让计算量激增。随后 Claude Code 成为首个智能体模型，能读取代码文件、编译、测试、评估并迭代。
**English / Original**
very big moment. The amount of input tokens that was necessary in order to produce, and the amount of output tokens it generated in order to reason the model was a little bit larger. It, you know, of course, you could have much larger models. The model O1 was a little bit larger, not much larger, but its input token usage for context and its output token for thinking increased the amount of computation tremendously. Then came Claude Code, the first Agentic model, it was able to read files code, compile it, test it, evaluate it, go back and iterate on it. Claude
## 00:48:50
**中文**
Claude Code 革新软件工程。NVIDIA 全公司都在组合使用 Claude Code、Codex 和 Cursor，今天没有软件工程师不借助一个或多个 AI 编程智能体。
**English / Original**
Code has revolutionized software engineering. As all of you know, 100% of NVIDIA is using a combination of or oftentimes all three of them Claude Code, Codex and cursor all over NVIDIA. There's not one software engineer today who is not assisted by one or many AI agents helping them code. Claude Code completely revolutionizes the the new inflection and for the first time,
## 00:49:20
**中文**
这带来新的拐点：你不再问 AI 是什么、在哪里、何时或如何，而是要求它创建、执行、构建，并调用工具。
**English / Original**
you don't ask the AI what, where, when, how you ask it. Create do build. You ask it to use tools.
## 00:49:37
**中文**
它会接收上下文、读取文件、分解问题、推理和反思，真正解决问题并执行任务。AI 从感知发展到生成，再到推理，如今终于能够完成富有生产力的工作。
**English / Original**
Take your context, read files. It's able to break down a problem. Reason about it. Reflect on it. It's able to solve problems and actually perform tasks. An AI that was able to perceive became an AI that could generate an AI that could generate, became an AI that could reason, an AI that could reason now became an AI that can actually do work, very productive work.
## 00:50:04
**中文**
过去两年计算需求急剧上升，GPU 现货价格飙升、一卡难求；即便我们大量出货，需求仍持续增长。根本原因就是这个拐点：AI 终于能做生产性工作，推理的拐点已经到来。
**English / Original**
The amount of computation in the last two years, we know that everybody in this room knows the computing demand for NVIDIA GPUs off the charts. Spot pricing is skyrocketing. You couldn't find a GPU if you tried. And yet in the meantime, we're shipping GPUs out incredible amounts of it, and demand just keeps on going up. There's a reason for that, this fundamental inflection. Finally, AI is able to do productive work and therefore the inflection point of inference has arrived.
## 00:50:43
**中文**
AI 要思考、做事、阅读和推理，都必须执行推理计算。它每次思考、推理、行动和生成 token 都在推理，重心早已不止训练。
**English / Original**
AI now has to think in order to think, it has to inference. AI now has to do. In order to do it has to inference. AI has to read. In order to do so. It has to inference. It has to reason. It has inference. Every part of AI, every time. It has to think, it has to reason, it has to do. It has to generate tokens. It has to inference. It's way past training.
## 00:51:09
**中文**
如今进入推理时代，所需 token 与计算量约增加一万倍；与此同时，使用量可能又增加一百倍。
**English / Original**
Now it's in the in the field of inference. So the the inference inflection has arrived. At the time when the amount of tokens, the amount of compute necessary increased by roughly 10,000 times. Now, when I combine these two, the fact that since in the last two years the computing demand, computing demand of the work has gone up by 10,000 times, and the amount of usage, the amount of usage has probably gone up by 100 times.
## 00:51:44
**中文**
所以我认为过去两年计算需求增长了一百万倍。这是所有初创公司、OpenAI 和 Anthropic 的共同感受：只要获得更多容量，就能生成更多 token、增加收入并服务更多用户。
**English / Original**
People have heard me say, I believe that computing demand has increased by 1 million times in the last two years. It is the feeling that we all have. It is the feeling every startup has. It's the feeling that OpenAI has. It's the feeling that Anthropic has. If they could just get more capacity, they could generate more tokens. Their revenues would go up. More people could use it.
## 00:52:08
**中文**
AI 也能变得更先进、更聪明，形成正向飞轮。去年此时我说，截至 2026 年，Blackwell 与 Rubin 已有约 5000 亿美元的高确定性需求和采购订单。
**English / Original**
The more advanced, the smarter the AI could become. We are now at that positive flywheel system. We have. We have reached that moment. The inflection, the inference inflection has arrived. Last year at this time, I said. That where I stood at that moment in time, we saw about $500 billion. We saw $500 billion of very high confidence demand and purchase orders for Blackwell and
## 00:52:51
**中文**
5000 亿美元是惊人的收入，但大家似乎不为所动——因为各位也都创下纪录。现在，在短短几个月之后、距离上届 GTC 一年，
**English / Original**
Rubin through 2026. I said that last year. Now, I don't know if you guys feel the same way, but $500 billion is an enormous amount of revenue. Not one impressed. I know why you're not impressed because all of you had record years. Well, I'm here to tell you that right now where
## 00:53:30
**中文**
我站在同一个地方，
**English / Original**
I stand a few short months after GT, CDC. One year after last GTC right here where I stand.
## 00:53:42
**中文**
看到截至 2027 年至少 1 万亿美元的需求。为什么合理？余下时间将解释。事实上供应仍会短缺，计算需求肯定更高。首先，过去一年我们做了很多工作。
**English / Original**
I see through 2027. At least $1 trillion. ________________ Now. Does it make any sense? And that's what I'm going to spend the rest of the time talking about. In fact, we are going to be short. I am certain computing demand will be much higher than that. And there's a reason for that. So the first thing is we did a lot of work in the last year.
## 00:54:21
**中文**
2025 年是 NVIDIA 的“推理之年”。我们不仅要擅长训练和后训练，还要在 AI 每个阶段都做到极致，使基础设施投资可长期扩展、使用寿命更长、成本更低。使用越久成本越低；我确信 NVIDIA 系统是全球成本最低的 AI 基础设施。
**English / Original**
Of course, as you know, 2025 was NVIDIA's year of inference. We wanted to make sure that not only were we good at training and post training that we were incredibly good at every single phase of AI. So that the investments that were made, investments made in our infrastructure could scale out for as long as they would like to use it. And the useful life of NVIDIA's infrastructure would be long, and therefore the cost would be incredibly low. The longer you could use it, the lower the cost. There's no question in my mind, NVIDIA systems are the lowest cost infrastructure you could get for AI infrastructure in the world. And so the first part was last year
## 00:55:00
**中文**
去年首先聚焦推理 AI并推动拐点。同时 Anthropic 和 Meta 的开放模型选择 NVIDIA；它们合计代表全球约三分之一 AI 算力。开源模型已接近前沿并无处不在，而 NVIDIA 是全球唯一能够运行
**English / Original**
was all about AI for inference. And it drove this inflection point. Simultaneously, we were very pleased last year that Anthropic has come to NVIDIA, that MSL Meta SL has chosen NVIDIA. And meanwhile, meanwhile, and as a collection as a group, this represents one third of the world's AI compute. open source models. Open source models have reached near the frontier and it is literally everywhere. And NVIDIA, as you know today were the only platform in the world today that runs
## 00:55:38
**中文**
所有 AI 领域与模型的平台，覆盖语言、生物、图形、视觉、语音、蛋白质、化学、机器人、边缘和云端以及各种语言。NVIDIA 架构可通用于所有这些负载，并保持卓越表现，因此成为成本最低、确定性最高的平台。面对 1 万亿美元基础设施投资，必须确信系统会被充分利用、性能优异且长期有效。
**English / Original**
every single domain of AI across every single one of these AI models in language and biology and computer graphics, computer vision and speech. Proteins and chemicals, robotics and otherwise edge or cloud, any language. NVIDIA's architecture is fungible for all of that, and we're incredible for all of that. That allows us to be the lowest cost, the highest confidence platform. Because when you're building these systems, as I mentioned, $1 trillion is an enormous amount of infrastructure. You have to have complete confidence that the
## 00:56:20
**中文**
在 NVIDIA 上进行这类投资可以完全放心。我们已证明，这是全球唯一能在任何地方可靠建设的基础设施：无论放入哪朵云、部署在本地，还是落地任何国家，我们都乐于支持。
**English / Original**
trillion dollars you're putting down will be utilized, would be performant, would be incredibly cost effective, and have useful life for as long as you could see that infrastructure investment you could make on NVIDIA. You could make with complete confidence. We have now proven that it is the only infrastructure in the world that you could go anywhere in the world and build with complete confidence. You want to put it in any of the clouds. We're delighted by that. You want to put it on prem. We're happy about that. You want to put it in any country, anywhere. We're delighted to support you. We are now a computing platform that runs
## 00:57:01
**中文**
我们已成为运行全部 AI 的计算平台。前五大超大规模云厂商贡献业务的 60%，其中一部分用于内部 AI，例如推荐系统正从表格、协同过滤和内容过滤转向深度学习与大语言模型。
**English / Original**
all of AI. Now, our business already starting to show that 60% of our business is hyperscalers, the top five hyperscalers. However, even within that top five hyperscalers, some of it is internal AI consumption. The internal AI consumption. Really important work, like recsys is moving from recommender systems of tables and collaborative filtering and content filtering.
## 00:57:29
**中文**
搜索等负载也在转向深度学习和 LLM，几乎所有超大规模工作负载都迁移到 NVIDIA GPU 擅长的形式。我们与每家 AI 实验室合作、加速所有模型，并把庞大的 AI 原生生态带到云端，因此无论投入多少算力都会很快被消耗。
**English / Original**
It's moving towards deep learning and large language models. Search. Moving to deep learning. Large language models. Almost all of these different hyperscale workloads are now moving, shifting towards a workload that NVIDIA GPUs are incredibly good at. But on top of that, because we work with every AI lab, because we work with every AI, we accelerate every AI model. And because we have a large ecosystem of AI natives that we work with that we can bring to the clouds. That investment, no matter how large, no matter how quick that compute will be consumed.
## 00:58:05
**中文**
这占业务 60%；另 40% 遍布区域云、主权云、企业、工业、机器人、边缘、超级计算机和各种服务器。AI 的多样性与广泛覆盖就是韧性。它不是单一应用技术，而是全新的基础计算平台迁移。
**English / Original**
And that represents 60% of our business. The other 40% is just everywhere. Regional clouds. Sovereign clouds. Enterprise. Industrial. Robotics. Edge. Big systems. Supercomputing systems. Small servers. Enterprise servers. The number of systems. Incredible. The diversity of AI is also its resilience. The span of reach of AI is its resilience. There is no question this is not a one app technology. This is now fundamental. This is absolutely a new computing platform
## 00:58:46
**中文**
我们的职责是继续推进技术。去年是推理之年，我们在 Hopper 如日中天时冒险重构，决定把 Hopper 与八路 NVLink 架构提升到下一层级。
**English / Original**
shift. Well, our job is to continue to advance the technology. And one of the most important things that I mentioned last year was last year was our year of inference. We dedicated everything. We took a giant chance and reinvented while Harper was at its prime and it was just cooking, we decided that the Hopper architecture, the NVLink by eight, had to be taken to the next level.
## 00:59:16
**中文**
我们彻底重构并解耦系统，创造 NVLink 72，制造与编程方式全面改变。Grace Blackwell NVLink 72 是艰难的巨大赌注，感谢所有伙伴。它还引入 NVFP4，不只是 FP4 精度，
**English / Original**
We completely re-architected the system, disaggregated to the computing system altogether and created NVLink 72. The way that it's built, the way it's manufactured, the way it's programmed, completely changed. Grace Blackwell NVLink 72 was a giant bet, and it wasn't easy for anybody. And many of my partners here in the room. I want to thank all of you for the hard work that you guys did. Thank you. NVLink 72 NVFP4 for not just FP4 precision.
## 00:59:50
**中文**
而是全新 Tensor Core 和计算单元。实验证明 NVFP4 推理不损精度，却大幅提高性能和能效，也可用于训练。再加 Dynamo、TensorRT-LLM 和大量新算法，以及投入数十亿美元的 DGX Cloud 超级计算机来优化内核与完整软件栈，最终让高效推理成为可能。
**English / Original**
FP4 is a whole different type of tensor core and computational unit. We've demonstrated now that we can inference NVFP4 for without loss of precision, but gigantic boost in performance and energy efficiency. We've also been able to use NVFP4 for training. So NVLink 72 NVFP4 the invention of Dynamo TensorRT-LLM, a whole bunch of new algorithms. We even built a supercomputer to help us optimize kernels and help us optimize our complete stack. We call it DGX cloud. We invested billions of dollars of supercomputing capability help us create the kernels, the software that made inference possible.
## 01:00:34
**中文**
有人曾说推理很容易，其实推理极难，也极重要，因为它驱动收入。SemiAnalysis 完成了史上最大、最全面的 AI 推理测试。纵轴是每瓦 token 数；每个数据中心或工厂都受电力限制，
**English / Original**
Well. The results all came together and people told. People used to tell me. But, Jensen, inference is so easy. Inference is the ultimate hard. Inference is ultimate hard. It is also ultimate important because it drives your revenues. ________________ And so this is the outcome. This is from SemiAnalysis. This is the largest, most comprehensive sweep of AI that has AI inference that has ever been done. And what you see here on the left, on this side, on this side is tokens per watt tokens per watt is important because every data center, every single factory, by definition,
## 01:01:16
**中文**
一座 1 吉瓦工厂不会凭空变成 2 吉瓦。因此要在固定电力下最大化工厂产品——token。横轴是交互性，即每次推理的速度；推理越快，响应越快，更重要的是，
**English / Original**
is power constrained. A one gigawatt factory will never become two. It's physically constrained. The laws of atoms, the laws of physicality. And so that one gigawatt of data center. You want to drive the maximum number of tokens, which is the production, the product of that factory. So you want that. You want to be on top of that curve as high as you want this. The x axis is the interactivity, the speed of inference, the speed of each inference. The faster you can inference, the faster you could of course, respond. But very importantly, the faster you can influence.
## 01:01:57
**中文**
可以运行更大模型、处理更多上下文并思考更多 token。横轴也代表 AI 智能程度，纵轴代表吞吐量。AI 越聪明，通常吞吐越低，因为思考更久。这个速度维度非常重要，稍后还会回来讲。
**English / Original**
The larger the models, the more context you can process, the more tokens you can think through. This axis is the same as smartness of the AI, and so this is the throughput of the AI. This is the smartness of the AI. I notice the smarter the AI, the lower your throughput. Makes sense. You're thinking longer. Okay. And so this axis is the speed. And I'm going to come back to this. This is important. This is where I torture all of you. But it's too important.
## 01:02:29
**中文**
从今以后，全球每位 CEO 都会用我接下来描述的方式研究业务，因为这就是你的 token 工厂、AI 工厂和收入。吞吐量代表固定数据中心功率下每瓦可获得的智能。
**English / Original**
Every CEO in the world you watch, every CEO in the world will study their business from now on in the way I'm about to describe, because this is your token factory. This is your AI factory. This is your revenues. There's no question about that going forward. And so this is the throughput. This is the intelligence better per per watt for a given power of data center.
## 01:02:54
**中文**
吞吐越高，生产 token 越多，另一侧体现成本。NVIDIA 性能全球最高并不意外，意外的是一代产品实现 35 倍提升，而摩尔定律只会让 Hopper 的后继者提高约 1.5 倍。
**English / Original**
The more throughput, the more tokens you can produce on this side is cost. Notice NVIDIA is the highest performance in the world. Nobody would be surprised by that. They would be surprised by the fact that in one generation. whereas Moore's Law would have given us through transistors 50%. Two times. Moore's law would probably give us one and a half times more performance. You would have expected from Hopper, age 201 and a half times higher. Nobody would have expected 35 times higher. I said last year at this time that NVIDIA's Grace
## 01:03:37
**中文**
去年我说 Grace Blackwell NVLink 72 每瓦性能提高 35 倍，没人相信。SemiAnalysis 的 Dylan Patel 后来说我故意保守，实际是 50 倍；他没说错。因此我们的单 token 成本全球最低，几乎无法超越。
**English / Original**
Blackwell NVLink 72 was 35 times perf per watt. Nobody believed me. And then SemiAnalysis came out and Dylan Patel had a quote. He accused me of sandbagging. He accused me of sandbagging. He says Jensen sandbagged. It's actually 50 times. And he's not wrong. He's not wrong. And so our cost per token. Yeah. Our cost per token is the lowest in the world. You can beat it. I've said before,
## 01:04:19
**中文**
我说过，架构错了，即使芯片免费也不够便宜，因为仍要建设 1 吉瓦数据中心；按 15 年摊销，即使内部什么都不装也约需 400 亿美元。必须装入最好的计算系统，获得最低 token 成本。NVIDIA 的 token 成本目前几乎不可触及，原因是极致协同设计。
**English / Original**
if you have the wrong architecture, even if it's free, it's not cheap enough. And the reason for that is because no matter what happens, you still have to build a gigawatt data center. You still have to build a gigawatt factory. And that gigawatt factory for 15 years amortized across that gigawatt factory is about $40 billion. Even when you put nothing on it, it's $40 billion in. You better make for darn sure you put the best computer system on that thing so that you could have the best token cost. NVIDIA's token cost is world class, basically untouchable at the moment. And the reason that's true is because of extreme
## 01:04:59
**中文**
我很高兴他称我们为“Token King”。如前所述，我们纵向整合所有软件与技术，同时横向开放。
**English / Original**
co-design. And so I'm very happy that he named us. There was a monkey King, token king. Well, we take we take all of our softwares, as I told you. We vertically integrate, but we horizontally open where vertical integration horizontal open. We integrate all of our software and all of our
## 01:05:40
**中文**
我们把完整技术打包并集成进全球推理服务商。Fireworks 等公司增长极快，去年增长达百倍。它们就是 token 工厂，性能和单位 token 生产成本决定一切。
**English / Original**
technology. However, we could package it up and integrate it into the world's inference service providers. And these these companies are growing so fast. They're growing so fast. Fireworks. Lin is here. Together. They're just growing so incredibly fast. 100 times in the last year. They are token factories. And the effectiveness, the performance and the token cost production capability for their factories is everything to them.
## 01:06:11
**中文**
同一系统更新 NVIDIA 软件、算法和技术后，平均速度从每秒约 700 token 升到近 5000，提升七倍，体现极致协同设计的力量。
**English / Original**
________________ And this is what happened. This is we updated their software same system. And notice their token speeds. Incredible. The difference. Before before NVIDIA updated everything and all of our algorithms and software and all the technology that we bring to bear about 700 tokens per second average went to nearly 5007 times higher. And so this is the incredible power of extreme co-design.
## 01:06:46
**中文**
数据中心过去是存放文件的地方，如今是生成 token 的工厂。土地、电力和建筑外壳都有限，一旦建成便受功率约束。
**English / Original**
________________ I mentioned earlier the importance of factories. This is the importance of factory your data center. It used to be a data center for files. It's now a factory to generate tokens. Your factory is limited no matter what. Everybody is looking for land, power and shell. Once you build it, you are power limited within that power.
## 01:07:06
**中文**
在有限基础设施中，推理是工作负载、token 是新商品、算力就是收入，因此必须把架构优化到极致。未来每家云、计算机、AI 公司乃至所有企业都会关注 token 工厂效率。
**English / Original**
Limited infrastructure. You better make for darn sure that your inference, because, you know, inference is your workload and tokens is your new commodity. That compute is your revenues, that you want to make sure that the architecture is as optimized as you can in the future. Every single CSP, every single computer company, every single cloud company, every single AI company, every single company period, are going to be thinking about their token factory effectiveness.
## 01:07:39
**中文**
这就是未来的工厂，因为每个人都依靠智能，未来智能会由 token 增强。2016 年 4 月 6 日，我们推出全球首台深度学习专用计算机 DGX-1：八块 Pascal GPU 通过第一代 NVLink 相连，单机 170 teraflops，
**English / Original**
This is your factory in the future. And the reason why I know that is because everybody in this room is powered by intelligence. And in the future, that intelligence will be augmented by tokens. So let me show you how we got here. ________________ [VIDEO NARRATION - JENSEN] On April 6th, 2016, a decade ago, we introduced DGX one, the world's first computer designed for deep learning. Eight Pascal GPUs connected with the first generation NVLink 170 teraflops in one computer, the world's
## 01:08:16
**中文**
面向 AI 研究者。Volta 带来 NVLink Switch，让 16 块 GPU 全互联并像一块巨型 GPU 工作。模型继续增大，数据中心必须成为单一计算单元，于是 Mellanox 加入 NVIDIA。2020 年 DGX A100 SuperPOD 成为首个兼具纵向与横向扩展的 GPU 超级计算机，纵向用 NVLink 3，横向用 ConnectX-6 和 Quantum InfiniBand。
**English / Original**
first computer designed for AI researchers. With Volta. We introduced NVLink switch 16 GPUs connected with full all to all bandwidth, operating as one giant GPU, a giant step forward, but model sizes continue to grow. The data center needed to become a single unit of computing, so Mellanox joined NVIDIA. In 2020, DGX A100 Superpod became the first GPU supercomputer combining scale up and scale out architecture. NVLink 3 for scale Up, ConnectX 6, and Quantum
## 01:08:59
**中文**
随后 Hopper 首次加入 FP8 Transformer Engine，开启生成式 AI 时代，并配备 NVLink 4、ConnectX-7、BlueField-3 DPU 和第二代 Quantum InfiniBand。Blackwell 再以 NVLink 72 重塑 AI 超算：72 块 GPU 提供每秒 130 TB 全互联带宽，
**English / Original**
InfiniBand for scale out. Then Hopper, the first GPU with the Fp8 transformer engine that launched the generative AI era NVLink for ConnectX 7 Bluefield three DPUs second generation quantum InfiniBand. It revolutionized computing. Blackwell redefined AI supercomputing system architecture with NVLink 7272 GPUs connected by NVLink spun 130TB per second of all, to all bandwidth compute,
## 01:09:40
**中文**
整合 Blackwell GPU、Grace CPU、ConnectX-8 与 BlueField-3，横向扩展采用 Spectrum-X 以太网。在预训练、后训练、推理和智能体系统三大扩展规律推动下，需求指数增长。Vera Rubin 为智能体 AI 各阶段设计，推进 CPU、存储、网络与安全，NVLink 72 提供 3.6
**English / Original**
trace, integrate Blackwell GPUs, Grace CPUs ConnectX 8 and BlueField-3. Scale out runs over Spectrum for Ethernet with three scaling laws in full steam pre-training, post training and inference, and now Agentic systems compute, demand continues to grow exponentially. And now Vera Rubin architected for every phase of a genetic AI, advancing every pillar of computing, including CPU storage, networking, and security. Vera Rubin, NVLink 72, 3.6.
## 01:10:22
**中文**
exaflops 算力和每秒 260 TB 全互联带宽。Vera CPU 机架负责编排和智能体工作流，BlueField 构建 AI 原生存储机架，Spectrum-X 共封装光学实现横向扩展、提高能效与韧性；新增 Groq 3 LPX 机架与 Vera Rubin 紧密连接，以 LPU 的大容量片上 SRAM 加速 token。
**English / Original**
Exaflops of compute 260TB per second of all to all NVLink bandwidth. The engine supercharging the era of Agentic AI, the Vera CPU rack designed for orchestration and agentic workflows. The Stat rack AI native storage built with Bluefield for scale out with Spectrum-X co-packaged optics, increasing energy efficiency and resiliency, and an incredible new addition, the Groq 3 LPX rack, tightly connected to Vera Rubin Groqs LPU's massive on chip SRAM, a token accelerator to the
## 01:11:02
**中文**
二者结合后，每兆瓦吞吐量提升 35 倍。
**English / Original**
already incredibly fast Vera Rubin together 35 times more throughput per megawatt.
## 01:11:09
**中文**
新 Vera Rubin 平台由七种芯片、五台机架级计算机组成，构成一台面向智能体 AI 的革命性超级计算机；十年间算力增长 4000 万倍。过去我说 Hopper 时会举起一块芯片，现在看来真可爱。
**English / Original**
The new Vera Rubin platform seven chips, five rack scale computers, one revolutionary AI supercomputer for agentic AI 40 million times more compute in just ten years. [JENSEN] Now, in the. In the good old days, when I would say Hopper, I would hold up a chip. That's just adorable.
## 01:11:48
**中文**
Vera Rubin 指的是整套系统：纵向完整集成、软件端到端优化，作为一个巨型系统运行。它专为智能体设计，因为智能体最重要的负载是思考；LLM 越来越大，需要更快生成更多 token，也必须频繁访问内存。
**English / Original**
This is Vera Rubin. When we think. When we when we think Vera Rubin, we think the entire system vertically integrated completely with software extended end to end optimized as one giant system. The reason why it's designed for Agentic systems is very clear, because agents. Of course, the most important workload is its thinking. The large language model, the large language models are getting larger and larger and larger. It's going to generate more and more tokens more quickly, so it can think more quickly. But it also has to access memory. It's going to pound
## 01:12:30
**中文**
它会重度访问 KV cache、结构化与非结构化数据、cuVS 和存储，因此我们重构存储系统。智能体还会调用工具，而 AI 希望浏览器、未来云端虚拟 PC 等工具尽可能快。
**English / Original**
on memory really hard. KB cache structured data, unstructured data, CVS. It's going to be pounding on the on the storage system really, really hard, which is the reason why we reinvented the storage system. It is also going to use tools and unlike humans that are more tolerant to slower computers, AI wants the tools to be as fast as possible. These tools. Web browsers. In the future, they could also be virtual PCs in the cloud. Those PCs have to be. And those computers have to be as
## 01:13:09
**中文**
因此我们设计全新 CPU，提供极高单线程性能、数据吞吐、数据处理能力与能效。它是全球唯一使用 LPDDR5 的数据中心 CPU，单线程性能和每瓦性能都非常出色。
**English / Original**
fast as possible. We created a brand new CPU, a brand new CPU that's designed for extremely high single threaded performance, incredibly high data output, incredibly good at data processing and extreme energy efficiency. It is the only data center CPU in the world that uses LPDDR5, LPDDR5, and incredible single threaded performance and performance per watt.
## 01:13:43
**中文**
这让它能与其他机架共同处理智能体工作负载。这就是 Vera Rubin 系统：100% 液冷，线缆全部消失，安装时间从两天降到两小时，显著缩短制造周期。它还是一台由
**English / Original**
That is unrivaled. And so that's why we built that, so that it could go along with the rest of these racks for a genetic processing. And so here it is. This is the Grace black wall. No. Vera Rubin, where is it? Here it is. Okay, so this is the Vera Rubin system. Notice, since the last time 100% liquid cooled, all of the cables gone. What used to take what used to take two days to install now takes two hours. Incredible. And so the manufacturing cycle time is going to dramatically reduce. This is also a supercomputer that is cooled by. It's cooled by
## 01:14:27
**中文**
45 摄氏度热水冷却的超级计算机，可减轻数据中心冷却压力，把成本和能耗留给计算系统。秘密武器是第六代 NVLink——它既非以太网也非 InfiniBand，而是全球唯一做到第六代纵向扩展交换的系统，难度极高。这里还有全液冷 NVLink 和新的 Groq 系统。
**English / Original**
hot water 45 degrees which takes the pressure off of the data center. Takes all of that cost and all of that energy that's used to cool the data center and makes it available for the system. This is the secret sauce. It is the only we're the only company in the world that has today built the sixth, sixth, generation scale up switching system. This is not Ethernet. This is not InfiniBand. This is NVLink. This is the sixth generation NVLink. This is insanely hard to do well. It is insanely hard to do, period. And I'm just super proud of the team. NVLink completely cooled. This is the brand new Groq system
## 01:15:10
**中文**
该系统含八颗 Groq 芯片，即第三代 LP30，已经量产。旁边是全球首款量产的共封装光学 Spectrum-X 交换机：光学器件直接封装在芯片上，与硅直接接口，把电子转换为光子并直接连接芯片。
**English / Original**
and I'll show you a little bit more about it. This system eight Groq Chips. This is the LP 30. The world's never seen it. Anything that the world's ever seen is V1. This is third generation. And we're in volume production now. And I'll show you more about that in just a second. The world's first CPO Spectrum-X switch. This is also in full production co-packaged optics. Optics comes directly onto this chip interfaces directly to silicon. Electrons get translated to photons. And it gets directly directly connected to this chip.
## 01:15:53
**中文**
我们与台积电共同发明名为 COOP 的革命性工艺，目前只有 NVIDIA 实现量产；Spectrum-X 也已全面量产。Vera 系统每瓦性能是现有任何 CPU 的两倍，也已投产。我们没想到会单独卖这么多 CPU，这项业务确定将达到数十亿美元，我为 CPU 团队自豪。
**English / Original**
We invented the process technology with TSMC. We're the only one in production with it today. It's called COOP. It's completely revolutionary. NVIDIA is in full production with Spectrum-X. This is the Vera system, twice the performance per watt of any, any, CPUs in the world today. It is also in production. Well, you know, we never we never thought we would be selling CPUs standalone. Um, we are selling a lot of CPU standalone. This is already for sure going to be a multi-billion dollar business for us. So I'm very, very pleased with our CPU architects.
## 01:16:33
**中文**
我们设计了革命性 CPU，以及由 Vera CPU 驱动的 CX9、BlueField-4 STX 新存储平台。这些机架彼此连接。NVLink 机架非常重，而且线缆似乎逐年增多。
**English / Original**
We've designed a revolutionary CPU and this is the CX9 powered with Vera CPU. The BlueField-4 STX. Our new storage platform. Okay, so these are the four. These are the the racks and it's connected. Each one of these racks. The NVLink rack. This is. I've shown you guys this before. It's a super heavy and seems to get heavier every year. Because I think there's just more cables in there every year. And so. So this is the NVLink rack. We've also taken this technology because it,
## 01:17:13
**中文**
这种结构化布线建设数据中心非常高效，所以我们也用于以太网：单机架包含 256 个液冷以太网节点。
**English / Original**
it is so efficient to create a data center with these cabling systems, structured cables. So we decided to do that for Ethernet. So this is Ethernet 256 liquid cooled nodes in one rack.
## 01:17:31
**中文**
它们通过出色连接器相连。再看 Rubin Ultra 计算节点：Rubin 水平插入，而 Rubin Ultra 垂直插入全新 Kyber 机架，使一个 NVLink 域能够连接 144 块
**English / Original**
And it is also connected with these incredible connectors. You guys want to see um. Rubin Ultra. So this is the Rubin Ultra compute node. Unlike Rubin that slides in horizontally Rubin Ultra goes into a whole new rack. It's called Kyber that enables us to connect 144
## 01:18:15
**中文**
GPU。Kyber 机架很重，我肯定举得动，但还是不举了。
**English / Original**
GPUs in one NVLink domain. And so the Kyber rack this I, I could lift it, I'm sure, but I won't.
## 01:18:27
**中文**
单个计算节点垂直滑入 Kyber 机架，顶部四个 NVLink 接头与中板连接。每个机架构成不同节点；最精彩的是中板背面取代了传输距离受限的铜缆系统，
**English / Original**
It's quite heavy. This, this is one compute node and it slides into the Kyber rack vertically. This is where it connects into this is the midplane the Kyber racks, those four top NVLink connectors, slide in and connect into this, and this becomes one of the nodes. And each one of these racks is a different compute node. And this is the amazing part. This is the midplane and the back of the midplane instead of the cabling system, which has its limits in terms of how
## 01:19:05
**中文**
让我们能够连接 144 块 GPU。
**English / Original**
far we could drive cables, copper cables, we now have this system to connect 144 GPUs.
## 01:19:13
**中文**
新 NVLink 也垂直插入中板：前面是计算节点，后面是 NVLink 交换机，共同形成一台巨型计算机，这就是 Rubin Ultra。请把它降下去，我还要用幻灯片。谢谢，这就是没有彩排的后果。只有 NVIDIA 主题演讲会把去年的幻灯片再讲一遍。
**English / Original**
This is the new NVLink. This sits also vertically and it connects into the midplane on the back compute in the front, NVLink switches in the back one giant computer. Okay, so that is. Reuben ultra. As I mentioned, as I mentioned. How about we take this back down? I need the rest of my slides. Oh, it's coming down. Okay. Thank you. Jeanine. This is what happens when you this is what happens when you don't practice. ________________ Okay. All right. So, um, you saw you. Take your time. Just don't get hurt. You saw. You saw this slide. You know, only on NVIDIA's keynote will you see last year's slide presented again.
## 01:19:50
**中文**
因为去年那张图极其重要，值得重讲。它可能是 AI 工厂未来最重要的图，全球每位 CEO 都会深入追踪。实际分析是多维的，但核心是在固定功率下研究工厂的吞吐量与 token 速度。
**English / Original**
And the reason for that is I just want to let you know that last year, I told you something very, very important and it's so important is worthwhile to tell you again. This is probably the single most important chart for the future of AI factories. And every CEO, every CEO in the world will be tracking it. We'll be studying it very deeply. It's much, much more complicated than this. It's multi-dimensional. But you will be studying the throughput and the speed of your AI factories, the throughput token speed at ISO power, because that's all the power you have throughput and token speed for your factories forever.
## 01:20:32
**中文**
这直接决定收入：今年的决策会精确反映在明年收入里。纵轴是吞吐量，横轴是 token 速率。如今 token 速度、模型规模、输入上下文与输出长度都在增长，输入可能从
**English / Original**
And that that analysis is going to lead directly to your revenues. What you do this year will show up precisely next year as your revenues. And this chart is what it's all about. And I said on the vertical axis, on the vertical axis, thank you guys. On the vertical axis is throughput. On the horizontal axis is token rate. ________________ Today I'm going to show you this because we're able because we're now able to increase the token speed and because model sizes are increasing, because the token length, the context length, depending on the different grades of different application use case continues to grow from maybe
## 01:21:16
**中文**
十万 token 增至数百万。这些变量最终决定未来 token 的营销与定价。Token 成为新商品后会像所有成熟商品一样分层：高吞吐低速度可用于免费层，下一层用更大模型、更快速度和更长上下文。
**English / Original**
100,000 tokens input length to maybe millions. The token input length is growing and also the output token length is growing. And so all of these play into ultimately. The marketing and the pricing of future tokens. Tokens are the new commodity and like all commodities, once it reaches an inflection, once it becomes mature or becomes maturing, it will segment into different parts. The high throughput low speed could be used for the free tier. The next tier could be the medium tier, larger model, maybe higher speed for sure. Larger input context.
## 01:22:00
**中文**
服务可能从免费到每百万 token 3 美元、6 美元等。边界应不断外推，因为模型越大越聪明，上下文越长越相关，速度越高就能思考和迭代更多。
**English / Original**
Length that translates to a different price point. You could see from all the different services. This one is free. It's a free tier. The first tier could be $3 per million tokens. The next tier could be $6 per million tokens. You would like to be able to keep pushing this boundary because the larger the model smarter the more input token context length more relevant, the higher the speed, the longer the more you can think and iterate smarter AI models.
## 01:22:32
**中文**
更聪明的模型允许每一档提高价格，也许是 45 美元；未来关键路径或超长研究需要极高 token 速度时，每百万 token 150 美元也不算什么。
**English / Original**
So this is about smarter AI models. And when you have smarter AI models, each one of these clicks allows you to increase the price. So this is $45. And maybe one day there'll be a premium model that allows you a premium service that allows you to generate token speeds that are incredibly high because you're in a critical path, or maybe you're doing really long research and $150 per million tokens is just not a thing.
## 01:23:00
**中文**
研究团队每天使用 5000 万 token、按每百万 150 美元计，也并非不可承受。AI 必须先在当前位置证明价值与用途，再不断改善；未来多数服务会覆盖完整分层。
**English / Original**
So let's translate that. Suppose you were to use 50 million tokens per day as a researcher at $150 per million tokens. As it turns out, as a research team, that's not even a thing. So we believe that this is the future. This is where AI wants to go. This is where it is today. It had to start here to establish the value and establish its usefulness and get better and better and better in the future. You're going to see most services encompass, encompass all of that.
## 01:23:30
**中文**
Hopper 位于图中这里，后继产品理应更高，却没人预料提高如此之多。Grace Blackwell 在免费层大幅提高吞吐，在主要变现层提高 35 倍，这与普通产品分层并无不同。
**English / Original**
________________ This is Hopper. Hopper started and I moved it moved the chart. This is 50. This is 100 Hopper looks like this. And you would have expected Hopper the next generation to be higher. But nobody would have expected it to be that much higher. ________________ This is Grace Blackwell. What Grace Blackwell did is at your free tier increase your throughput tremendously, However, where you mostly monetize your service, it increased your throughput by 35 times. This is no different than any product that every company makes.
## 01:24:06
**中文**
档次越高，质量和性能越高，销量与容量越低。Grace Blackwell 将这一层提升 35 倍，还开辟全新层级，较 Hopper 巨大跃升。
**English / Original**
The higher the tier, the higher the quality. The higher the performance. The lower the volume, the lower the capacity. And so it is no different than any other business in the world. And so now we're able to increase this tier by 35 x. And we introduced a whole new tier. This. This is the benefit of Grace Blackwell. A huge jump over Hopper. Well, this is what we're doing with. ________________ Okay, so this is Grace Blackwell. Okay, let me just reset. Reset this.
## 01:24:43
**中文**
再看 Vera Rubin：每个层级的吞吐都提升，在平均售价最高、价值最大的层级提高十倍。做到这一点极难，得益于 NVLink 72、极低延迟与极致协同设计，使整个区域上移。
**English / Original**
________________ And this is Vera Rubin. Okay. Now just think. Just think what just happened at every single tier, at every single tier, at every single tier, we increase the throughput. And at the tier that where your highest ASP and your most valuable segment, we increased it by ten X. That is the hard work. This is incredibly hard to do out here. This is the benefit of NVLink 72. This is the benefit of extremely low latency. This is the benefit of extreme co-design that we could shift the entire area up. Now, what does it mean
## 01:25:26
**中文**
对客户意味着什么？假设 1 吉瓦电力平均分给免费、中档、高档和高端层级。免费层吸引更多用户，高端层服务最有价值客户，各层产出的总和就是收入。
**English / Original**
from a customer perspective in the end? ________________ Suppose I were to take all of that and I just multiply it against. Suppose I took 25% of my power, used it in a free tier, 25% of my power in the medium tier, 25% of my power in the high tier, and 25% of my power in the premium tier. My data center only has a gigawatt. And so I get to decide how I want to distribute. The free tier allows me to attract more customers. This allows me to serve my most valuable customers. And the combination, the product of all that allows you basically your revenues, the revenues you can generate.
## 01:26:04
**中文**
在这个简化例子中，Blackwell 可产生五倍收入，Vera Rubin 又能产生五倍。因此应尽快部署 Vera Rubin，因为 token 成本下降、吞吐上升。但我们还想更进一步。高吞吐需要海量浮点运算，而低延迟
**English / Original**
Assuming this simplistic example allows Blackwell to generate five times more revenues. Vera Rubin to generate five times. Yeah. ________________ So, Vera Rubin, you should get there as soon as you can. And the reason for that is because your, your cost of tokens goes down and your throughput goes up. Now, but we want even more. We want even more. And so let me just show you back to this. This is as you, as I, as I told you, this throughput requires a ton of flops. This latency,
## 01:26:46
**中文**
与高交互性需要巨大带宽。芯片表面积有限，因此优化吞吐与优化低延迟天然冲突，这正是我们与 Groq 结合的原因。
**English / Original**
this interactivity requires an enormous amount of bandwidth. Computers don't like extreme amounts of flops. Extreme amount of bandwidth. Because there's only so much surface area for chips that any systems has. And so optimizing for high throughput and optimizing for low latency are in fact enemies of each other. And so this is what happened when we combined with Groq. Okay.
## 01:27:11
**中文**
我们收购 Groq 芯片团队并许可其技术，共同集成系统，在最有价值层级把性能提升 35 倍。这张图也解释了 NVIDIA 为何主导大多数负载：高吞吐区域极其依赖 NVLink 72，它是正确且难以击败的架构，
**English / Original**
________________ And so we, we acquired the team that worked on the Groq chips and licensed the technology. And we've been working together now to integrate the system. This is what that looks like. So at the most valuable tier, at the most valuable tier, we're now going to increase performance by 35 X. Now this very simple chart revealed to you exactly the reason why NVIDIA is so strong in the vast majority of the workloads so far. And the reason for that is because up in this area throughput matters so much. NVLink 72 is so game changing. It is exactly the right architecture,
## 01:27:52
**中文**
即使加入 Groq 也很难在这里超越。
**English / Original**
and it's even hard to beat. Even as you add Groq to it.
## 01:27:56
**中文**
但若服务目标从每秒 400 token 延伸到 1000 token，NVLink 72 会因带宽不足触顶，这时 Groq 就能进一步外推边界。
**English / Original**
However, if you extended this chart way out here and you said you wanted to have services, that delivers not 400 tokens per second, but a thousand tokens per second, all of a sudden, NVLink 72 runs out of steam and simply can't get there. We just don't have enough bandwidth. And so this is where Groq comes in, and this is what happens when we push that out.
## 01:28:20
**中文**
它突破 NVLink 72 的极限。相对 Blackwell，若负载以高吞吐为主，Vera Rubin 可提升五倍，应全部采用；若大量负载是编程和高价值工程 token 生成，则可把约 25% 数据中心配置为 Groq，
**English / Original**
________________ So it goes out beyond. Thank you. Goes out beyond even the limits of what NVLink 72 can do. And if you were to do that, translate that into revenues relative to Blackwell. ________________ Vera Rubin is five X if most of your workload is high throughput. I would stick with just 100%. Vera Rubin. If a lot of your workload wants to be coding and very high valued engineering token generation, I would add Groq to it. I would add Groq to maybe 25% of my total data
## 01:29:01
**中文**
其余 75% 使用 Vera Rubin，从而进一步扩展性能与价值。Groq 吸引我的原因是它采用确定性数据流处理器、静态编译和编译器调度，编译器预先决定数据何时到达、何时计算。
**English / Original**
center. The rest of my data center is all 100%. Vera Rubin And so that gives you a sense of how you would add Groq to Vera Rubin and extend its performance and extend its value even more. ________________ This is what happens. This is a contrast. The reason why the reason why Groq was so attractive to me is because their computing system, a deterministic data flow processor. It is statically compiled. It is compiler scheduled, meaning the compiler figures out when the data. When do the compute. The compute and
## 01:29:39
**中文**
计算与数据同步到达，一切由软件预先静态排程，没有动态调度。架构拥有海量 SRAM，专为推理这一 AI 工厂核心负载设计。世界对高速、高智能 token 的需求越大，集成价值越高。这是两个极端处理器。
**English / Original**
data arrives at the same time. All of that is done statically in advance and scheduled completely in software. There's no dynamic scheduling. The architecture is designed with massive amounts of SRAM. It is designed just for inference. This one workload. Now this one workload, as it turns out, is the workload of AI factories. And as the world continues to increase the amount of high speed tokens it wants to generate with super smart tokens, it wants to generate, the value of this integration is going to get even higher. And so these are two extreme processors. You
## 01:30:17
**中文**
一颗 Groq 芯片只有约 500 MB，而 Rubin 有 288 GB；要容纳 Rubin 的模型参数、上下文和 KV cache，需要大量 Groq 芯片，这曾限制 Groq 进入主流。突破思路是用 Dynamo 软件把推理解耦并重构流水线，
**English / Original**
could see one chip 500MB one Vera Rubin chip, one Rubin chip, 288GB. It would take a lot of rock chips to be able to hold the parameter size of Rubin, as well as all of the context that has to go. The CVV cache that has to go along with it. so that limited Grox ability to really reach the mainstream, to really take off until we had a great idea. What if we disaggregated inference all together with a piece of software called Dynamo? What if we re-architected the way that inference
## 01:30:55
**中文**
让 Vera Rubin 承担适合它的高吞吐工作，把解码生成中低延迟、带宽受限部分卸载给 Groq。两个差异极大的处理器由此统一：一个负责高吞吐，一个负责低延迟，但仍需大量内存。
**English / Original**
is done in the pipeline, so that we could put the work that makes perfect sense on Vera Rubin, and then offload the decode generation, the low latency, the bandwidth limited challenged part of the workload for Groq. And so we united unified two processors of extreme differences one for high throughput, one for low latency. It still doesn't change the fact that we need a lot of memory.
## 01:31:24
**中文**
所以加入大量 Groq 芯片扩充内存，存放万亿参数模型；旁边的 Vera Rubin 则容纳智能体系统所需海量 KV cache。这就是解耦推理：先做 prefill，再紧密集成 decode，其中注意力部分运行在
**English / Original**
________________ And so Groq, we're just going to add a whole bunch of Groq chips, which expands the amount of memory it has. And so if you could just imagine out of a trillion parameter model, We have to store all of that in rock chips. However, it sits next to. NVIDIA Vera Rubin where we could. We could hold the massive amounts of KV cache that's necessary in processing all of these agentic AI systems. ________________ It's based upon this idea of disaggregated inference. We do the prefill. That's the easy part. But we also tightly integrate the decode. So the attention part of decode is done on NVIDIA's
## 01:32:07
**中文**
需要大量数学计算的 Vera Rubin，前馈网络和 token 生成运行在 Groq。二者通过特殊低延迟模式的以太网紧耦合，延迟约减半；上层运行 AI 工厂操作系统 Dynamo，性能提升 35 倍。
**English / Original**
Vera Rubin, which needs a lot of math and the feedforward network part of it, the decode part is done. The token generation part is done on Vera Rubin on the Groq chip, the two of them working tightly coupled together over today Ethernet with a special mode to reduce latency by about half. And so that capability allows us to integrate these two systems. We run Dynamo, this incredible operating system for AI factories on top of it and you get 35 times increase. 35 times increase.
## 01:32:43
**中文**
此外还开辟了前所未有的 token 生成性能层级。这就是包含 Groq 的 Vera Rubin 系统。感谢三星为我们制造 Groq LP30 芯片并全力增产；芯片已经量产，
**English / Original**
Not to mention additional new tiers of inference performance. For token generation, the world's never seen. ________________ So this is it. This is Groq. The Vera Rubin systems including Groq. I want to thank Samsung, who manufactures the Groq LP 30 chip for us. And they're cranking as hard as they can. I really appreciate appreciate you guys. We're in production with the Groq chip and,
## 01:33:17
**中文**
预计下半年、约第三季度出货。
**English / Original**
uh, you know, we'll ship it in the second half, probably about Q3 timeframe. Okay. Groq LP.
## 01:33:28
**中文**
Vera Rubin 的早期送样非常顺利，不像 Grace Blackwell 与 NVLink 72 初次整合时那么复杂。Satya 已发消息说首台 Vera Rubin 机架已在 Microsoft Azure 运行。我们会持续生产，供应链已能每周制造数千套，
**English / Original**
________________ Vera Rubin You know, it's kind of hard. It's kind of hard to imagine any more customers. You know, and, and the really great thing is, is, um, grace Blackwell early sampling of it was really complicated because of coming together with NVLink 72, but the sampling of Vera Rubin is just going incredibly well. And in fact, Satya, I think, texted out already that the first Vera Rubin rack is already up and running at Microsoft Azure. And so I'm super excited for them. We're just going to keep cranking these things out. We have now set up a supply chain that could manufacture thousands a week
## 01:34:11
**中文**
相当于每月交付数吉瓦 AI 工厂产能。
**English / Original**
of these systems, essentially multi gigawatts of AI factories per month inside our supply chain.
## 01:34:20
**中文**
我们会同时大量生产 Vera Rubin 与 GB300 机架。Vera CPU 已全面量产并非常成功，因为 AI 调用工具需要 CPU，而 Vera 精准命中需求，也非常适合下一代数据处理。Vera CPU 加 CX-9 接入 BlueField-4 栈，全球存储行业都在加入，
**English / Original**
And so we're going to crank out these, these Vera Rubin racks while we're cranking out the GB 300 racks. We are in full production. The Vera CPUs incredibly successful. And the reason for that is because AI needs CPUs for tool use and various CPU was designed just perfectly for that sweet spot. Incredible. For the next generation of data processing, Vera CPU is ideal. ________________ The Vera CPU plus CX-9 connected into the BlueField-4 stack. 100% of the world's storage industry is joining us on this system, and the reason for that
## 01:35:02
**中文**
因为大家看到存储系统即将承受巨大压力。
**English / Original**
is because they see exactly the same thing. The storage system is going to get pounded.
## 01:35:07
**中文**
过去是人类使用存储和 SQL，未来是 AI 使用。系统要存储 cuDF、cuVS 加速数据以及至关重要的 KV cache。这就是 Vera Rubin 系统。
**English / Original**
It's going to get pounded because we used to have humans using the storage systems. We used to have humans using SQL. Now we're going to have AI using these storage systems. And it's going to store cuDF accelerated storage. cuVS accelerated storage as well as very importantly KV caching. ________________ Okay, so this is the Vera Rubin system.
## 01:35:28
**中文**
惊人的是，仅两年时间，在同一座 1 吉瓦工厂里，摩尔定律只能让晶体管、算力和带宽前进几步，而这种架构将 token 生成速率从 200 万提升到 7 亿，达到
**English / Original**
Now what's amazing is this in just two years time, in a one gigawatt factory, in just two years time, in one gigawatt factory. Using using the mathematics that I showed you earlier. Whereas Moore's law would have given us a couple of steps, we would have, you know, X factor, the number of transistors we had, X factor, the number of flops we had X factor, the number of amount of bandwidth. But with this architecture, we're going to take our token generation speed token generation rate from 2 million to 700,000,350
## 01:36:10
**中文**
350 倍增长。这就是极致协同设计的力量：纵向集成优化，再横向开放给所有人。路线图每年都有新架构。Blackwell 已到来；Rubin 有向后兼容的 Oberon 系统，客户无需改变现有设施即可升级。
**English / Original**
times increase. This is this is the power of extreme co-design. This is what I mean when we integrate and optimize vertically, but then we open it horizontally for everybody to enjoy. ________________ This is our roadmap. Very quickly. Blackwell is here. The Oberon system. In the case of Rubin, we have the Oberon system. We're always backwards compatible so that if you wanted to not change anything and just keep on moving through with the new architecture, you could do so. The old system, the standard rack system,
## 01:36:50
**中文**
标准 Oberon 机架仍可用，采用铜缆纵向扩展，也能加光学纵向扩展到 NVLink 576。NVIDIA 不会在铜缆和光学之间二选一，而会两者都做：Kyber 提供 NVLink 144，Oberon 则以 NVLink 72 加光学
**English / Original**
Oberon still available. Oberon is copper scale up, and with Oberon we could also use optical scale out or excuse me, optical scale up to expand to NVLink 576. Okay. And so there's a lot of conversation about is NVIDIA going to copper scale up or optical scale up? We're going to do both. So we're going to have NVLink 144 with Kyber and then with Oberon we're going to NVLink 72, plus optical
## 01:37:27
**中文**
达到 NVLink 576。Rubin Ultra 芯片正在流片，新 LP35 首次加入 NVIDIA NVFP4 计算结构，再带来数倍提速。Oberon NVLink 72 光学纵向扩展采用全球首款共封装光学 Spectrum-6，这些都已投产。
**English / Original**
to get to NVLink 576 the next generation of Rubin with Rubin Ultra. We have the Reuben ultra chip, which is coming, which is taping out, and we have a brand new chip LP 35, LP 35 will, for the first time incorporate NVIDIA's NVFP4 computing structure, give you another few X factor speedup. Okay. And so this is Oberon NVLink 72 optical scale up. And it uses Spectrum-6, the world's first co-packaged optical. And um all of this is in production.
## 01:38:08
**中文**
下一代是 Feynman：全新 GPU、显著升级的 LP40 LPU，由 NVIDIA 的规模与 Groq 团队共同打造；还有名为 Rosa（Rosalyne 的简称）的新 CPU，以及连接 CPU 与下一代 SuperNIC-10 的 BlueField-5。
**English / Original**
The next generation from here is Feynman. Feynman has a new GPU, of course. It also has a new LPU, LP 40 big step up. Incredible, incredible new technology now uniting the scale of NVIDIA and the Groq team building together. LP 40. It's going to be incredible. A brand new CPU called Rosa, short for Rosalyne. BlueField-5, which connects the CPU with the next SuperNIC-10. We will
## 01:38:49
**中文**
我们既有铜缆纵向扩展 Kyber，也有 Kyber CPO 光学纵向扩展。
**English / Original**
have Kyber, which is copper scale up. We will also have Kyber CPO scale up.
## 01:39:00
**中文**
这是首次同时用铜缆和共封装光学进行纵向扩展。铜缆仍重要，纵向和横向光学也都会做。整个生态需要更多铜缆、光学器件和 CPO 产能，
**English / Original**
So for the first time, we will scale up with both copper and co-packaged optics. Okay. And so a lot of people have been asking, you know Jensen, is copper going to still be important? The answer is yes. Jensen are you going to scale up optical? Yes. Are you going to scale out optical? Yes. And so for everybody who is in our ecosystem, we need a lot more capacity. And that's really the key. We need a lot more capacity for for copper. We need a lot more capacity for optics. We need
## 01:39:42
**中文**
因此我们一直与伙伴共同打基础，支撑这种增长。Feynman 将全部具备。每年都有新架构。NVIDIA 很快从芯片公司转为 AI 基础设施、AI 计算和 AI 工厂公司。如今建设完整 AI 工厂，其中仍浪费许多电力，
**English / Original**
a lot more capacity for CPO. And that's the reason why we've been working with all of you to lay the foundation for this level of growth. And so Feynman will have all of that. Let me see if I missed everything. That's it. Every single year, brand new architecture. Very quickly. Very quickly, NVIDIA went from a chip company to an AI factory company or AI infrastructure company, AI computing company, these systems. And now we're building entire AI factories. There's so much power that is
## 01:40:24
**中文**
必须让工厂以最佳方式协同。过去不同部件供应商直到数据中心才第一次相遇，但超级复杂系统不能这样建设，必须先在虚拟环境相遇。因此我们创建 Omniverse 和 Omniverse DSX World，让所有人共同设计吉瓦级 AI 工厂。
**English / Original**
squandered in these AI factories. We want to make sure that these AI factories come together designed in the best possible way. Most of these components never meet each other. Most. Most of us technology vendors now, we all know each other. But in the past we never met each other until the data center. That can't happen. We're building super complex systems and so we have to meet each other virtually somewhere else. And so we created Omniverse and Omniverse DS world, a platform where all of us can meet and design these gigafactories. Giga, you know,
## 01:41:00
**中文**
平台模拟机架的机械、热、电气和网络，并与生态伙伴工具集成；还连接电网，让双方交换信息、协调电网与数据中心功率。数据中心内用 Max-Q 动态调整功率、冷却和各项技术，
**English / Original**
gigawatt AI factories virtually in system. We have simulation systems for the racks for mechanical thermal, electrical networking. Those simulation systems integrated into all of our ecosystem partners with incredible tools companies. See also operated connected to the grid so that we could interact with each other, send each other information so that we could adjust grid power and data center power accordingly, saving energy. And then inside the data center using Max-Q so that we could adjust the system dynamically across power and cooling and all of the different
## 01:41:42
**中文**
避免浪费任何电力，以最优速率交付海量 token 吞吐。我相信这里存在两倍提升空间，在如此规模下意义巨大。这就是 NVIDIA DSX 平台，同样包含硬件层、库层和生态层。
**English / Original**
technologies we all work on together, so that we leave no power squandered, so that we run at the most optimal rate to deliver enormous amount of token throughput. There's no question in my mind there's a factor of two in here and a factor of two at the scale we're talking about is gigantic. We call this the NVIDIA DSX platform. And just as all of our platforms, there's the hardware layer, there's the library layer and there's the ecosystem layer. It's exactly the same way.
## 01:42:14
**中文**
请看视频。史上最大基础设施建设正在进行，世界竞相建设芯片、系统和 AI 工厂，每延迟一个月都损失数十亿美元。AI 工厂收入等于每瓦 token 数，因此功率受限时，每一个闲置瓦特都是收入损失。NVIDIA DSX 是 Omniverse 数字孪生蓝图，用于以最大吞吐、韧性和能效设计运营 AI 工厂。
**English / Original**
Let's show it to you. [VIDEO NARRATION - JENSEN] The greatest infrastructure build out in history is underway. The world is racing to build chip system and AI factories, and every month of delay costs billions in lost revenues. AI factory revenues are equal to tokens per watt, so with power constraints every unused. watt is revenue lost. NVIDIA RDS is an Omniverse digital twin blueprint for designing and operating AI factories for maximum token throughput, resilience, and energy efficiency.
## 01:42:54
**中文**
开发者通过多种 API 连接：DSX Sim 模拟物理、电气、热和网络；DSX Exchange 交换运营数据；DSX Flex 在电网与工厂间安全动态管理电力；DSX Max-Q 动态最大化 token 吞吐。流程从 NVIDIA 与设备商的仿真资产开始，由 PTC Windchill PLM 管理，再在 Dassault Systèmes
**English / Original**
Developers connect through several APIs. DSXM for physical, electrical, thermal, and network simulation. DSS Exchange for AI factory operational data. DSS flex for secure dynamic power management between the grid and DSS Max-Q to dynamically maximize token throughput. It starts with SIM ready assets from NVIDIA and equipment manufacturers managed by PTC Windchill PLM. Then model based systems engineering is done in Dassault Systemes
## 01:43:31
**中文**
3DEXPERIENCE 中完成基于模型的系统工程，Jacobs 将数据导入定制 Omniverse 应用，完成设计。
**English / Original**
3D experience. Jacobs brings the data into their custom Omniverse app to finalize design.
## 01:43:41
**中文**
随后用 Siemens STAR-CCM+ 做外部热仿真、Cadence Reality 做内部分析、ETAP 做电气分析、NVIDIA DSX Air 做网络仿真，并通过 Procore 虚拟调试以加快施工。投产后，数字孪生成为运营者，AI 智能体结合 DSX Max-Q 编排基础设施；Phaidra 智能体监控冷却和电气系统，
**English / Original**
It's tested with leading simulation tools using Siemens STAR-CCM+ for external thermals. Cadence reality for internal eTap for electrical and NVIDIA's network simulator DSX Air, and virtually commission through Procore to ensure accelerated construction time. When the site goes live, the digital twin becomes the operator. AI agents work with DS Max-Q to dynamically orchestrate infrastructure. Phaedra's agent oversees cooling and electrical systems,
## 01:44:21
**中文**
向 Max-Q 发送信号，持续优化计算吞吐与能效。Emerald AI 智能体解释实时电网需求与压力信号并动态调功。借助 DSX，NVIDIA 与生态伙伴正在全球建设具备极高韧性、效率和吞吐的 AI 基础设施。
**English / Original**
sending signals to Max-Q, which continuously optimizes compute throughput and energy efficiency. Emerald AI agents interpret live grid demand and stress signals and adjust power dynamically. With DSX, NVIDIA and our ecosystem of partners are racing to build AI infrastructure around the world, ensuring extreme resiliency, efficiency, and throughput.
## 01:44:51
**中文**
令人惊叹。Omniverse 从地球开始，旨在容纳各种尺度的全球数字孪生。感谢强大的伙伴生态；几年前我们还不认识许多公司，如今却紧密合作，共建全球最大计算机，并把它扩展到行星尺度。
**English / Original**
________________ It's incredible. Right? Well. On Omniverse, Omniverse was designed to hold the world's digital twin starting from the earth, and it's going to hold digital twins of all sizes. And so we have just such a great ecosystem of partners. I want to thank all of you. All of these companies are brand new to our world. We didn't know many of you. Just a couple of years ago, and now we're working so close together to work on and build together the largest computer the world's ever seen, and also to do it at planetary scale.
## 01:45:29
**中文**
这正是新时代基础设施合作的方式。NVIDIA DSX 是我们的新 AI 工厂平台。再简短谈一件事：我们要进入太空。通过辐射认证的 Thor 已用于卫星成像；未来还会在太空建设数据中心。我们正与伙伴开发 Vera Rubin Space One，准备把数据中心送入太空。
**English / Original**
So NVIDIA DSX is our new AI factory platform. ________________ I'll spend very little time on this at this time. However, we're going to space. We've already been out in space. Thor is radiation approved and we're in satellites. You do imaging from the from satellites. In the future, we'll also build data centers in the in the in the in space, obviously very complicated to do. So we have we're working with our partners on a new computer called Vera Rubin Space one. And it's going to go out to space and start data centers out, out in space. now. Of course, in space there's no
## 01:46:13
**中文**
太空没有对流，只能靠辐射散热，因此必须解决冷却难题，许多优秀工程师正在研究。下面谈新事物：Peter Steinberger 在现场，他开发了 OpenClaw，可能没想到它会如此成功。
**English / Original**
connection, there's no convection, there's just radiation. And so we have to figure out how to, um, cool these systems out in space. But we've got lots of great engineers working on it. ________________ Let me talk to you about something new. So, so, um, uh, Peter Steinberger is here, and, um, he, he wrote a piece of software, uh, it's called OpenClaw. And and, um, I don't know if he realized, uh, how successful it was going to be.
## 01:46:49
**中文**
OpenClaw 意义深远，几周内成为人类历史上最流行的开源项目，超过 Linux 三十年的成绩。今天我们宣布支持它。使用方式非常简单，只需输入一条命令，
**English / Original**
Um, but the importance is profound. OpenClaw is the number one. It's the most popular open source project in the history of humanity. And it did so in just a few weeks. It exceeded it exceeded what Linux did in 30 years. And it's that important. It is that important. It will do, well, uh, this is all you do. Okay. ________________ We're announcing our support of it. Uh, let me let me just quickly go through this. I want to show you a couple of things. You simply type this,
## 01:47:27
**中文**
控制台就会找到并下载 OpenClaw，为你构建 AI 智能体，随后可以要求它完成其他任务。
**English / Original**
you type it this into a into a console and it goes out. It finds OpenClaw, it downloads it, it builds you an AI agent and then you could tell it whatever else you need to do. Okay,
## 01:47:41
**中文**
请看。
**English / Original**
so let's take a look. ________________
## 01:48:30
**中文**
【视频片段】
**English / Original**
[VIDEO]: [VIDEO SOUNDCLIPS]
## 01:49:15
**中文**
一个新的开源项目刚刚发布。Andrej Karpathy 推出一种研究方式：把任务交给 AI 智能体后去睡觉，它一夜运行 100 个实验，保留有效结果、淘汰无效结果。【Peter】我很喜欢自己的工具赋能普通人；一位 60 岁父亲安装它后，甚至把酿酒机器通过蓝牙接入 OpenClaw。
**English / Original**
An open source project just dropped. Andrej Karpathy has just launched something called a. Research is a huge deal. You give an AI agent a task, go to sleep. It runs 100 experiments overnight. Keeping what works and what doesn't. [PETER STEINBERGER] I really love what my stuff enables that person to do. And he had like one guy, he told me like he installed it as a 60 year old dad and like they made beer, connected the machine via Bluetooth to open cloth.
## 01:49:50
**中文**
随后他们把一切自动化，包括供人订购龙虾的完整网站，深圳数百人排队购买。人人都在谈 OpenClaw，甚至已有 ClawCon 大会。
**English / Original**
And then we automated everything, including the whole website for people to order the lobster. Hundreds of people are queuing up for lobsters in Shenzhen. Do you want to build open floor with OpenClaw? Everyone is talking about OpenClaw. But what is OpenClaw? Believe it or not, there's already a ClawCon. ________________ [JENSEN]:
## 01:50:29
**中文**
不可思议。我用这张图说明 OpenClaw：它是连接并调用 LLM 的智能体系统，管理各种资源，可访问工具、文件系统和模型，支持调度与定时任务。
**English / Original**
Incredible. Incredible. Now, I illustrated effectively what OpenClaw is in this way. And so all of you can understand it. But let's just think what happened. What is OpenClaw? It connects. It's an it's a genetic system. It calls and connects to large language models. So the first thing it has it has resources that it manages it. It could access tools. It could access file systems. It could access large language models. It's able to do scheduling. It's able to do cron jobs.
## 01:51:01
**中文**
它能把提示中的问题逐步分解，生成并调用子智能体；还具备多模态输入输出，你可以挥手或说话，它能理解，也能发消息、短信和邮件。按这种结构看，它其实是一套操作系统。
**English / Original**
It's able to decompose a problem that a prompt that you gave it into step by step by step. It could spawn off and call upon other subagents. It has IO. You could talk to it in any modality you want. You could wave at it and understand you. You could talk to any modality you want. It sends you messages. It texts you, sends you email. So it's got I o. Um, what else does it have? Well, based on that, you could, you could say, in fact, it's an operating system. I've just used the same
## 01:51:42
**中文**
这与操作系统架构的描述完全一致。OpenClaw 本质上开源了智能体计算机的操作系统：Windows 让个人电脑成为可能，OpenClaw 则让个人智能体成为可能。其采用速度已说明影响，但更重要的是，每家公司、每家软件企业都开始意识到，
**English / Original**
syntax that I would describe in operating system art. OpenClaw has open sourced essentially the operating system of agentic computers. It is no different than how windows made it possible for us to create personal computers. Now, OpenClaw has made it possible for us to create personal agents. The implication is incredible. The implications are incredible. First of all, the adoption says something, you know all in itself. However, the most important thing is this every single company now realizes every single company, every single software company,
## 01:52:20
**中文**
CEO 必须回答：你的开源战略是什么？
**English / Original**
every single technology company. For the CEOs, the question is, what's your open source strategy?
## 01:52:27
**中文**
正如企业都需要 Linux、HTTP/HTML 和 Kubernetes 战略，如今每家公司都需要开放战略与智能体系统战略。这就是新计算机。
**English / Original**
Just as we need it all have a Linux strategy. We all need it to have HTTP, HTML strategy, which started the internet. We all needed to have a Kubernetes strategy, which made it possible for mobile cloud to happen. Every company in the world today needs to have an open strategy, an agentic system strategy. This is the new computer.
## 01:52:48
**中文**
OpenClaw 之前的企业 IT 是这样：数据中心存放人的文件和企业结构化数据；数据经过包含工具、记录系统和固化工作流的软件，变成人类数字员工使用的工具。旧 IT 产业由软件公司创建工具、保存文件，
**English / Original**
________________ Now this is just the exciting part. This is enterprise IT before OpenClaw, you know, and, and I mentioned earlier the way enterprise it works. And the reason why it's called data centers is because these large rooms, these large buildings held data, held the files of people. The structured data of business, it would pass through software that has tools and systems of records and all kinds of workflow that's codified into it, and that turns into tools that humans would use. Digital workers would use. That is the old IT industry, software companies creating tools, saving files, and of course,
## 01:53:32
**中文**
并由全球系统集成商和顾问帮助企业使用与集成这些工具。
**English / Original**
GSIs consultants that help companies figure out how to use these tools and integrate these tools.
## 01:53:36
**中文**
这些工具对治理、安全、隐私和合规仍极有价值；但进入 OpenClaw 和智能体时代后，每家 IT 公司和 SaaS 公司都会变成 AaaS 公司。
**English / Original**
These these tools are incredibly valuable for governance and security and privacy and compliance. And all of that continues to be true. It's just that post-OpenClaw post agentic. This is what it's going to look like. ________________ This is the extraordinary part. Every single IT company, every single company, every SaaS company, every SaaS company will become a. Agaas company. No question about it.
## 01:54:09
**中文**
也就是 Agentic-as-a-Service（智能体即服务）。OpenClaw 像 Linux、Kubernetes、HTML 一样，在恰当时刻提供行业所需的开放栈，让所有企业都能在其上构建。
**English / Original**
Every single SaaS company will become Agaas company, an Agentic-as-a-service company. And what's amazing is this you now open cloud gave us gave the industry exactly what it needed at exactly the time, just as Linux gave the industry exactly what it needed, exactly the time, just as Kubernetes showed up at exactly the right time, just as HTML showed up, it made it possible for the entire industry to grab on to this open source stack and go do something with it.
## 01:54:42
**中文**
但有个问题：企业网络里的智能体能访问敏感信息、执行代码并与外界通信。它可能读取员工、供应链和财务数据，再发往外部，显然不能放任。因此我们与 Peter 及全球顶尖安全和计算专家合作，
**English / Original**
There's just one catch. Agentic systems in the corporate network can have access to sensitive information. It can execute code and it can communicate externally. Just say that out loud. Okay, think about it. Access sensitive information. Execute code. Communicate externally. You could of course access employee information, access supply chain, access finance information sensitive information and send it out communicate externally. Obviously this can't possibly be allowed. And so what we did was we worked with Peter, we took some of the world's
## 01:55:21
**中文**
使 OpenClaw 具备企业级安全与隐私。NVIDIA 为此推出 NemoClaw 参考设计及智能体 AI 工具包，其中 OpenShell 已集成进 OpenClaw，使其达到企业就绪状态。
**English / Original**
best security and computing experts, and we worked with Peter to make OpenClaw, OpenClaw, enterprise secure and enterprise private capable. ________________ And we call that this is our NVIDIA OpenClaw reference for open Nemo claw, which is a reference for OpenClaw. And it has all these Agentic AI tool kits. And the first part of it is technology we call open shell that has now been integrated into OpenClaw. Now it's enterprise ready. This stack, this stack with a reference design we call Nemo.
## 01:56:05
**中文**
NemoClaw 可下载试用，并连接全球 SaaS 企业极有价值的策略引擎；带 OpenShell 的 OpenClaw 可以执行这些策略。
**English / Original**
NemoClaw. Okay. With a reference stack we call NemoClaw. You could download it, play with it and you could connect to it. The policy engine of all of the SaaS companies in the world. And your policy engines are super important, super valuable. So the policy engines could be connected. NemoClaw or OpenClaw with open shell would be able to execute that policy engine.
## 01:56:31
**中文**
它包含策略、网络护栏和隐私路由器，使智能体能在公司内部安全运行。定制智能体还需要定制模型，这就是 NVIDIA 开放模型计划。我们已进入各 AI 模型领域前沿，包括 Nemotron、
**English / Original**
It has a policy. It has a network guardrail, it has a privacy router. And as a result, we could protect and keep the the clause from executing inside our company and do it safely. We also added several things to the system. And one of the most important things you want to do with your own claw custom claws is so that you can have your custom models. And this is NVIDIA's open model initiative. ________________ We are now at the frontier of every single domain of AI models, whether it's Nemotron,
## 01:57:11
**中文**
物理 AI 世界基础模型 Cosmos、通用与人形机器人模型 GR00T、自动驾驶 Alpamayo、数字生物学 BioNeMo、AI 物理 Earth-2。世界多元，没有单一模型能服务所有行业。近 300 万开放模型覆盖语言、视觉、生物、物理和自主系统，使开发者能构建专用 AI。
**English / Original**
Cosmos, World Foundation model, GR00T, artificial general robotics, humanoid robotics models, Alpamayo for autonomous vehicle, BioNeMo for digital biology, Earth-2 for AI physics. We are at the frontier on every single one. Take a look. ________________ [VIDEO NARRATION - JENSEN] The world is diverse. No single model can serve every industry. Open models is one of the largest and most diverse AI ecosystems in the world. Nearly 3 million open models across language, vision, biology, physics and autonomous systems enable AI builds for specialized domains. NVIDIA
## 01:57:54
**中文**
NVIDIA 是最大的开源 AI 贡献者之一，发布六大开放前沿模型家族，并提供训练数据、配方和框架帮助定制采用。Nemotron 核心覆盖语言推理、视觉理解、RAG、安全和语音。
**English / Original**
is one of the largest contributors to open source AI. We build and release six families of open frontier models, plus the training data, recipes, and frameworks to help developers customize and adopt new leaderboard topping models are launching for every family. At the core, Nemotron, reasoning models for language. Visual understanding. RAG. Safety. And speech.
## 01:58:27
**中文**
Cosmos 用于物理 AI 世界生成与理解；Alpamayo 是首个会思考推理的自动驾驶 AI；GR00T 面向通用机器人；BioNeMo 面向生物、化学和分子设计；Earth-2 基于 AI 物理预测天气气候。这些开放模型让开发者为专门领域构建部署 AI。
**English / Original**
Cosmos frontier models for physical AI world generation and understanding. Alpamayo. The world's first thinking and reasoning. Autonomous vehicle AI. GR00T, foundation models for general purpose robots. BioNeMo open models for biology, chemistry and molecular design. Earth-2 models for weather and climate forecasting rooted in AI physics. NVIDIA open models give researchers and developers the foundation to build and deploy AI for their own specialized domains.
## 01:59:08
**中文**
这些模型不仅位列排行榜前沿，更重要的是我们会持续每天改进：Nemotron 3 后有 4，Cosmos 1 后有 2，GR00T 也会不断升级。
**English / Original**
________________ Our models are. Thank you. Our models are valuable to all of you because, number one, it's on the top of the leaderboard. It's world class. But most importantly, it's because we are not going to give up working on it. We're going to keep on working on it every single day. Nemotron 3 is going to be followed by neutron for cosmos one was followed by cosmos two. GR00T, GR00T at generation two each and one of these.
## 01:59:39
**中文**
我们会继续推进所有模型，纵向整合、横向开放，让人人加入 AI 革命。它们在研究、语音、世界模型、通用机器人、自动驾驶和推理榜单上领先；这里是 OpenClaw 中的 Nemotron 3。
**English / Original**
We're going to continue to advance these models. Vertical integration. Horizontal openness. So that we can enable everybody to join the AI revolution. Number one on leaderboard, across research and voice and world models and artificial general robotics and self-driving cars and reasoning. ________________ And of course, one of the most important one, this is Nemotron 3 in OpenClaw.
## 02:00:08
**中文**
在排行榜前三名中，它属于全球最佳模型之列，说明我们处于前沿。同时我们也要提供基础模型，供所有人微调和后训练成所需智能。Nemotron 3 Ultra 将成为全球最优秀的基础模型。
**English / Original**
This is Nemotron 3 and OpenClaw. And look at the top three. There are the three best models in the world. Okay, so we are at the frontier. ________________ It is also true. It is also true that we want to create the foundation model so that all of you can fine tune it, post train it into exactly the intelligence you need. This is Nemotron 3 Ultra. It is going to be the best base model the worlds ever created.
## 02:00:40
**中文**
它帮助各国建设主权 AI。今天还宣布 Nemotron 联盟。我们投入数十亿美元 AI 基础设施，既开发推理库所需核心引擎，也创建激活各行业的 AI 模型。LLM 很重要，但世界各国与不同行业
**English / Original**
________________ This allows us to help every country build their sovereign AI, and we're working with so many different companies out there. And one of the most exciting things that we're doing today. I'm announcing today is a Nemotron coalition. We are so dedicated to this. We have invested billions of dollars of AI infrastructure so that we could develop the core engines for AI that's necessary for all the libraries of inference and so on, but also to create the AI models to activate every single industry in the world. Large language models is really important. Of course it's important. How can how can human intelligence not be. However, in different industries around the world,
## 02:01:24
**中文**
还必须能够定制自己的领域模型；生物、物理、自动驾驶、通用机器人和人类语言差异巨大。我们能与各地区共建专用主权 AI。Nemotron 4 联盟成员包括图像公司 Black Forest Labs、编程公司 Cursor、下载量达十亿的智能体框架 LangChain、Mistral，
**English / Original**
in different countries, around the world, you need to have the ability to customize your own models and the domains. The domain of the domain of the models is radically different from biology to physics, to The self-driving cars to general robotics, to, of course, human language. And we have the ability to work with every single region to create their domain specific, their sovereign AI. Today, we're announcing a coalition to partner with us to make Nemotron 4 even more amazing. ________________ And that coalition has some amazing companies in it. Black Forest Labs imaging company, Cursor, the famous coding company. We use lots of it. LangChain billion
## 02:02:06
**中文**
以及 Arthur Mensch。
**English / Original**
downloads for creating custom agents. Mistral. Arthur. Arthur Mensch. I think he's here.
## 02:02:13
**中文**
还有 Perplexity——非常好用的多模态智能体系统——Reflection、印度的 Sarvam、Mira Murati 的 Thinking Machines 等优秀公司。全球每家企业和软件公司都需要智能体战略和开源战略；这些公司都认同，并与我们集成 Nemo、
**English / Original**
Incredible. Incredible company. Perplexity. Perplexity. His computer. Absolutely. Use it. Everybody use it. It is so good. A multi-modal agentic system. Reflection. Sarvam from India. Thinking Machines. Mira Murati's lab. Incredible companies joining us. Thank you. I said. I said that every single enterprise company, every single software company in the world needs an Agentic systems need an agent strategy. You need to have an open source strategy. ________________ And they all agree. And they're all partnering with us to integrate Nemo,
## 02:02:56
**中文**
NemoClaw 参考设计、NVIDIA 智能体 AI 工具包和所有开放模型。
**English / Original**
the NemoClaw reference design, the NVIDIA Agentic AI toolkit, and of course, all of our open models.
## 02:03:05
**中文**
合作伙伴多得数不清，感谢大家。现在是企业复兴与重塑的时刻：原本 2 万亿美元的产业将变成数万亿美元产业，不仅提供人使用的工具，还出租各专门领域的专业智能体。
**English / Original**
One company after another. There's so many. ________________ And we're partnering with all of you. I'm really grateful for that. And, um, this is our moment. This is a reinvention. This is, this is a renaissance, a renaissance of the enterprise. It from what would be a $2 trillion industry. This is going to become a multi trillion dollar industry offering not just tools for people to use, but agents that are specialized in very special domains that you're expert in that we could rent.
## 02:03:39
**中文**
未来公司每位工程师都会需要年度 token 预算。他们年薪数十万美元，我可能再给相当于工资一半的 token，使能力放大十倍。Token 额度已成为硅谷招聘条件。
**English / Original**
I could totally imagine in the future, every single engineer in our company will need an annual token budget. They're going to make a few hundred thousand dollars a year their base pay. I'm going to give them probably half of that on top of it as tokens so that they could be amplified ten X. Of course we would. It is now one of the recruiting tools in Silicon Valley.
## 02:04:06
**中文**
求职者会问工作附带多少 token，因为获得 token 的工程师生产力更高。这些 token 将由我们共同建设的 AI 工厂生产。今天每家企业都建立在文件系统和数据中心之上，
**English / Original**
How many tokens comes along with my job. And the reason for that is very clear, because every engineer that has access to tokens will be more productive. And those tokens, as you know, will be produced by AI factories that all of you and us, we partner to build. Okay. So every single enterprise company in today sit on top of file systems and data centers.
## 02:04:32
**中文**
未来每家软件公司都会智能体化，既为员工使用 token，也为客户制造 token。OpenClaw 事件的重要性堪比 HTML 和 Linux：行业如今拥有世界级、巨大的开放框架，可用来构建开放核心战略；我们还提供经过优化的 Nemo 参考设计，
**English / Original**
Every single software company of the future will be agentic and they will be token manufacturers. They'll be token users for their engineers, and there'll be token manufacturers for all of their customers. ________________ The open clause in event the OpenClaw event cannot be understated. This is as big of a deal as HTML. This is as big of a deal as Linux. We have now a world class open, a gigantic framework that all of us could use to build our open core strategy, and we've created a reference design we call Nemo. Nemo that all of you could use that is optimized.
## 02:05:15
**中文**
它高性能、安全可靠。说到智能体，它们会感知、推理和行动。今天多数是数字智能体，在数字世界推理、写软件；但我们也长期开发具身智能体，也就是机器人，它们需要物理 AI。
**English / Original**
It's performant. It is safe and secure. ________________ Speaking of agents, agents, as you know, perceive, reason and act. Most of the agents in the world today that I've spoken about are digital agents. They act in the digital world. They reason, they write software. It's all digital. But we also have been working on physically embodied agents for a long time. We call them robots. And the AIS that they need are physical eyes. We have some big
## 02:05:54
**中文**
展会有 110 台机器人，几乎所有机器人公司都与 NVIDIA 合作。我们提供三台基础计算机：训练计算机、合成数据与仿真计算机、机器人内部计算机；还提供完整软件栈和 AI 模型，并融入 Siemens、Cadence 等全球生态。今天又宣布许多新伙伴。
**English / Original**
announcements here. I'm going to just walk through a few of them, 110 robots here, almost every single company in the world, I can't think of one that are building robots is working with NVIDIA. We have three computers the training computer, the synthetic data generation and simulation computer, and of course, the robotics computer that sits inside the robot itself. We have all the software stacks necessary to do so, the AI models to help you. And all of this is integrated into ecosystems around the world. And all of our partners, from Siemens to Cadence, incredible partners everywhere. And today we're announcing a whole bunch of new, new partners. As
## 02:06:37
**中文**
自动驾驶的 ChatGPT 时刻已经到来，我们知道汽车可以成功自主行驶。今天宣布四家加入 NVIDIA robotaxi-ready 平台的新伙伴：比亚迪、现代、日产、吉利，合计年产 1800 万辆；此前已有奔驰、丰田和通用。
**English / Original**
you know, we've been working on self-driving cars for a long time. The ChatGPT moment of self-driving cars has arrived. We now know we could successfully, autonomously drive cars. And today, we are announcing four new partners for NVIDIA's robotaxi ready platform. BYD, Hyundai, Nissan, Geely. All together, 18 million cars built each year, joining our partners from before. Mercedes. Toyota. GM. The number of robotaxi ready cars
## 02:07:16
**中文**
未来支持机器人出租车的车辆数量会非常庞大。我们还宣布与 Uber 合作。
**English / Original**
in the future are going to be incredible. And we're announcing also a partnership with Uber.
## 02:07:23
**中文**
这些车辆将在多座城市接入 Uber 网络。ABB、Universal Robots、KUKA 等公司正把我们的物理 AI 模型与仿真系统集成，将机器人部署到各地生产线。还有 Caterpillar 和 T-Mobile；未来无线电塔会变成 NVIDIA Aerial AI-RAN，即机器人化无线电塔，
**English / Original**
Multiple cities we're going to be deploying and connecting these robo taxi ready vehicles into their network. And so a whole bunch of new cars. We have ABB, Universal Robotics, Kuka, so many robotics companies here, and we're working with them to implement our physical AI models integrated into simulation systems so that we could deploy these robots into manufacturing lines all over. We have Caterpillar here. We even have T-Mobile here. And the reason for that is in the future, that radio radio tower used to be a radio tower is going to be an NVIDIA Aerial AI ram. And so this is going to be a robotics radio tower,
## 02:08:08
**中文**
能推理流量并调整波束成形，尽可能节能并提高信号质量。现场还有许多人形机器人，我最喜欢的是迪士尼机器人。请看视频。
**English / Original**
meaning it can reason about the traffic, figures out how to adjust its beamforming so that it could save as much energy as possible and increase the amount of fidelity as possible. There's so many humanoid robots here, but one of my favorites. One of my favorites is a Disney robot. You know what? Tell you what, let me just show you some of the videos.
## 02:08:32
**中文**
物理 AI 首次全球规模化部署已经到来。借助 NVIDIA Alpamayo，自动驾驶车辆具备推理能力，能在不同场景安全智能运行。我们要求汽车讲述行动：“我要向右变道以沿路线行驶。”
**English / Original**
Let's look at that first. ________________ [VIDEO NARRATION - JENSEN] The first global rollout of physical AI at scale is here. Autonomous vehicles and with NVIDIA Alpamayo vehicles now have reasoning helping them operate safely and intelligently across scenarios. We ask the car to narrate its actions. I'm changing lanes to the right to follow my route.
## 02:09:05
**中文**
它能边决策边解释：“我的车道有一辆并排停放的车，我要绕过去。”也能服从指令：“奔驰，能快一点吗？”“好的，我会加速。”这是物理 AI 与机器人时代。但现实世界极其多样、不可预测、充满边缘案例，真实数据永远不足以覆盖所有场景。
**English / Original**
Explain its thinking as it makes decisions. There's a double parked vehicle in my lane. I'm going around it. And follow instructions. Hey, Mercedes, can we speed up? Sure, I'll speed up. This is the age of physical AI and robotics. Around the world. Developers are building robots of every kind, but the real world is massively diverse, unpredictable, full of edge cases. Real world data will never be enough to train for every scenario.
## 02:09:44
**中文**
机器人需要由 AI 和仿真生成的数据。开发者用互联网规模视频和人类示范预训练世界基础模型，评估后再后训练；经典与神经仿真生成海量合成数据并规模化训练策略。NVIDIA 构建了开源 Isaac Lab，用于机器人训练、评估和仿真。
**English / Original**
We need data generated from AI and simulation for robots. Compute is data developers, pre-trained World Foundation models on internet scale, video and human demonstrations, and evaluate the model's performance to prepare them for post-training. Using classical and neural simulation. They generate massive amounts of synthetic data and trained policies at scale to accelerate developers. NVIDIA built open source Isaac Lab for robot training and evaluation and simulation.
## 02:10:21
**中文**
Newton 提供可扩展、GPU 加速的可微物理仿真，Cosmos 提供神经世界模型，GR00T 开放机器人基础模型负责推理和动作生成。充足算力正帮助开发者弥合物理 AI 数据缺口。Peritas AI 在 Isaac Lab 训练手术室助手，并用 Cosmos 扩增数据。
**English / Original**
Newton for extensible and GPU accelerated differentiable physics simulation. Cosmos world models for neural simulation. And GR00T open robotics foundation models for robot reasoning and action generation. With enough compute, developers everywhere are closing the physical AI data gap. Peritas AI trains their operating room assistant robot in NVIDIA Isaac Lab, multiplying their data with NVIDIA Cosmos World models.
## 02:10:54
**中文**
Skild AI 用 Isaac Lab 与 Cosmos 为其机器人“大脑”生成后训练数据，再通过强化学习在数千种变化中增强模型。Humanoid 用 Isaac Lab 训练全身控制和操作策略，Hexagon Robotics 用其训练与生成数据，Foxconn 和 Noble Machines 也在其中微调 GR00T 模型。
**English / Original**
Skild AI uses Isaac Lab and Cosmos to generate post-training data for their Skild AI brain. They use reinforcement learning to harden the model across thousands of variations. Humanoid uses Isaac Lab to train whole body control and manipulation policies. Hexagon robotics uses Isaac Lab for training and data generation. Foxconn fine tunes Group models in Isaac lab, as does Noble Machines.
## 02:11:27
**中文**
Disney Research 在 Newton 与 Isaac Lab 中使用 Kamino 物理模拟器，为各个世界的角色机器人训练策略。
**English / Original**
Disney Research uses their Kamino physics simulator in Newton and Isaac Lab to train policies across their character robots in every universe. ________________
## 02:12:45
**中文**
【黄仁勋】女士们先生们，Olaf！【Olaf】借过。【黄】Newton 真能用，Omniverse 也能用。你好吗？【Olaf】见到你太开心了。【黄】因为我给了你 Jetson 计算机。【Olaf】那是什么？【黄】就在你肚子里，而且你是在 Omniverse 里学会走路的。
**English / Original**
JENSEN: Ladies and gentlemen. Olaf. OLAF: Coming through. JENSEN: Newton works. Wow. Omniverse works. Olaf, how are you? OLAF: I'm so happy now that I'm meeting you. JENSEN: I know because I gave you your computer. Jetson. OLAF: What's that? JENSEN: Well, It's in your tummy. OLAF: That's going to be amazing. JENSEN: And you learn how to walk inside Omniverse.
## 02:13:22
**中文**
【Olaf】我爱走路，比骑驯鹿好多了，还能仰望美丽天空。【黄】这得益于物理：我们与 Disney、DeepMind 联合开发、运行在 NVIDIA Warp 上的 Newton 求解器，让你能适应物理世界。
**English / Original**
OLAF: I just love walking. This is so much better than riding on a reindeer. Gazing up at the beautiful sky. JENSEN: And it was because of physics. Using this Newton solver that runs on top of NVIDIA Warp that we jointly developed with Disney and with DeepMind, that made it possible for you to be able to adapt to the physical world.
## 02:13:49
**中文**
看看你多聪明。【Olaf】我是雪人，不是“雪人百科”。【黄】想象未来 Disneyland 里这些机器人角色四处走动。不过说实话，我原以为你会更高，没见过这么矮的雪人。
**English / Original**
Check that out. That's how smart you are. OLAF: I'm a snowman, not a snowclopedia. JENSEN: Could you imagine this? The future of Disneyland. All these. All these robots. All these characters wandering around. Oh, you know, I have to admit, though. I thought you were going to be taller. I've never seen such a short snowman, to be honest.
## 02:14:18
**中文**
【Olaf】才不是。【黄】愿意帮我收尾吗？【Olaf】好耶！【黄】通常我会重述主题：推理拐点、AI 工厂、开放核心智能体革命，以及物理 AI 和机器人。不过这次何不请一些朋友一起结束？
**English / Original**
OLAF: Nope. JENSEN: Hey, tell you what, you want to help me out. OLAF: Hooray! JENSEN: Okay. Usually. Usually I close the keynote by telling you what I told you. We talked about inference, inflection. We talked about the AI factory. We talked about the open core agent revolution that's happening. And of course, we talked about physical AI and robotics. But tell you what, why don't we get some friends to help us close it out?
## 02:14:47
**中文**
【Olaf】当然。【黄】好，播放吧。
**English / Original**
OLAF: Of course. JENSEN: All right, play it. Come on.
## 02:15:32
**中文**
【视频——歌曲播放】【黄】好。
**English / Original**
[VIDEO - SONG PLAYS] JENSEN: All right.
## 02:18:45
**中文**
祝大家度过精彩的 GTC！
**English / Original**
Have a great GTC. ________________