Jensen Huang
Good morning. Nice to see all of you. All right. What's the game plan?
早上好。很高兴见到大家。好的。有什么计划？
Colette Kress
Colette Kress
Okay. Well, we've got a full house and we're thanking you all for coming out for our first in-person in such a long time. Jensen and I are here to kind of really go through any questions that you have, questions from yesterday.
好的。 好，我们满座了，感谢大家在这么长时间以来第一次亲临现场。 Jensen 和我在这里，准备回答您的任何问题，也是为了回答昨天提出的问题。
And we're going to go through a series of folks that are going to be in the aisles that you can just reach-out to us, raise your hand, we'll get to you with a mic and Jensen are here to answer any questions from yesterday.
我们将接连邀请一系列人前来参与，并在过道里为您提供帮助，只需向我们伸出手，我们会为您传递麦克风，Jensen会回答昨天提出的任何问题。
We thought that would be a better plan for you. I know you have already asked quite a few questions, both last night and this morning, but rather than giving you a formal presentation, we're just going to go through of good Q&A today. Sound like a good plan.
我们认为这对你来说是一个更好的计划。我知道你昨晚和今早已经问了很多问题，但与其给你一个正式的介绍，我们今天更好的是进行一些问答。听起来是个不错的计划。
I'm going to turn it to Jensen to see if he wants to add some opening remarks because we have just a quick introduction. We'll do it that way. Okay.
我会把话题转给Jensen，看看他是否想要补充一些开场白，因为我们只有一个简短的介绍。我们就这样处理。好的。
Jensen Huang
Jensen Huang
Yeah. Thank you. First, great to see all of you. There were so many things I wanted to say yesterday and probably have said -- and wanted to say better, but I got to tell you, I've never presented at a rock concert before. I don't know about you guys, but I've never presented in a rock concert before. The -- I had simulated what it was going to be like, but when I walked on stage, it still took my breath away. And so anyways, I did the best I could.
是的。谢谢你们。首先，很高兴见到大家。昨天有很多事情我想说，也可能已经说了，也想说得更好，但我得告诉你们，我以前从未在摇滚音乐会演讲过。我不知道你们如何，但我以前从未在摇滚音乐会演讲过。我预演过那种场景，但走上舞台时，还是令我惊叹。不管怎样，我尽力了。
Next, after the tour, I'm going to do a better job, I'm sure. I just need a lot more practice. But there were a few things I wanted to tell you. Is there a clicker -- oh, look at that. See, this is like spatial computing. It's -- by the way, if you get -- I don't know you'll get a chance, because it takes a little step up, but if you get a chance to see Omniverse in Vision Pro, it is insane. Completely incomprehensible how realistic it is.
接下来，游览完之后，我一定会做得更好的。我只是需要更多的实践。不过有几件事我想告诉你。你有一个点击器吗——哦，看这个。这就像是空间计算。顺便说一句，如果你有机会（因为需要一点提升），看到Omniverse in Vision Pro，简直太疯狂了。它的真实感完全难以置信。
All right. So we spoke about five things yesterday and I think the first one really deserves some explanation. I think the first one is, of course, this new industrial revolution. There were two -- there are two things that are happening, two transitions that are happening. The first is moving from general purpose computing to accelerated computing. If you just looked at the extraordinary trend of general-purpose computing, it has slowed down tremendously over the years.
好的。昨天我们谈到了五件事情，我觉得第一件事情真的值得解释一下。我认为第一件事情是，当然，这个新的工业革命。目前有两个变化正在发生，两个过渡正在发生。第一个是从通用计算过渡到加速计算。如果你仅仅看一看通用计算这一非凡的趋势，你会发觉随着时间的推移，它明显减缓了。
And in fact, we've known that it's been slowing down for about a decade and people just didn't want to deal with it for a decade, but you really have to deal with it now. And you can see that people are extending the depreciation cycle of their data centers as a result. You could buy a whole new set of general purpose servers and it's not going to improve your throughput of your overall data center dramatically.
实际上，我们已经知道它在过去的十年里一直在减速，只是人们在这十年里不想处理它，但现在你真的必须处理它。你可以看到，由此导致人们延长了他们数据中心的折旧周期。你可以购买一整套全新的通用服务器，但这并不会显著提高您整个数据中心的吞吐量。
And so you might as well just continue to use what you have for a little longer. That trend is never going to reverse. General purpose computing has reached this end. We're going to continue to need it and there's a whole lot of software that runs on it, but it is very clear we should accelerate everything we can.
所以你可能也可以继续使用你现有的一段时间。这种趋势永远不会逆转。通用计算已经走到了这一步。我们将继续需要它，有很多软件运行在它上面，但非常明显我们应该加快一切。
There are many different industries that have already been accelerated, some that are very large workloads that we really would like to accelerate more. But the benefits of accelerated computing is very, very clear.
有许多不同的行业已经加速发展，一些工作负载非常大，我们真的非常希望能够加速更多。但加速计算的好处非常非常明显。
One of the areas that I didn't spend time on yesterday that I really wanted to was data processing. NVIDIA has a suite of libraries that before you could do almost anything in a company, you have to process the data. You have to, of course, ingest the data, and the amount of data is extraordinary. Zettabytes of data being created around the world, just doubling every couple of years, even though computing is not doubling every couple of years.
昨天我没有花时间处理的一个领域是数据处理。NVIDIA拥有一套库，几乎任何在公司中你想做的事情都需要处理数据。你当然需要摄取数据，而且数据量异常庞大。全球每两三年产生的数据量是以泽字节计，尽管计算能力并非每两三年就翻倍。
So you know that data processing, you're on the wrong side of that curve already on data processing. If you don't move to accelerated computing, your data processing bills just keep on going up and up and up and up. And so for a lot of companies that recognize this, AstraZeneca, Visa, Amex, Mastercard, so many, so many companies that we work with, they've reduced their data processing expense by 95%, basically 20 times reduction.
所以你知道数据处理，你已经处于数据处理曲线的错误一侧。如果你不转向加速计算，你的数据处理费用将不断增加。为了节省数据处理费用，很多公司已经意识到这一点，像阿斯利康、维萨、美国运通、万事达卡等，我们合作的许多公司减少了95%的数据处理开支，基本上减少了20倍。
To the point the acceleration is so extraordinary now with our suite of libraries called rapids, that the inventor of Spark, who started a great company called Databricks, and they are the cloud large scale data processing company, they announced that they're going to take Databricks their photon engine, which is their crown jewel and they're going to accelerate that with NVIDIA GPUs.
目前，我们的名为 Rapids 的库套件加速得如此惊人，以至于 Spark 的发明人，他创建了一家名为 Databricks 的伟大公司，他们是云大规模数据处理公司，他们宣布他们将利用 NVIDIA GPU 加速他们的皇冠上的明珠 - Databricks 的 Photon 引擎。
Okay. So the benefit of acceleration, of course, pass along savings to your customers, but very importantly, so that you can continue to sustainably compute. Otherwise, you're on the wrong side of that curve. You'll never get on the right side of the curve. You have to accelerate. The question is today or tomorrow? Okay. So accelerated computing. We accelerated algorithms so quickly that the marginal cost of computing has declined so tremendously over the last decade that it enabled this new way of doing software called generative AI.
好的。加速的好处是当然要将节省传递给您的客户，但非常重要的是，这样您才能持续不断地进行计算。否则，您就会落在那条曲线的错误一侧。您永远无法走上正确的一侧。您必须加速。问题是今天还是明天？好的。因此，加速计算。我们加快了算法的速度，以至于过去十年计算的边际成本大大下降，这使得称为生成式人工智能的新软件方式得以实现。
Generative AI, as you know, requires a lot of flops, a lot of flops, a lot of computation. It is not a normal amount of computation, an insane amount of computation. And yet it can now be done cost effectively that consumers can use this incredible service called ChatGPT. So, it's something to consider that accelerated computing has dropped, has driven down the marginal cost of computing so far that enabled a new way of doing something else.
生成式人工智能，就如您所知，需要大量的浮点运算，大量的计算。这不是一般量级的计算，而是疯狂级别的计算。然而，现在可以以成本效益的方式完成这些计算，让消费者能够使用名为ChatGPT的令人难以置信的服务。因此，值得考虑的是，加速计算的成本已经降低到了可以实现一种新的方法的程度。
And this new way is software written by computers with a raw material called data. You apply energy to it. There's an instrument called GPU supercomputers. And what comes out of it are tokens that we enjoy. When you're interacting with ChatGPT, you're getting all -- it's producing tokens.
这种新方式的软件是由一种名为数据的原材料由计算机编写而成的。你向其中应用能量。有一种称为GPU超级计算机的仪器。从中产生的是我们喜爱的“代币”。当你与ChatGPT进行互动时，你得到的是所有这些——它正在生成代币。
Now, that data center is not a normal data center. It's not a data center that you know of in the past. The reason for that is this. It's not shared by a whole lot of people. It's not doing a whole lot of different things. It's running one application 24/7. And its job is not just to save money, its job is to make money. It's a factory.
现在，那个数据中心不是一个普通的数据中心。它不是你过去所熟知的数据中心。原因在于这个数据中心不是由很多人共享的。它不是在进行很多不同的事情。它全天候只运行一个应用程序。它的任务不仅仅是为了节省成本，它的任务是为了赚钱。它就像一个工厂。
This is no different than an AC generator of the last industrial revolution. And it's no different than the raw material coming in is, of course, water. They applied energy to it and turns into electricity. Now it's data that comes into it. It's refined using data processing, and then, of course, generative AI models.
这与上一次工业革命时期的交流发电机没有什么不同。它所处理的原料当然是水。他们向其中输入能量，将其转化为电能。现在，输入的是数据。通过数据处理以及生成式人工智能模型的加工，然后当然将其输出。
And what comes out of it is valuable tokens. This idea that we would apply this basic method of software, token generation, what some people call inference, but token generation. This method of producing software, producing data, interacting with you, ChatGPT is interacting with you.
这样可以产生有价值的代币。我们会将这种基本的软件方法应用到代币生成，有些人称之为推理，但实际上是代币生成。这种产生软件、生成数据并与您互动的方法，就像ChatGPT正在与您互动一样。
This method of working with you, collaborating with you, you extend this as far as you like, copilots to artificial intelligence agents, you extend the idea as long as you like, but it's basically the same idea. It's generating software, it's generating tokens and it's coming out of this thing called an AI generator that we call GPU supercomputers. Does that make sense?
这种与您合作的方法，与您合作，您可以将其延伸到尽可能远的程度，共同操控人工智能代理，您可以随意延伸这个想法的时间，但基本上是相同的想法。它生成软件，生成令牌，它是从我们称之为GPU超级计算机的AI生成器中出来的。这个道理通吗？
And so the two ideas. One is the traditional data centers that we use today should be accelerated and they are. They're being modernized, lots and lots of it, and more and more industries one after another. And so what is a trillion dollars of data centers in the world will surely all be accelerated someday. The question is, how many years would it take to do? But because of the second dynamic, which is its incredible benefit in artificial intelligence, it's going to further accelerate that trend. Does that make sense?
两种观点。一是我们今天使用的传统数据中心应该加速发展，而它们确实在加速。它们正在现代化，一个接一个行业都在现代化。所以世界上的万亿美元数据中心肯定有一天会全部加速发展。问题是，需要多少年才能完成呢？但由于第二种动态，即人工智能带来的巨大好处，它将进一步加速这一趋势。这有意义吗？
However, the second data center, the second type of data center called AC generators or excuse me, AI generators or AI factories, as I've described it as, this is a brand new thing. It's a brand new type of software generating a brand new type of valuable resource and it's going to be created by companies, by industries, by countries, so on and so forth, a new industry.
然而，第二个数据中心，也就是被称为交流发电机或者打扰一下，人工智慧发电机或者人工智慧工厂，就像我所描述的那样，这是一个全新的概念。这是一种全新的软件类型，生成一种全新的有价值的资源，并且将由公司、行业、国家等等创建，这是一个新的产业。
I also spoke about our new platform. People are -- there are a lot of speculations about Blackwell. Blackwell is both a chip at the heart of the system, but it's really a platform. It's basically a computer system. What NVIDIA does for a living is not build the chip. We build an entire supercomputer, from the chip to the system to the interconnects, the NVLinks, the networking, but very importantly the software.
我也提到了我们的新平台。人们--对Blackwell有很多猜测。Blackwell既是系统核心的芯片，但实际上它是一个平台。它基本上是一个计算机系统。NVIDIA的工作不仅仅是构建芯片。我们从芯片到系统再到互连、NVLinks、网络，构建了一整个超级计算机，但非常重要的是软件。
Could you imagine the mountain of electronics that are brought into your house, how are you going to program it? Without all of the libraries that were created over the years in order to make it effective, you've got a couple of billion dollars' worth of asset you just brought into your company.
你能想象一下带进你家的大量电子产品吗？你将如何对其进行编程？没有多年来创建的所有库，用于使其有效，你刚刚引入公司几十亿美元的资产。
And anytime it's not utilized is costing you money. And the expense is too incredible. And so our ability to help companies not just buy the chips, but to bring up the systems and put it to use and then working with them all the time to make it -- put it to better and better and better use, that is really important.
任何时候如果未被利用都会让你损失金钱。而这种开支是相当可观的。因此，我们帮助公司不仅购买芯片，而且还帮助搭建系统并投入使用，然后与他们一直合作，不断改进，让其发挥更好的作用，这是非常重要的。
Okay. That's what NVIDIA does for a living. The platform we call Blackwell has all of these components associated with it that I showed you at the end of the presentation to give you a sense of the magnitude of what we've built. All of that, we then disassemble. This is the hard -- this is the part that's incredibly hard about what we do.
好的。这就是 NVIDIA 的生意。我们称之为 Blackwell 的平台有我在演示结尾向您展示的所有这些组件，让您了解我们已经建立的规模。然后我们把所有这些都拆掉。这就是我们工作中极其困难的部分。
We build this vertically integrated thing, but we build it in a way that can be disassembled later and for you to buy it in parts, because maybe you want to connect it to x86. Maybe you want to connect it to a PCI-Express fabric. Maybe you want to connect it across a whole bunch of fiber, okay, optics.
我们构建这种垂直集成的东西，但我们以一种可以后来拆卸，并让您可以购买零部件的方式来构建它，因为也许您想要将其连接到x86。也许您想要将其连接到PCI-Express总线。也许您想要通过大量光纤连接到整个系统，对，就是光学。
Maybe you want to have very large NVLink domains. Maybe you want smaller NVLink domains. Maybe you can use arm, maybe so on and so forth. Does it make sense? Maybe you would like to use Ethernet. Okay, Ethernet is not great for AI. It doesn't matter what anybody says.
也许你想要很大的NVLink领域。也许你想要较小的NVLink领域。也许你可以使用arm，也许等等。这有意义吗？也许你想使用以太网。好的，以太网不太适合AI。不管别人说什么都无所谓。
You can't change the facts. And there's a reason for that. There's a reason why Ethernet is not great for AI. But you can make Ethernet great for AI. In the case of the ethernet industry, it's called Ultra Ethernet. So in about three or four years, Ultra Ethernet is going to come, it'll be better for AI. But until then, it's not good for AI. It's a good network, but it's not good for AI. And so we've extended Ethernet, we've added something to it. We call it Spectrum-X that basically does adaptive routing. It does congestion control. It does noise isolation.
你无法改变事实。这有其原因。以太网不是很适合人工智能，这是有原因的。但你可以让以太网适合人工智能。在以太网行业中，这就是所谓的Ultra Ethernet。所以大约三到四年后，Ultra Ethernet将推出，对于人工智能来说会更好。但在那之前，它不适合人工智能。以太网是一个良好的网络，但不适合人工智能。因此，我们扩展了以太网，我们给它添加了一些东西。我们称之为Spectrum-X，它基本上进行自适应路由、拥塞控制、噪音隔离。
Remember, when you have chatty neighbors, it takes away from the network traffic. And AI, AI is not about the average throughput. AI is not about the average throughput of the network, which is what Ethernet is designed for, maximum average throughput. AI only cares about when did the last student turn in their partial product? It's the last person. A fundamentally different design point. If you're optimizing for highest average versus the worst student, you will come up with a different architecture. Does it make sense?
记住，当你有爱说话的邻居时，会占用网络流量。而AI则不是关于平均吞吐量的。AI也不是关于网络的平均吞吐量，这正是以太网为设计目的的，具有最大平均吞吐量。AI只关心最后一个学生什么时候交上他们的部分成果？就是最后一个人。这是一个基本不同的设计点。如果你是为了最高平均而不是最差学生来优化，你会想出一个不同的架构。这有意义吗？
Okay. And because AI has all reduce all to all, all gather, just look it up in the algorithm, the transformer algorithm, the mixture of experts algorithm, you'll see all of it. All these GPUs all have to communicate with each other and the last GPU to submit the answer holds everybody back. That's how it works. And so that's the reason why the networking is such a large impact.
好的。因为AI已经将所有变量降至一致，所有聚集，只需要查看算法中的相应部分，变压器算法，专家算法的混合，你都会看到它。所有这些GPU都需要彼此通信，而最后一个提交答案的GPU会拖慢其他所有GPU的速度。这就是运行原理。所以这就是为什么网络通信对性能影响如此巨大的原因。
Can you network everything together? Yes. But will you lose 10%, 20% of utilization? Yes. And what's 10% to 20% utilization if the computer is $10,000? Not much. But what's 10% to 20% utilization if the computer is $2 billion? It paid for the whole network, which is the reason why supercomputers are paid -- are built the way they are. Okay.
可以把所有东西都连接在一起吗？可以。但你会失去10％、20％的利用率吗？会。如果一台计算机值10000美元，10%到20%的利用率意味着什么？不算太多。但如果一台计算机值20亿美元，10%到20%的利用率意味着什么？这就足以支付整个网络，这也是为什么超级计算机被建造的方式。好的。
And so anyways, I showed examples of all these different components and our company creates a platform and all the software associated with it, all the necessary electronics, and then we work with companies and customers to integrate that into their data center, because maybe their security is different, maybe their thermal management is different, maybe their management plane is different, maybe they want to use it just for one dedicated AI, maybe they want to rent it out for a lot of people to do different AI with.
总之，我展示了所有这些不同组件的示例，我们公司创建了一个平台以及与之相关的所有软件，所有必要的电子设备，然后我们与公司和客户合作，将其整合到他们的数据中心中，因为也许他们的安全性不同，也许他们的热管理不同，也许他们的管理层面不同，也许他们只想用它来进行单个专用的人工智能，也许他们想将其出租给许多人来进行不同的人工智能应用。
The use cases are so broad. And maybe they want to build an on-prem and they want to run VMware on it. And maybe somebody just wants to run Kubernetes, somebody wants to run Slurm. Well, I could list off all of the different varieties of environments and it is completely mind blowing.
使用案例如此广泛。也许他们想要构建一个本地部署，然后在上面运行 VMware。也许有人只想运行 Kubernetes，有人想运行 Slurm。我可以列举所有不同类型的环境，这简直令人难以置信。
And we took all of those considerations and over the course of quite a long time, we've now figured out how to serve literally everybody. As a result, we could build supercomputers at scale. But basically what NVIDIA does is build data centers. Okay. We break it up into small parts and we sell it as components. People think as a result, we're a chip company.
通过考虑所有这些因素，经过相当长的时间，我们现在已经弄清楚如何真正为每个人提供服务。因此，我们可以按规模构建超级计算机。但基本上，英伟达所做的是构建数据中心。好的。我们将其分解成小部分，并将其作为组件出售。人们认为，因此我们是一家芯片公司。
The third thing that we did was we talked about this new type of software called NIMs. These large language models are miracles. ChatGPT is a miracle. It's a miracle not just in what it's able to do, but the team that put it so that you can interact with ChatGPT in very high response rate. That is a world class computer science organization. That is not a normal computer science organization.
我们做的第三件事是讨论这种新类型的软件，叫做NIMs。这些大型语言模型是奇迹。ChatGPT是一个奇迹。它不仅仅在于它所能做到的事情上是一个奇迹，而且是团队让你可以以非常高的响应速度与ChatGPT互动。这是一个世界一流的计算机科学组织。这不是一个普通的计算机科学组织。
The OpenAI team that's working on this stuff is world class, is a world class team, some of the best in the world. Well, in order for every company to be able to build their own AI, operate their own AI, deploy their own AI, run it across multiple clouds, somebody is going to have to go do that computer science for them. And so instead of doing this for every single model, for every single company, every single configuration, we decided to create the tools and tooling and the operations and we're going to package up large language models for the very first time.
致力于这项工作的OpenAI团队是世界一流的，是世界级的团队，拥有一流的成员。为了让每家公司能够构建自己的AI、运行自己的AI、部署自己的AI，并在多个云端运行它，某些人不得不为他们进行这项计算机科学工作。因此，我们决定不再为每个模型、每家公司、每个配置分别进行这项工作，而是创建工具和工具链、运营模式，并首次打包大型语言模型。
And you could buy it. You could just come to our website, download it and you can run it. And the way we charge you is all of those models are free. But when you run it, when you deploy it in an enterprise, the cost of running it is $4,500 per GPU per year. Basically, the operating system of running that language model.
您可以购买它。您只需访问我们的网站，下载它，然后便可运行它。我们的收费方式是所有这些模型都是免费的。但是，当您运行它时，将其部署在企业中时，每块GPU每年运行的成本为$4,500。基本上，这是运行语言模型的操作系统。
Okay. And so the per instance, the per-use cost is extremely low. It's very, very affordable. And -- but the benefit is really great. Okay. We call that NIMs, NVIDIA Inference Microservices. You take these NIMs and you're going to have NIMs of all kinds. You're going to have NIMs of computer vision. You're going to have NIMs of speech and speech recognition and text to speech and you're going to have facial animation. You're going to have robotic articulation. You're going to have all kinds of different types of NIMs.
好的。所以每个实例，每次使用的成本非常低廉。非常实惠。而且好处真的非常大。我们把它叫做 NIMs，即 NVIDIA 推理微服务。您拿这些 NIMs，您将拥有各种不同类型的 NIMs。您将拥有计算机视觉的 NIMs。您将拥有语音和语音识别的 NIMs，以及文本转语音的 NIMs。您将拥有面部动画。您将拥有机器关节活动。您将拥有各种不同类型的 NIMs。
These NIMs, the way that you would use it is you would download it from our website and you would fine tune it with your examples. You would give it examples. You say the way that you responded to that question isn't exactly right. It might be right in another company, but it's not right in ours. And so I'm going to give you some examples that are exactly the way we would like to have it. You show it your work products. This is the way -- this is what a good answer looks like. This is what right answer looks like, whole bunch of them.
这些NIMs的使用方法是您可以从我们的网站上下载它，并使用您的示例进行微调。您可以提供示例。您可以说您对那个问题的回答并不完全正确。这在另一家公司可能是正确的，但在我们这里是不正确的。因此，我将给您一些确切符合我们要求的示例。您展示您的工作成果。这就是一个好回答的样子。这就是正确答案的样子，有很多例子。
And we have a system that helps you curate that process that tokenize that, all of the AI processing that goes along with it, all the data processing that goes along with it, fine tuning that, evaluate that, guardrail that so that your AIs are very effective, number one, also very narrow.
我们有一个系统，可以帮助您策划那个流程、对其进行令牌化，管理整个与此相关的AI处理，所有与此相关的数据处理，对其进行微调，评估它，设定防护措施，以确保您的人工智能非常有效，首先，也非常专精。
And the reason why you want it to be very narrow is because if you're a retail company, you would prefer your AI just didn't pontificate about some random stuff, okay. And so whatever the questions are, it guardrails it back to that lane. And so that guard railing system is another AI. So, we have all these different AIs that help you customize our NIMs and you could create all kinds of different NIMs.
你希望它保持非常狭窄的原因是，如果你是一家零售公司，你更希望你的 AI 不要随意进行某些主题的演讲。无论问题是什么，它都会将其引导回到这个范围。所以这种引导系统是另一个 AI。因此，我们有各种不同的 AI 来帮助你定制我们的 NIMs，你可以创建各种不同的 NIMs。