Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat
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I asked Jensen about TPU competition, Nvidia’s lock on the ever more bottlenecked supply chain needed to make advanced chips, whether we should be selling AI chips to China, why Nvidia doesn’t just become a hyperscaler, how it makes its investments, and much more. Enjoy!Watch on YouTube; read the transcript.Sponsors* Crusoe’s cloud runs on state-of-the-art Blackwell GPUs, with Vera Rubin deployment scheduled for later this year. But hardware is only part of the story—for inference, Crusoe’s MemoryAlloy tech implements a cluster-wide KV cache, delivering up to 10x faster TTFT and 5x better throughput than vLLM. Learn more at crusoe.ai/dwarkesh* Cursor helped me build an AI co-researcher over the course of a weekend. Now I have an AI agent that I can collaborate with in Google Docs via inline comment threads! And while other agentic coding tools feel like a total black-box, Cursor let me stay on top of the full implementation. You can try my co-researcher out at github.com/dwarkeshsp/ai_coworker, or get started on your own Cursor project today at cursor.com/dwarkesh* Jane Street spent ~20,000 GPU hours training backdoors into 3 different language models, then challenged my audience to find the triggers. They received some clever solutions—like comparing the base and fine-tuned versions and extrapolating any differences to reveal the hidden backdoor—but no one was able to solve all 3. So if open problems like this excite you, Jane Street is hiring. Learn more at janestreet.com/dwarkeshTimestamps(00:00:00) – Is Nvidia’s biggest moat its grip on scarce supply chains?(00:16:25) – Will TPUs break Nvidia’s hold on AI compute?(00:41:06) – Why doesn’t Nvidia become a hyperscaler?(00:57:36) – Should we be selling AI chips to China?(01:35:06) – Why doesn’t Nvidia make multiple different chip architectures? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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中英文字稿 
我们观察到许多软件公司的估值大幅下跌，因为人们预计AI会使软件变得普通化。然而，有一种可能过于简单的思维方式，那就是像这样：NVIDIA将一个GDS2文件发送给台积电，台积电制造逻辑芯片、开关，然后与SK海力士、美光和三星生产的HBM一起封装。之后，它被发送到台湾的ODM组装成机架。因此，NVIDIA实际上是在创造其他人制造的软件产品。如果软件被大众化，NVIDIA会因此而被大众化吗？然而，最终，总需要有某种东西将电子信号转化为代币。而这些电子信号到代币的转化，包括让这些代币随着时间更有价值的过程，我认为这是很难完全被大众化的。
▶ 英文原文 ⏱
We've seen the valuations of a bunch of software companies crash because people are expecting AI to commoditize software. And there's a potentially naive way of thinking about things, which is like, look, NVIDIA sends a GDS2 file to TSMC. TSMC builds the logic dies, it builds the switches, then it packages them with the HBM that SK Hynix and Micron and Samsung make. Then it sends it to an ODM in Taiwan where they assemble the racks. And so NVIDIA is fundamentally making software that other people are manufacturing. And if software gets commoditized, does NVIDIA get commoditized? Well, in the end, something has to transform electrons to tokens. That transformation, there's no, the transformation of electrons to tokens and making those tokens more valuable over time. I don't, I think that that, that's hard to, hard to completely commoditize.
从电子到代币的转变是一段令人难以置信的旅程。制造代币就像是让一个分子比另一个分子更有价值，让一个代币比另一个更有意义。这种转变、制造过程及其背后的科学原理仍未被深入理解，这段旅程也远未结束。因此，我怀疑这种情况会发生变化。我们当然会努力提高效率。事实上，NVIDIA的整个理念就如同您提问时设想的那样：输入是电子，输出是代币，而NVIDIA就是连接中间的那个环节。我们的任务是尽可能高效地促成这种转变，以实现卓越的能力。
▶ 英文原文 ⏱
The transformation from electrons to tokens is such an, such an incredible journey and, and making that token, you know, it's like making one molecule more valuable than another molecule, making one token more valuable than another. And so, so the, the, the, the, the, the, the, the, the transformation, the manufacturing, all of the science that goes in there is far from deeply understood and it's far from, the journey is far from, far from over. And so, so I, I, I doubt that it will happen. We're going to make it more efficient, of course. I mean, the whole, the whole thing about NVIDIA, in fact, the way that you framed the question is, is my mental model of our company. The input is electron, the output is tokens. That is in the middle NVIDIA. And our job is to, to do as much as necessary, as little as possible to enable that transformation to be done at incredible capabilities.
我的意思是，我尽量减少自己需要做的事情。对于我不需要亲自去做的事情，我会与其他人合作，并将其纳入我的生态系统来完成。看看今天的NVIDIA，我们可能拥有最大的合作伙伴生态系统，包括供应链上下游的合作伙伴、所有计算机公司、应用程序开发者和模型制造商等。AI可以被形容为一个五层的蛋糕，而我们在所有五个层级都有对应的生态系统。因此，我们尽量少做不必要的事情，但我们必须做的那部分，实际上是非常困难的。而且我认为这部分不会被商品化。
▶ 英文原文 ⏱
And, and what I mean by as little as possible, whatever I don't need to do, I partner with somebody and I make it part of my ecosystem to do. And if you look at NVIDIA today, we probably have the largest ecosystem of partners, both in supply chain upstream, supply chain downstream, all of the computers, computer companies, and all the application developers, and all the model makers, and all the, you know, AI is a five-year, five-layer cake, if you will. And, and we have ecosystems across the entire five layers. And, and so we try to do as little as possible, but the part that we have to do, as it turns out, is insanely hard. And, and I don't think that that gets commoditized.
事实上，我其实也不认为那些企业软件公司，那些工具制造商，你知道，现在大多数软件公司其实都是工具制造商。有些不是，但有些是流程编码的系统。对于很多公司来说，它们就是工具制造商。比如说，Excel 是一种工具，PowerPoint 是一种工具，Cadence 制造工具，Synopsys 制造工具。我实际上看到了和别人不同的观点。我认为，代理的数量将会呈指数级增长，工具用户的数量也将呈指数级增长，所有这些工具的使用实例数量很可能会飙升。
▶ 英文原文 ⏱
In fact, in fact, I also don't think that the, the enterprise software companies, the tools makers, you know, most of the software companies today are tools makers. Some of them are not, but are, some of them are workflow codification, you know, systems. But for a lot of companies, they're tool makers. For example, you know, Excel is a tool, PowerPoint's a tool, Cadence makes tools, Synopsys makes tools. I, I actually see the opposite of what people see. I think the number of agents are going to grow exponentially. The number of tool users are going to grow exponentially. And it's very likely that the number of instances of all these tools are going to skyrocket.
很可能Synopsys设计编译器的使用实例数量将大幅增加。而使用楼层规划工具、所有布局工具和设计规则检查工具的代理数量也将激增。今天，这些代理的使用受到工程师数量的限制。明天，这些工程师将得到一批代理的支持，我们将以前所未有的方式探索设计空间，使用我们今天所用的工具。因此，我认为工具的使用将推动软件公司的快速增长。之所以这一切尚未发生，是因为这些代理在使用工具方面还不够成熟。
▶ 英文原文 ⏱
It is very likely the number of instances of Synopsys design compiler is going to skyrocket. And the number of, number of agents that are going to be using the floor planners and all of our layout tools and our design, design rule checkers, the number of agents that are, today we're limited by the number of engineers. Tomorrow, those engineers are going to be supported by a bunch of agents and we're going to be exploring the design space like you've never seen explored before and want to use the tools that we use today. And so, so I think, I think tool use is going to cause, cause the software companies to skyrocket. The reason why it hasn't happened yet is because the agents aren't good enough at using their tools yet.
因此，这些公司要么亲自创建代理，要么代理将足够强大，可以使用这些工具。我认为这两者将相结合。在你们最新的申报文件中，你们与代工厂、存储器、封装等方面的合同承诺金额接近一千亿美元，然后据半导体分析报告称，你们将会有两千五百亿美元的此类合同承诺。因此，一种解释是，NVIDIA 的模式实际上是通过长期锁定这些稀缺的组件，即便其他人有加速器，但他们能否真正获得构建所需的存储器和逻辑组件？
▶ 英文原文 ⏱
And so either these companies are going to build the agents themselves or agents are going to get good enough to be able to use those tools. And I think it's going to be a combination of both. I think in your latest filings, it was, you had almost a hundred billion dollars in purchase commitments with people, foundries, memory, packaging, and then semi-analysis has reported that you will have $250 billion of these kinds of purchase commitments. And so one interpretation is NVIDIA's mode is really that you've locked up many years of these scarce components that are ever, you know, somebody else might have an accelerator, but can they actually get the memory to build it? Can they actually get the logic to build it?
这实际上是接下来几年中，NVIDIA的主要策略之一。这是我们能够做到，而其他人很难实现的一件事。我们之所以能做到，是因为我们在产业链上游作出了巨大的承诺。有些承诺是显而易见的，正如你提到的那些。而有些则是不那么明显的。例如，我们的供应链已经进行了很多上游投资，因为我向各大公司的CEO们解释了这个行业将会有多大，并具体说明了原因，与他们一起分析推理，并展示了我所看到的前景。由于这个告知、激励和与不同行业的CEO们达成一致的过程，他们愿意进行这些投资。那么，为什么他们愿意为我进行投资，而不是为别人呢？
▶ 英文原文 ⏱
And this is really NVIDIA's big mode for the next few years. Well, it's one, it's one of the things that we can do that is hard for someone else to do. The reason why we could, we've made enormous commitments upstream. Some of it is explicit, these commitments that you mentioned. Some of it is implicit. For example, a lot of the investments that are upstream are made by our supply chain, because I said to the CEOs, let me tell you how big this industry is going to be. And let me explain to you why. And let me reason through it with you. And let me show you what I see. And so as a result of that, that process of informing, inspiring, aligning with CEOs of all different industries upstream, they're willing to make the investments. Now, why are they willing to make the investments for me and not someone else?
原因是他们知道我有能力购买他们的产品，然后通过我的下游渠道进行销售。由于NVIDIA拥有强大的下游供应链，而且我们的下游需求非常大，这促使他们愿意在上游进行投资。如果你看GTC，人们都会对其规模和参会人员感到惊叹。GTC是一个360度全方位的人工智能盛会，将整个AI领域汇聚于一处。大家集合在一起是因为他们需要彼此交流。我将他们聚在一起，使下游能够看到上游，上游能够看到下游，所有人都能看到AI领域的最新进展。
▶ 英文原文 ⏱
And the reason for that is because they know that I have the capacity to buy it, buy their supply and sell it through my downstream. The fact that NVIDIA has downstream supply chain and our downstream demand is so large, they're willing to make the investment upstream. And so if you look at GTC and, you know, people are marveled by the scale of GTC and the people that go. It's a 360 degree is that the entire universe of AI all in one place. And they're all in one place because they need to see each other. I bring them together so that the downstream could see the upstream, the upstream could see the downstream, and all of them could see all the advances in AI.
非常重要的是，他们可以亲身接触所有的AI新生代和AI初创公司，了解那些正在建设中的项目和发生的惊人变化，这样他们就能亲眼看到我所描述的一切。因此，我花费大量时间直接或间接地向我们的供应链、合作伙伴和生态系统传达我们面临的机遇。你知道，在我大多数的演讲中，有些人总是说，Jensen，你的演讲就像是一个接一个的公告，一直不断。
▶ 英文原文 ⏱
And very importantly, they can all meet the AI natives and all the AI startups that are all, you know, being built and all the amazing things that are happening so that they could see firsthand all the things that I tell them. And so I spend a lot of my time informing directly or indirectly our supply chain and our partners and our ecosystem about the opportunity that's in front of us. You know, most of my keynotes, you know, some people always say, you know, Jensen, in most keynotes, it's like one announcement after another announcement, after another announcement, after another announcement.
我们的主题演讲往往包含一些让人感到有些折磨的内容，因为这部分内容几乎就像教育。而实际上，这正是我所关注的重点。我需要确保供应链的上下游以及整个生态系统都能够理解即将发生的事情、为何会发生、何时发生、规模有多大，并且能够像我一样系统地进行思考。因此，按照你描述的模式，如果我们未来几年的规模达到一万亿美元，我们将有能力构建相应的供应链来支持这一目标。
▶ 英文原文 ⏱
Our keynotes are, there's always a part of it that's a little torturous in the sense that it's almost comes across like an edge, like education. And in fact, that's exactly on my mind. I need to make sure that the entire supply chain upstream and downstream, the ecosystem understands what is coming at us, why it's coming, when it's coming, how big is it going to be, and be able to reason about it systematically, just like I reason about it. And so I think the mode as you describe it, we're able to, of course, build for a future, if our next several years is a trillion dollars in scale, we have the supply chain to do it.
如果没有我们的业务覆盖面和速度，就像现金流一样，需要有供应链流动和周转。如果某个架构的业务周转很低，没有人会为其建立供应链。因此，我们能够维持大规模运营，只是因为我们下游的需求非常强劲，大家都看到这一点，也都听说过，他们都能看到这一切即将到来。这使得我们能够按照这样的规模来进行我们正在做的事情。
▶ 英文原文 ⏱
Without our reach, the velocity of our business, you know, just as there's cash flow, there's supply chain flow, there turns. Nobody's going to build a supply chain for an architecture if the architecture, the business turns is low. And so our ability to sustain the scale is only because our downstream demand is so great, and they see it, and they all hear about it, they see it all coming. And so that allows us to do the things that we're able to do at the scale we're able to do.
我确实想更具体地了解上游是否能够跟上。多年来，你们的收入一直每年翻倍，你们提供给世界的计算能力（FLOPS）每年增长超过三倍。在现有规模上实现翻倍，真是令人难以置信。没错。那么再看看逻辑，你们是台积电N3制程的最大客户，也是N2的主要客户之一。今年AI整体将占N3产能的60%。
▶ 英文原文 ⏱
I do want to understand more concretely whether the upstream can keep up. For many years now, you guys have been 2x-ing revenue year over year. You guys have been more than tripling the amount of flops you're providing to the world year over year. And 2x-ing at the scale now, it's really incredible. Exactly. Yeah. So then you look at logic, say. You're the biggest customer on TSMC's N3 node, and you're one of the biggest on N2. AI as a whole this year is going to be 60% of N3.
根据一些分析，明年将达到86%。如果你已经占据大多数，如何实现翻倍增长呢？而且如何年复一年地做到这一点呢？所以我们现在是否处于一个阶段，AI计算的增长速度因为上游原因而不得不放缓？你是否看到了解决这些问题的方法，比如我们如何每年建造两倍的工厂？实际上，从某种程度上来说，瞬时的需求超过了上下游的供给。可能在某个时刻，我们会因为水管工数量不够而受到限制，这种情况确实会发生。
▶ 英文原文 ⏱
It's going to be 86% next year, according to some analysis. How do you 2x if you're the majority? And how do you do that year over year? So are we in a regime now where the growth rate in the AI compute has to slow because of upstream? Do you see a way to get around these, you know, how do we build 2x more fabs year over year, ultimately? Yeah, at some level, the instantaneous demand is greater than the supply upstream and downstream in the world. And it could be at any instance, we could be limited by the number of plumbers, which actually happens.
水管工被接纳参加明年的GTC。顺便说一句，这是个好主意。而且这是个不错的条件。你想要一个市场，一个行业，在那里瞬时需求大于行业的总供应量。显然，相反的情况就不太好。如果我们之间的距离太远，如果某个特定项目或组件离得太远，显然整个行业将会蜂拥而上。所以，比如说，我注意到人们现在不太谈论共同作者了。是的。
▶ 英文原文 ⏱
The plumbers are admitted to next year's GTC. You know, by the way, great idea. But that's a good condition. You want a market, you want an industry where the instantaneous demand is greater than the total supply of the industry. The opposite is obviously less good. If we're too far apart, if one particular item, one particular component is too far away, obviously the industry swarms it. So, for example, I noticed people aren't talking very much about co-auths anymore. Yeah.
原因是因为在过去的两年里，我们对这个领域倾注了大量精力，并且在多个方面实现了翻倍增长。现在我认为我们处于一个相当不错的状态。台积电也意识到，互连供应必须跟上其他逻辑芯片和内存的需求。因此，他们在扩展互连技术和未来封装技术方面，与逻辑芯片的扩展保持同等水平，这是非常好的。因为在很长一段时间里，互连技术和HBM内存算是比较特定的技术。但它们现在不再是特定领域的技术了。大家现在都意识到，它们是主流的计算技术。
▶ 英文原文 ⏱
And the reason for that is because for two years, we swarmed a living daylights out of it. And we double, double, double on several doubles. And now I think we're in a fairly good shape. And TSMC now knows that co-auth supply has to keep up with the rest of the logic demand and the memory demand. And so they're scaling co-auths and they're scaling future packaging technologies at the same level as they scale logic, which is terrific. Because for a long time, co-auths was rather specialty and HBM memory was rather specialty. But they're not specialties anymore. People now realize they're mainstream computing technology.
当然，现在我们对于更大范围的供应链有了更强的影响力。在过去，当人工智能革命刚刚开始时，我现在说的这些事情其实五年前我就提到过。当时，有些人相信并对其进行了投资。例如，Sanjay和美光团队，我仍然清楚地记得那次会议，在会上我明确地说了将会发生什么、为什么会发生以及今天的预测。他们对此坚定支持，并在LPDDR和HBM存储等方面与我们进行合作。他们确实投入了大量精力，这对公司显然带来了巨大的益处。
▶ 英文原文 ⏱
And then, of course, we're now much more able to influence a larger scope of our supply chain. In the past, in the beginning of the AI revolution, all the things that I say now, I was saying five years ago. And some people believed in it and invested in it. For example, Sanjay and the Micron team, I still remember the meeting really well, where I was clear about exactly what's going to happen and why it's going to happen and the predictions of today. And they really doubled down on it. And we partnered with them across LPDDR, across HBM memories. They really invested in it. And it obviously has been tremendous for the company.
有些人来得稍微晚了一些，但现在他们都到了。因此，我认为每一个瓶颈问题都得到了极大的关注。如今，我们提前几年就开始预见这些瓶颈。例如，过去几年我们与Lumentum和Coherent以及整个硅光子生态系统的投资，彻底重塑了硅光子的生态系统和供应链。我们围绕台积电建立了完整的供应链，与他们在Coop方面合作，发明了很多技术。我们将这些专利授权给供应链，保持其开放性。
▶ 英文原文 ⏱
Some people came a little bit later, but now they're all here. And so I think each one of these bottlenecks gets a great deal of attention. And now we're prefetching the bottlenecks years in advance. So, for example, the investments that we've done with Lumentum and Coherent and all of the silicon photonics ecosystem, the last several years, we really reshaped the ecosystem and the supply chain of silicon photonics. We built up an entire supply chain around TSMC. We partnered with them on Coop, invented a whole bunch of technology. We licensed those patents to the supply chain, keep it nice and open.
我们正在通过发明新技术、新的工作流程和新的测试设备、双面探针以及投资公司、帮助它们扩大产能来准备供应链。因此，你可以看到我们正在努力塑造生态系统，使其能够为供应链做好准备，以支持大规模扩张。有些瓶颈比其他瓶颈更容易解决。扩大 Coop 规模与扩大其他方面的规模相比，后者更困难。我提到了最难的一个领域。那是什么呢？管道工。是的，确实如此。
▶ 英文原文 ⏱
And so we're preparing the supply chain through invention of new technologies, new workflows, new testing equipment, double-sided probing, investing in companies, helping them scale up their capacity. And so you could see that we're trying to shape the ecosystem so that it's ready, the supply chain, so that it's ready to support the scale. It seems like some bottlenecks are easier than others. And so scaling up Coop versus scaling up. I went to the hardest one, by the way. Which is? Plumbers. Yeah. It's true.
是的，我其实去了最难的一个领域。是的，是的，就是水管工和电工。原因是这样的：我对那些关于工作终结和失业问题的悲观论者有一些担忧。比如，如果我们不鼓励人们去做软件工程师，我们就会面临软件工程师短缺的风险。类似的预测在十年前一些悲观论者就已经提出过，他们那时告诉大家，无论做什么，都不要成为放射科医生。可能你现在还能在网上找到一些这样的言论。
▶ 英文原文 ⏱
Yeah, I actually went to the hardest one. Yeah. Yeah, plumbers and electricians. And the reason for that is because. And this is one of the concerns that I have about the doomers, describing the end of work and killing of jobs. And one of the things that if we discourage people from being software engineers, we're going to run out of software engineers. And the same prediction 10 years ago, some of the doomers were saying that, were telling people, whatever you do, don't be a radiologist. And you might hear some of those videos are still on the web.
你知道，放射学将是第一个被淘汰的职业。世界将不再需要放射科医生。不过你猜怎么着？我们其实缺放射科医生。但是，好吧。回到这个问题上，有些东西可以扩展，而另一些东西，比如，你如何实现每年制造双倍的逻辑量？归根结底，这是受限于的。存储器和逻辑受限于紫外光刻。你如何在一年内制造出双倍的紫外光刻机？对，每年。所有这些都不是能快速扩展的。
▶ 英文原文 ⏱
You know, radiology is going to be the first career to go. The world's not going to need any more radiologists. Guess what we're short of? Radiologists. Oh, but okay. So going back to this point about, well, some things you scale, other things like, how do you actually get. How do you actually manufacture 2x the amount of logic a year? Ultimately, that's bottlenecked by. Memory and logic are bottlenecked by UV. How do you get to 2x as many UV machines a year? Yeah. Year over year. None of that's impossible to scale quickly.
你只需要这样做。你可以做到。这些事情在两三年内都很容易实现。你只需要有个需求信号。一旦你能制造一个，你就能制造10个。然后你就能制造一百万个。所以这些东西并不难复制。你深入供应链多深呢？你会去找ASML公司说：“嘿，我展望未来三年，为了让NVIDIA每年能产生2万亿的收入，我们需要更多的AUV机器。”
▶ 英文原文 ⏱
You just need to. You could do. All of that is easy to do within two or three years. You just need a demand signal. It's not. Once you can build one, you can build 10. And once you can build 10, you can build a million. And so these things are not hard to replicate. How far down the supply chain do you go where you. Do you go to ASML and say, hey, if I look out three years from now, for me to. For NVIDIA to be generating 2 trillion in a year in revenue, we need way more AUV machines.
其中有些事情我必须直接处理，有些则是间接处理。有些事情，如果我能说服台积电，那么阿斯麦也会被说服。这就是我们需要考虑关键瓶颈的原因。但如果台积电被说服了，你在几年内就会有充足的EUV机器。因此，我的观点是，没有任何瓶颈会持续超过两三年。一个也不会。
▶ 英文原文 ⏱
And some of them I have to directly. Some of them are indirectly. And some of them. If I can convince TSMC, ASML will be convinced. And so that's. You know, we have to think about the critical pinch points. But if TSMC is convinced, you'll have plenty of EUV machines in a few years. And so none of that. My point is that none of the bottlenecks last longer than a couple, two, three years. None of them.
同时，我们正在提高计算效率，实现10倍、20倍的提升。在从Hopper到Blackwell的情况下，效率提升了30倍到50倍。由于CUDA的灵活性，我们正在开发全新的算法，并利用各种新技术来不仅提升效率，还增加处理能力。因此，这些方面我一点都不担心。我担心的是我们之后的事情，比如限制能源的发展政策。没有能源，你无法推动增长，也无法创造一个新的制造业。
▶ 英文原文 ⏱
And meanwhile, we're improving computing efficiency by 10X, 20X. In the case of Hopper to Blackwell, some 30, 50X. We're coming up with new algorithms because CUDA is so flexible. We're developing all kinds of new techniques so that we drive efficiency in addition to increasing capacity. And so those are things that none of that worry me. It's the stuff that's downstream from us. Energy policies that prevent energy from. You know, you can't grow. You can't create an industry without energy. You can't create a whole new manufacturing industry without energy.
我们想让美国重新工业化。我们希望带回芯片制造、电脑制造和包装业务。此外，我们想打造新的产品，如电动汽车和机器人，并建立人工智能工厂。而这些都离不开能源支持。这些过程需要很长时间，但增加芯片产能通常需要两到三年，增加合作产能也需要两到三年。有趣的是，我有时听到一些客人告诉我完全相反的观点。在这种情况下，我缺乏技术知识来判断。不过，好在你现在是在跟专家交流。
▶ 英文原文 ⏱
We want to re-industrialize the United States. We want to bring back chip manufacturing and computer manufacturing and packaging. And we want to build new things like EVs and robots. And we want to build AI factories. And you can't build any of these things without energy. And those things take a long time. But more chip capacity, that's a two, three-year problem. More co-op capacity, two, three-year problem. Interesting. I feel like I have guests tell me the exact opposite thing sometimes. And in this case, I just don't have the technical knowledge to adjudicate, but. Well, the beautiful thing is you're talking to the expert.
好的。对，对。嗯，我想问一下关于你们的竞争对手。如果看一下TPU（张量处理单元），可以说世界上排名前三的模型中，有两个是用TPU训练的，例如Claude和Gemini。这对于NVIDIA未来的发展意味着什么呢？
好吧，我们与他们有很大的不同。NVIDIA开发的技术是加速计算，而不是张量处理单元。加速计算被用于各种用途，比如分子动力学和量子色动力学。它也被用于数据处理，包括数据框架、结构化数据和非结构化数据。同样，也应用于流体动力学和粒子物理学。你知道的，就是这样。
▶ 英文原文 ⏱
Yeah. True, true. Okay. I want to ask about your competitors. Yeah. So if you look at TPU, arguably two out of the top three models in the world, Claude and Gemini, were trained on TPU. What does that mean for NVIDIA going forward? Well, we have a very different. We build a very different thing. You know, what NVIDIA built is accelerated computing, not a tensor processing unit. And accelerated computing is used for all kinds of things. You know, molecular dynamics and quantum chromodynamics. And it's used for data processing, data frames, structured data, unstructured data. It's used for fluid dynamics, particle physics. You know?
此外，我们也将其用于人工智能。因此，加速计算的应用更加多样化。尽管今天关于人工智能的讨论显然非常重要且具有影响力，但计算的范围远不止于此。NVIDIA所做的事情是将传统的通用计算重新发明为加速计算。我们的市场覆盖范围远远超过任何TPU或ASIC。因此，如果你看看我们的地位，我们是唯一能够加速各种应用程序的公司。我们拥有一个庞大的生态系统，各种框架和算法都可以在NVIDIA上运行。
▶ 英文原文 ⏱
And in addition, we use it for AI. And so accelerated computing is much more diverse. And although AI is the conversation today is obviously very important and impactful, computing is much broader than that. And what NVIDIA has done is reinvented the way computing is done from general-purpose computing to accelerated computing. Our market reach is far greater than any TPU, any ASIC can possibly have. And so if you look at our position, we're the only company that accelerates applications of all kinds. We have a gigantic ecosystem. And so all kinds of frameworks and algorithms all run on NVIDIA.
我们的计算机是专为他人操作而设计的，所以任何操作员都可以购买我们的系统。而大多数自制系统都是需要用户自己操作的，因为它们从未被设计成足够灵活，供他人使用。因此，由于我们系统的易操作性，我们的产品已经遍布各大云服务，包括谷歌、亚马逊、Azure和OCI。不管你是想租用操作还是自行操作，如果你打算租用操作，那你最好拥有一个庞大的客户生态系统。
▶ 英文原文 ⏱
And because our computers are designed to be operated by other people, anyone who's an operator could buy our systems. Most of these home-built systems, you have to be your own operator because it was never designed to be flexible enough for other people to operate. And so as a result of the fact that anybody can operate our systems, we're in every cloud, including Google and Amazon and, you know, Azure and OCI, right? And so whether you want to operate it to rent or operate it, if you want to operate it to rent, you better have large ecosystem of customers.
在许多行业中，如果你是终端用户，并且希望自己运营，我们显然有能力帮助你实现自主运营。例如，埃隆·马斯克和他的 XAI。由于我们可以支持任何公司、任何行业的运营者，你可以利用我们的支持为礼来公司（Lilly）等建立用于科学研究和药物发现的超级计算机。因此，我们可以帮助他们运营自己的超级计算机，并将其用于加速药物发现和生物科学的多样化研究。
▶ 英文原文 ⏱
In many industries that be the off-takers, if you're operating it, if you want to operate it for yourself, you know, we obviously have the ability to help you operate yourself, like, for example, for Elon with XAI. And because we could enable operators in any company, in any industry, you could use it to build a supercomputer for scientific research and drug discovery at Lilly. And so we can help them operate their own supercomputer and use it for the entire diversity of drug discovery and biological sciences that we accelerate.
因此，有很多应用程序是 TPU 无法解决的，因为 NVIDIA 开发的 CUDA 是一个出色的张量处理单元。CUDA 不仅仅是一个处理器，它涵盖了数据处理、计算、人工智能等各个生命周期。因此，我们的市场机会要大得多，我们的影响力也更广泛。由于我们现在几乎支持世界上的每一个应用程序，所以无论在哪里构建 NVIDIA 系统，都可以确信会有客户使用这些系统。
▶ 英文原文 ⏱
And so there are just, you know, a whole bunch of applications that we can address that you can't do so with TPUs. Because NVIDIA has built CUDA as a fantastic tensor processing unit as well, but it does, you know, it does every life cycle of data processing and computing and AI and so on and so forth. And so our market opportunity is just a lot larger. Our reach is a lot greater. And because we have such a large, we basically support every application in the world now, you could build NVIDIA systems anywhere and know that there will be customers for it.
因此，这是完全不同的事情。接下来我要问一个有点长的问题，你们有惊人的收入。而这些收入，大部分并不是来自制药和量子领域的每季度600亿美元。你们之所以能取得这样的收入，是因为人工智能是前所未有的技术发展，非常快速。那么问题来了，什么才是对人工智能最有利的？虽然我不太了解具体细节，但我和搞人工智能研究的朋友交流时，他们说：当我使用TPU（张量处理单元）时，它是一个大型的时序阵列，非常适合进行矩阵乘法运算。
▶ 英文原文 ⏱
And so it's a very different thing. This is going to be sort of a long question, but, you know, you have spectacular revenue. And this revenue is mostly, you're not making $60 billion a quarter from pharma and quantum. You're making it because AI is unprecedented technology that is going unprecedentedly fast. And so then the question is, what is best for AI specifically? And I'm not in the details, but I talk to my AI researcher friends and they say, look, when I use a TPU, it's this big systolic array that's perfect for doing matrix multiplies.
相较之下，GPU非常灵活。在需要大量分支处理和不规则内存访问时，它的表现非常出色。然而，AI呢？主要涉及的是不断重复的、非常可预测的矩阵乘法操作。而且，TPU无需为执行集群、线程切换以及内存交换留出芯片面积，因此真正针对当前在线的大量计算任务进行了优化。这种优化正适合当前大部分的增长需求和使用场景，从而推动了收入的增长。
▶ 英文原文 ⏱
Whereas a GPU is very flexible. It's great when you have lots of branching, when you have irregular memory access. But these, you know, what is AI? Just like these very predictable matrix multiplies again and again and again. And you don't have to give up any die area for warp schedulers, for, you know, switches between threads and memory banks. And so the TPU is really optimized for the majority, the bulk of this growth in revenue and use case for a compute that is coming online right now.
是啊，我想知道你对此有何反应。矩阵乘法是人工智能中的一个重要部分，但它并不是唯一的部分。如果你想提出一种新的注意力机制，或者以不同的方式进行分解，或者想要创造一种完全新的架构，例如混合SSM。如果你想创建一个结合扩散和自回归的模型，那么你需要一个通用可编程的架构。我们可以运行你能想象到的所有内容，这就是它的优势。它使得新算法的发明变得更简单得多。因为这是一个可编程的系统，发明新算法的能力正是让人工智能迅速进步的关键所在。
▶ 英文原文 ⏱
Yeah, I wonder how you react to that. Matrix multiplies is an important part of AI, but it's not the only part of AI. And if you want to come up with a new attention mechanism or if you want to disaggregate in a different way, if you want to come up with a whole new type of architecture altogether, for example, you know, a hybrid SSM. If you want to create a model that fuses diffusion and autoregressive somehow, you want an architecture that's just generally programmable. And, and we run everything you can imagine. And so that's the advantage. It allows for invention of new algorithms a lot more, a lot, a lot more easily. And so, because it's a programmable system. And, and the ability to invent new algorithms is really what makes AI advance so quickly.
你知道的，TPU（张量处理单元）和其他技术一样，也受到摩尔定律的影响。我们知道摩尔定律的增长速度大约是每年25%。所以，要实现10倍甚至100倍的提升，唯一的方法是每年从根本上改变算法及其计算方式。这也是NVIDIA的核心优势所在。我们能够让Blackwell的能效比Hopper提升50倍的唯一原因就是这个。我最初说Blackwell的能效比Hopper高35倍时，没人相信。然后Dylan写了一篇文章，说实际上我保守估计了，能效提升实际上是50倍。仅仅依靠摩尔定律是不可能合理实现这样的增长的。
▶ 英文原文 ⏱
You know, TPUs, like anything else, is impacted by Moore's Law. And we know that Moore's Law is increasing about 25% per year. And so the only way to really get 10x leaps, 100x leaps, is to fundamentally change the algorithm and how it's computed every single year. And that's NVIDIA's fundamental advantage. The only reason why we were able to make Blackwell the Hopper 50 times, you know, I said it was 35 times. And, and, and when I first announced it was going to, Blackwell is going to be 35 times more energy efficient than Hopper, nobody believed it. And, and, and then Dylan wrote an article. He said, he said, in fact, in fact, I sandbagged, it's actually 50 times. And you can't reasonably do that with just Moore's Law.
我们解决这个问题的方法是通过新的模型、新的专家混合模型（MOE）来实现，这些模型是在计算系统中进行并行化、解耦和分布式处理的。没有CUDA的支持，很难深入研究并设计新的核心算法。我们架构的可编程性，加上NVIDIA作为一个极致协同设计公司的特点，让我们可以将部分计算任务卸载到网络结构中，比如通过MVLink和Spectrum X进入网络。此外，我们能够在处理器、系统、网络结构、库和算法之间同时进行变革。如果没有CUDA，我根本不知道该从何开始。
▶ 英文原文 ⏱
And so the, the way that we solve that problem is new out, new models, MOEs, um, uh, paralyzed and disaggregated and, and distributed, uh, uh, across a computing system. Uh, and without the ability to really get down and come up with new kernels with CUDA, it's really hard to do. And, and, and so the combination of the programmability of our, of our architecture, uh, the, the fact that NVIDIA is an extreme co-design company where we could even offload some of the computation into the fabric itself. MVLink, for example, into the network, Spectrum X, um, uh, and that we could affect change across the processors, the system, the fabric, the libraries, the algorithm, all of that was done simultaneously. Without CUDA to do that, I wouldn't even know where to start.
我的赞助商Crusoe是最早提供NVIDIA Blackwell和Blackwell Ultra平台的云服务之一。他们刚刚宣布了计划在今年晚些时候部署的NVIDIA Vera Rubin。但是，获得最先进的硬件只是故事的一部分。例如，大多数推理引擎已经可以为单个用户的前向传播进行KV缓存。但Crusoe可以跨多个用户和GPU进行这项操作。所以，如果有一千个代理在运行同一个系统提示，Crusoe只需计算一次KV缓存，这样集群中的每个GPU都可以使用。这一点尤其重要，因为系统变得更加真实，需要更长的前缀才能使用工具和访问文件。
▶ 英文原文 ⏱
My sponsor, Crusoe, was among the first clouds to offer NVIDIA's Blackwell and Blackwell Ultra platforms. And they just announced their NVIDIA Vera Rubin deployment, scheduled for later this year. But access to state-of-the-art hardware is only part of the story. For example, most inference engines already do KV caching for a single user's forward passes. But Crusoe does it across users and GPUs. So if a thousand agents are running on the same system prompt, Crusoe only has to compute the KV cache once for it to become available to every single GPU in the cluster. This is especially important as systems get more authentic and require much longer prefixes in order to use tools and access files.
在最近的一次基准测试中，Crusoe能够提供比VLLM快10倍的首次响应时间和高达5倍的吞吐量。这只是您应该使用Crusoe来运行推理工作负载的众多理由之一。而且，如果您需要用于训练的GPU，您无需更换云服务。Crusoe同样能为您提供支持。请访问crusoe.ai/thorkash了解更多信息。
▶ 英文原文 ⏱
In a recent benchmark, Crusoe was able to deliver up to 10 times faster time-to-first token and up to five times better throughput than VLLM. This is just one among many reasons that you should run your inference workload with Crusoe. And if you need GPUs for training, you don't need to switch clouds. Crusoe's got you covered there too. Go to crusoe.ai slash thorkash to learn more.
这引出了一个有关NVIDIA客户群的有趣问题，其中如果您60%的收入来自这五大超级规模计算公司。在一个拥有不同客户的时代，比如说是那些做实验的教授，他们需要CUDA的帮助，不能使用其他加速器，必须用CUDA运行PyTorch以实现全面优化。但如果是这些超级规模计算公司，它们有资源去编写自己的内核。事实上，为了在其特定架构中获得那额外的5%性能，它们不得不这样做。
▶ 英文原文 ⏱
So this gets at an interesting question about NVIDIA's clientele, where if 60% of your revenue is coming from these big five hyperscalers. In a different era with different customers, let's say it's professors who are running experiments, and they are helped a bunch by they need CUDA. They can't use another accelerator. They need to just run PyTorch with CUDA and have everything optimized. But if you've got these hyperscalers, they have the resources to write their own kernels. In fact, they have to to get that extra last 5% that they need for their specific architecture.
Anthropic 和 Google 主要使用自己的加速器或运行 TPUs 和 Tranium。但是，即使是使用 GPUs 的 OpenAI 也有 Triton，他们觉得需要自己的内核。因此，他们从 CUDA C++ 开始，没有使用 Kublas 和 Nickel 等，而是开发了自己的技术栈，可以编译到其他加速器上。因此，如果大多数客户能够并且确实在替代 CUDA，那么 CUDA 在 NVIDIA 平台上推动前沿 AI 发展的重要性到底有多大呢？
▶ 英文原文 ⏱
Anthropic, Google are mostly running their own accelerators or running TPUs and Tranium. But even OpenAI using GPUs has Triton, which they're like, we need our own kernels. So they've, down to CUDA C++, they've, instead of using Kublas and Nickel and everything, they've got their own stack, which compiles to other accelerators as well. And so if most of your customers can and do make replacements for CUDA, to what extent is CUDA really the thing that is going to make Frontier AI happen on NVIDIA?
CUDA 是一个丰富的生态系统。因此，如果你想在任何电脑上开发应用，首先选择在 CUDA 上进行开发是非常明智的。由于这个生态系统非常完善，我们支持各种开发框架。如果你需要创建自定义的内核，例如，我们对 Triton 进行了大量贡献，Triton 的后端包含了大量的 NVIDIA 技术。我们很乐意帮助各种框架发挥其最大的潜力。市面上有众多的框架，如 Triton、VLM、SGLang 等等。此外，还有大量新的强化学习框架正在涌现，比如 Vero、Nemo RL，以及更多其他框架。而现在随着后期训练和强化学习领域的发展，这一领域正在快速崛起。
▶ 英文原文 ⏱
CUDA is a rich ecosystem. And so if you want to build on any computer first, building on CUDA first is incredibly smart. And because the ecosystem is so rich, we support every framework. If you want to create custom kernels, if you need, for example, we contribute enormously to Triton. And so the back end of Triton, huge amounts of NVIDIA technology. We're delighted to help every framework become as great as it can be. And there's lots and lots of frameworks. There's Triton, there's VLM, there's SGLang, and there's more, right? And now there's a whole bunch of new reinforcement learning frameworks coming out. You know, you got Vero, you got Nemo RL, you got a whole bunch of new. And then now with post-training and reinforcement learning, that entire area is just exploding, right?
因此，如果你想建立一个架构，基于CUDA是最合理的选择。因为你知道CUDA的生态系统非常完善。当出现问题时，更可能是你自己的代码出了问题，而不是底层大量的代码出现错误。不要忘记，当你构建这些系统时，你实际上是在处理大量的代码。当遇到问题时，你需要思考，是你自己的错误还是机器的问题。你希望总是能确认是自己的问题，这样你就可以信任计算机本身。
▶ 英文原文 ⏱
And so if you want to build on an architecture, building on a CUDA makes the most sense. Because you know that the ecosystem is great. You know that if something happens, it's more likely in your code and not in the mountain of code underneath. You know, don't forget the amount of code that you're dealing with when you're building these systems. When something doesn't work, was it you or was it the computer? You would like it always to be you and to be able to trust the computer.
显然，我们自身仍然存在许多漏洞。但是，由于我们的系统非常稳固，因此至少可以在这个基础上进行构建。首先，系统的丰富性、可编程性和功能性都是其优点。其次，作为开发者，在构建任何软件时，最重要的就是安装基础。你希望你的软件能够在很多其他电脑上运行。你不是仅仅为自己开发软件，而是为了自己的用户群或其他人的用户群，因为你是一个框架建设者。
▶ 英文原文 ⏱
And obviously, we still have lots and lots of bugs ourselves. But our system is so well wrung out that you could at least build on top of the foundation. So that's number one, is that the richness of the ecosystem, the programmability of it, the capability of it. The second thing is, if you were a developer and you were building anything at all, the single most important thing you want more than anything is install base. You want the software that you run to run on a whole bunch of other computers. You don't want to build a software. You're not building software just for yourself. You're building software for your fleet or for everybody else's fleet because you're a framework builder.
英伟达的CUDA生态系统无疑是其最大的财富。我们如今不知道有多少，大概有数亿的GPU。每个云服务都有它。可以追溯到A10、A100、H100、H200等等，还有L系列、P系列等一大批各种规格和形状的产品。如果您是机器人公司，就会希望在机器人中运行这个CUDA套件。我们几乎无处不在。因此，只要您开发出软件和模型，它在各个领域都能够发挥作用。
▶ 英文原文 ⏱
And NVIDIA's CUDA ecosystem is ultimately its great treasure. We are now, I don't know how many, several hundred million GPUs. Every cloud has it. Goes back to A10, A100, H100, H200, you know. The L series, the P series. I mean, there's a whole bunch of them. And they're in all kinds of sizes and shapes. And if you're a robotics company, you want that CUDA stack to actually run in the robot itself. We're literally everywhere. And so the install base says that once you develop the software, once you develop the model, it's going to be useful everywhere.
所以，我们的安装基础非常宝贵。最后，由于我们处于每一个云环境中，这让我们非常独特。作为一家人工智能公司和开发人员，你可能不太确定会选择与哪个云服务提供商合作，也不确定想在哪里运行。但如果你愿意，我们可以在任何地方运行，包括本地部署。因此，我认为丰富的生态系统、广泛的安装基础以及我们所在位置的多样性，这些结合使得CUDA非常有价值。
▶ 英文原文 ⏱
And so the install base is just too incredibly valuable. And then lastly, the fact that we're in every single cloud makes us genuinely unique. Because, you know, you're an AI company and you're an AI developer. You're not exactly sure which CSP you're going to partner with and where you would like to run it. And we'd run it everywhere, including on-prem for you if you like. And so I think that the richness of the ecosystem, the expansiveness of the install base, and the versatility of where we are, that combination makes CUDA invaluable.
这很有道理。我想我好奇的是，这些优势对于你的主要客户来说是否真的很重要。有很多人可能会在意这些优势，尤其是那些能够自己构建软件技术栈的人，这类人可能会占你收入的大部分。特别是在一个人工智能在验证闭环紧密的任务上表现得特别好的情况下，你可以对这些任务进行强化学习。
▶ 英文原文 ⏱
So that makes a lot of sense. I guess the thing I'm curious about is whether those advantages matter a lot to your main customers. Like there's many people who they might matter for, the kind of person who can actually build their own software stack, who will make up most of your revenue. Especially if you go to a world where AI is getting especially good at the things which have tight verification loops, where you can RL on them.
然后，关于如何编写一个在规模扩展时最有效地执行注意力机制或多层感知器（MLP）的核函数的问题。这是一个非常可验证的反馈循环。那么，所有的大规模计算公司能否自己编写这些定制的核函数？尽管如此，NVIDIA仍然在PICE性能方面表现出色，所以他们可能仍然倾向于使用NVIDIA。然而，问题是，这是否只是变成一个谁能在给定预算内提供最佳规格、最佳浮点运算性能和内存带宽的问题呢？
▶ 英文原文 ⏱
And then this question of how do you write a kernel that does attention or MLP the most efficiently across a scale-up. It's a very verifiable sort of feedback loop. And so, oh, can everybody, can all the hyperscalers write these custom kernels for themselves? And they might still, NVIDIA still has great PICE performance, so they might still prefer to use NVIDIA. But then the question is, does it just become a question of who is offering the best specs, the best flops and memory and memory bandwidth for a given dollar?
历史上，NVIDIA 在硬件和软件方面一直拥有并仍然保持着人工智能领域中最高的利润率，超过70%，这主要得益于 CUDA 模式。现在的问题是，如果大多数客户其实有能力自行开发而不使用 CUDA 模式，那么你是否还能维持这些利润率？我们分配了大量工程师到这些 AI 实验室，与他们合作，优化他们的技术栈。这样做的原因是因为没有人比我们更了解我们的架构。
▶ 英文原文 ⏱
Where historically NVIDIA has just had, and still has, the best margins in all of AI across hardware and software, 70% plus, because of this CUDA mode. And the question is, oh, can you sustain those margins if, for most of your customers, they can actually afford to build instead of the CUDA mode? The number of engineers we have assigned to these AI labs is insane, working with them, optimizing their stack. And the reason for that is because nobody knows our architecture better than we do.
这些架构不像CPU那样通用。CPU的特点就像一辆凯迪拉克，非常稳定，是个不错的"巡航者"。它从不会太快，人人都可以轻松驾驶，因为它有定速巡航功能，一切都很简单。相比之下，NVIDIA的GPU加速器更像F1赛车。尽管大家可以以每小时100英里的速度驾驶它，但要将它推到极限需要相当多的专业知识。我们使用大量的人工智能来创建内核，而且可以肯定的是，我们在这方面还将继续有需要相当长一段时间。
▶ 英文原文 ⏱
And these architectures are not as general purpose as a CPU. The reason why a CPU is so, you know, a CPU is kind of like a Cadillac, you know, it's just always, you know, it's a nice cruiser. It never goes too fast. Everybody drives it pretty well, you know, it's got cruise control, you know, and everything is easy. But in a lot of ways, NVIDIA's GPUs are, accelerators are kind of like F1 racers. And yeah, I could imagine everybody's able to drive it at 100 miles an hour, but it takes quite a bit of expertise to be able to push it to the limit. And we use, we use a ton of AI to create the kernels that we have. And I'm pretty sure we're going to still be needed for quite some time.
我们经常通过我们的专业知识帮助AI实验室合作伙伴轻松地将他们的系统性能提高一倍。这并不罕见，在我们完成他们系统或某个内核的优化后，他们的模型运行速度提高到三倍、两倍或提升50%。这个提升幅度非常显著，尤其是当谈论到他们整个设备群的基础安装时，包括他们所有的Hoppers和Blackwalls。当性能提高一倍时，收入也会翻倍。这直接转化为收入。NVIDIA的计算平台是世界上性能最好的平台，无可匹敌。
▶ 英文原文 ⏱
So, our expertise helps our AI labs partners get another 2x out of their stack easily, oftentimes. It's not unusual that we, you know, by the time that we're done optimizing their stack or optimizing a particular kernel, their model sped up by 3x, 2x, 50%. That's a huge number, especially when you're talking about the install base of the fleet that they have, of all the hoppers and blackwalls that they have. When you increase it by a factor of two, that doubles the revenues. That directly translates to revenues. NVIDIA's computing stack is the best performance partitio in the world, bar none.
没有人可以向我证明，今天世界上任何一个平台在性能与总拥有成本（TCO）的比率上更出色。没有一个公司可以。而事实上，基准测试结果已经在那里。Dylan 说得对，Inference Max 就在那里供大家使用。没有一个，TPU 不会出现，Tranium 也不会出现。我鼓励他们使用 Inference Max 来展示他们令人难以置信的推理成本。这真的，非常，非常困难。没有人愿意站出来。MLperf，我欢迎 Tranium 来展示他们一直声称的 40%。我很希望看到他们展示 TPU 的成本优势。在我看来，这完全没有意义。从基本原则来看，这毫无意义。
▶ 英文原文 ⏱
Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company. And in fact, the benchmarks are out there. Dylan's, right, Inference Max is sitting out there for everybody to use. And not one, TPU won't come, Tranium won't come. I encourage them to use Inference Max and demonstrate their incredible inference cost. It's really, really hard. Nobody wants to show up. MLperf, I would welcome Tranium to demonstrate their 40% that they claim all the time. I would love to hear them demonstrate the cost advantage of TPUs. It makes no sense in my mind. It makes absolutely zero sense. On first principles, it makes no sense.
我认为我们如此成功的原因主要是因为我们的总拥有成本（TCO）非常优秀。你提到60%的客户来自全球排名前五的公司，但大多数业务是面对外部客户的。例如，大部分AWS的业务，大部分NVIDIA和AWS的服务都是为了外部客户，而不是内部使用。Azure的客户显然都是外部客户。OCI（Oracle云基础设施）的所有客户也都是外部的，而非内部使用。他们之所以选择我们，是因为我们的影响力非常大。我们能够为他们带来世界上优秀的客户。
▶ 英文原文 ⏱
And so I think the reason why we're so successful is simply because our TCO is so great. There's a second, you say 60% of our customers are the top five, but most of that business is external. For example, most of AWS is, most of NVIDIA and AWS is for external customers, not internal use. Most of our customers at Azure, obviously, all of our customers are external. All of our customers at OCI are external, not internal use. The reason why they favor us is because our reach is so great. We can bring them all of the great customers in the world.
它们都基于英伟达。所有这些公司选择英伟达的原因是因为我们的覆盖范围和多样性非常强大。我认为成功的关键在于已有的用户基础、我们架构的可编程性、丰富的生态系统，以及现在全球有这么多的人工智能公司，数量达到了数万家。如果你是其中一家AI初创公司，你会选择什么架构呢？你当然会选择最普及的架构，而我们是世界上最普及的。
▶ 英文原文 ⏱
They're all built on NVIDIA. And the reason why all these companies are built on NVIDIA is because our reach and our versatility is so great. And so I think the flywheel is really install base, the programmability of our architecture, the richness of our ecosystem, and the fact that there's so many AI companies in the world. There's tens of thousands of them now. And if you were one of those AI startups, what architecture would you choose? You would choose an architecture that's the most abundant, we're the most abundant in the world.
拥有最大的安装基础，我们有最大的安装基础，并且拥有丰富的生态系统。这就是驱动力，这也是为什么在这些因素的结合下，我们的性能每美元的性价比非常好，以至于他们的代币成本最低。其次，我们的每瓦性能是全球最高的。因此，如果其中一个公司或我们的合作伙伴建设一个一吉瓦的数据中心，那么这个一吉瓦的数据中心应该能够提供最大化的收入和代币数量，这直接转化为收入。
▶ 英文原文 ⏱
The one has the largest install base, we're the most largest install base, and one that has a rich ecosystem. And so that's the flywheel, that's the reason why between the combination of one, our perf per dollar is so great that they have the lowest cost tokens. Second, our perf per watt is the highest in the world. And so if one of these companies, if our partners built a one gigawatt data center, that one gigawatt data center better deliver the maximum amount of revenues and number of tokens, which directly translates to revenues.
你希望它尽可能多地产生令牌，从而最大化该数据中心的收入。我们拥有全球最高的每瓦特令牌生成架构。最后，如果你的目标是租赁基础设施，我们拥有全球最多的客户。这就是为什么这一循环能够运转的原因。有趣的是，我想问题归结到这里的实际市场结构是什么。因为即便有其他公司，也可能存在一个有成千上万家AI公司拥有大致相同计算份额的世界。
▶ 英文原文 ⏱
You want it to generate as many tokens as possible, maximize the revenues for that data center. We have the highest tokens per watt architecture in the world. And then lastly, if your goal is to rent the infrastructure, we have the most customers in the world. And so that's the reason why the flywheel works. Interesting. I guess the question comes down to what is the actual market structure here? Because even if there's other companies, there could have been a world where there's tens of thousands of AI companies that have roughly equal share of compute.
但即便是通过这五家超大规模云计算公司，实际上是那些能自己负担得起并有能力让不同的加速器发挥作用的人，比如那些在亚马逊上使用计算资源的用户、Anthropic、OpenAI和这些大型基础实验室。我认为你的假设前提可能是错误的。不过，让我换个稍微不同的问题来问你。等一下，可以回来让我纠正你的前提。好的，我先问你一个不同的问题。但是，请确保在之后让我回来修正，因为这对人工智能来说实在太重要了。
▶ 英文原文 ⏱
But if even through these five hyperscalers, really the people on Amazon using the computer, Anthropik, OpenAI, and these big foundation labs, is who can themselves afford and have the ability to make different accelerators work. No, I think your assumption is premise is wrong. Maybe. Yeah. But let me ask you a slightly different question. Come back and make me correct your premise. Okay. Let me just ask you a different question, which is, okay. But still, make sure, make me come back and fix, because it's just too important to AI.
这对于科学的未来太重要了，对于产业的未来也同样重要。这个前提，这个基础，听我把问题问完，然后你们可以一起回答。好的。那么，如果关于价格、性能等方面的说法都是真的，你认为为什么会出现这种情况？比如，Anthropik就在几天前宣布，他们与博通和谷歌签署了一项关于TPU和大部分计算的多千兆瓦协议。显然，对于谷歌来说，TPU是他们大部分的计算资源。如果我看这些大型的AI公司，似乎有一段时间他们都在使用NVIDIA的产品，而现在不是了。所以我很好奇，如果这些纸面上的东西都是真的，他们为什么选择其他加速器呢？
▶ 英文原文 ⏱
It's too important to the future of science. It's too important to the future of the industry. That premise, the premise, look. Let me just finish the question and then you can address it together. Yeah. So what do you think, if all these things are true about price performance and performance for what, et cetera, are true, why do you think it is the case that, say, Anthropik, for example, just announced a couple days ago, they have a multi-gigawatt deal with Broadcom and Google for TPUs and majority of their compute. Obviously, for Google, TPUs are majority of the compute. So if I look at these big AI companies, it seems like a lot of their companies, there was some point where it was all NVIDIA, and now it's not. And so I'm curious how to square, if these things are true on paper, why are they going with other accelerators?
是的。Anthropik 是一个独特的案例，而不是一种趋势。如果没有 Anthropik，为什么 TPU 会有任何增长呢？这完全是因为 Anthropik。同样地，如果没有 Anthropik，Tranium 又怎么会有增长呢？这也完全依赖于 Anthropik。我认为这一点已经相当广为人知并被理解了。并不是有很多 ASIC 机会，只有一个 Anthropik。但 OpenAI 与 AMD 有合作，他们正在打造自己的 Titan 加速器。是的，但我认为我们都可以承认，他们主要还是依赖于 NVIDIA。我们仍然会有很多合作。
▶ 英文原文 ⏱
Yeah. Anthropik is a unique instance and not a trend. Without Anthropik, why would there be any TPU growth at all? It's 100% Anthropik. Without Anthropik, why would there be any Tranium growth at all? It's 100% Anthropik. I think that's fairly well known and well understood. It's not that there's an abundance of ASIC opportunities. There's only one Anthropik. But OpenAI deals with AMD. They're building their own Titan accelerator. Yeah, but they're mostly, I think we could all acknowledge, they're vastly NVIDIA. And we're going to still do a lot of work together.
是的，我们不会因为别人使用其他东西或尝试新事物而感到不快。如果他们不去尝试其他选择，他们怎么会知道我们的东西有多好呢？有时候我们也需要提醒自己这一点。同时，我们也必须不断努力来维持我们当前的位置。总有很多夸大的说法，还有很多被取消的ASIC项目。即使你要制造一个ASIC，你仍然需要打造出比NVIDIA更好的产品。而要做到比NVIDIA更好并不简单，实际上也不太可行。
▶ 英文原文 ⏱
Yeah. And we're not, we're not, I'm not offended by other people using something else and trying things. If they don't try these other things, how would they know how good ours is? You know, and sometimes you've got to be reminded of it. And we got to, and we have to continuously earn, earn the position that we're in. There are always big claims and look at the number of ASICs that have been canceled. Just because you're going to build an ASIC, you still have to build something better than NVIDIA. And it's not that easy building something better than NVIDIA. It's not sensible, actually.
你知道吗，英伟达一定是有什么严重的疏漏。要知道，我们的规模和速度，每年都在实现巨大飞跃，是全球唯一能做到这一点的公司。我猜他们的逻辑是，只要产品性能不上不下超过70%的差距就行，因为他们的利润率是70%。但是，不要忘了，即使在ASIC（专用集成电路）领域，利润率也很高。英伟达的利润率是70%，而ASICs的利润率是65%。你真的能节省多少呢？
▶ 英文原文 ⏱
You know, it's, we, NVIDIA has got to be missing something seriously. You know, and because our scale, our velocity, we're the only company in the world that's cranking it out every single year. Big leaps every single year. I guess their logic is that, hey, it doesn't need to be better. It just needs to be not more than 70% worse because they're paying you 70% margins. No, no, no. Don't forget. Even in ASICs, margin's really quite high. NVIDIA's margin's 70%, let's say. But in ASICs, margin's 65%. What are you really saving?
哦，你是说像博通之类的吗？是的，当然。你得付钱给某人。所以，我觉得ASIC的利润率非常好，从我所了解的来看。他们自己也这么认为，因此他们对自己出色的ASIC利润率感到非常自豪。然后你问为什么。很久以前，我们没有能力做到这一点。在那个时候，我并没有深入理解建立一个基础AI实验室有多困难。
▶ 英文原文 ⏱
Oh, you mean from Broadcom or something like that? Yeah, sure. You got to pay somebody. And so, so I think the, the ASIC margins are, are incredibly good from what I can tell. And, and they believe, they believe it so too. And so they're, they're quite proud of their, their incredible ASIC margins. And so you asked the question why. A long time ago, we just didn't have the ability to do it. And, and this is, this is, this is, and at the time, I, at the time, I didn't deeply internalize how difficult it would be to build a, a foundation AI lab.
就像 OpenAI 和 Anthropic 这样的大公司。事实上，他们需要从供应商那里获得巨额投资。我们当时没有能力对 Anthropic 投资数十亿美元，以便他们使用我们的计算资源。但谷歌和 AWS 可以做到这一点。他们在一开始就投入了巨资，作为回报，Anthropic 使用了他们的计算资源。当时我们实在没有这个能力。
▶ 英文原文 ⏱
Like OpenAI and Anthropic. And the, the fact that they needed huge investments from the supplier themselves. We just weren't in a position to make the multi-billion dollar investment into Anthropic so that they could use our, use our compute. But Google and, and AWS were. And they put in huge investments in the beginning so that Anthropic, in return, use their compute. We, we just weren't in a position to do so at the time.
不，我也没有这样做。我认为我的错误在于没有深入意识到他们实际上没有其他选择。风险投资公司是不会投入五十亿到一百亿美元资金到一个人工智能实验室，并期待它变成Anthropic这样的公司。所以，这就是我的失误。但即使当时我理解这一点，我也认为我们没有能力进行这样的投资。不过，我不会再犯同样的错误。我很高兴能投资于OpenAI，并帮助他们扩大规模。
▶ 英文原文 ⏱
Nor, nor did I, I would say my mistake is I didn't deeply internalize that they, they really had no other options. That, that, that a VC would never put in five, $10 billion of investment into an AI lab with the, with the hopes of it turning out to be Anthropic. And so, and so that was my miss, but even if I understood it, I don't think we would have been in a position to do that at the time. But I'm not going to make that same mistake again. And, and, and, um, uh, I'm delighted to invest in open AI and, and, um, um, I'm delighted to, to, uh, help them scale.
嗯，呃，但是当时我们就是无法做到这一点。你知道，如果我能把一切都倒带重来，NVIDIA 那时候就可能像现在这样大，我会非常乐意去做。这其实很有趣，因为多年来，NVIDIA 一直是一家在人工智能领域赚大钱的公司。而现在，你们正在进行投资。据报道，你们对OpenAI的投资达到了300亿，对Anthropic的投资达到了100亿。
▶ 英文原文 ⏱
And, um, uh, but we just weren't at, at the time able to do so. You know, if I, if I could, uh, rewind everything, uh, NVIDIA, NVIDIA could have been as big back then as we are now, I would have been more than happy to do it. This is, this is actually quite interesting, which is, um, for many years, NVIDIA has been this, um, the company in AI making money, making lots of money. And, um, now you're investing it. It's been reported that you've done up to 30 billion in open AI and 10 billion in, um, Anthropic.
嗯，不过他们的估值现在已经上升了，而且我相信它们会继续上涨。所以，多年来，如果你一直在给他们提供计算资源，你就会看到我的趋势，而且在几年前或甚至是去年的某些情况下，它们的价值只有现在的十分之一。而且如果你有了这些现金，就有可能是英伟达自己成为一个基础实验室，进行巨大的投资以实现这种可能，或者就是你现在在目前估值下进行的交易其实可以更早进行。于是，我很好奇，为什么不早一点去做呢？我们是在尽可能早的时间做到的，我们是在尽可能早的时间做到的。而且，如果我能做到的话，我会更早去做的。在Anthropic需要我们这样做的时候，我们当时的确不具备这样的条件。
▶ 英文原文 ⏱
Um, but now their valuations have increased and I'm sure they'll continue to increase. Um, and so if over, over all these many years, you know, you were giving them the compute, you saw where I was headed and then they were worth like one 10th what they are now a couple of years ago, or even a year ago in some cases. Um, and you had all this cash. There's, there's, there's a world where either NVIDIA themselves becomes a foundation lab, um, that does a huge investment to make that possible or has made the deals you made now at current valuations much earlier on. Um, and you had the cash to do it. So I'm, I am curious actually why not have done it earlier? We did it as soon as we could have. We did it as soon as we could have. And, and, um, if I could have, I would have done it even earlier. Um, at the time that Anthropic needed us to do it, we just weren't in a position to do it.
这不是我们当时的想法，明白吗？怎么说呢？是资金问题还是其他的？是的，说到底是投资规模的问题。我们当时从来没有在公司外部做过大的投资，投入也不多。而且我们没有意识到我们需要这样做。我一直以为他们可以像所有公司那样去找风险投资，不是吗？但是他们想做的事情，是无法通过风险投资实现的。OpenAI想做的事情，风险投资是无法支持的。我现在才意识到这一点，但我当时并不清楚。这就是他们的聪明之处，他们真的很有才华。他们当时就意识到自己必须采取这样的行动，我对此感到很高兴。即便如此，即使我们让Anthropic不得不去寻找其他投资者，我仍然为此感到欣慰。
▶ 英文原文 ⏱
It wasn't, it wasn't, you know, it wasn't in our sensibility to do so. How so? Like a cash thing or just? Yeah, the level of investment, you know, we never invested outside the company at the time and not that much. And, um, and we didn't realize we needed to, you know, I always, I always thought that they could just go raise VCs for God's sakes, like, like all companies do. Um, but, but, um, uh, what they were trying to, what they were, were trying to do, uh, couldn't have been done through VCs. What OpenAI wanted to do couldn't have been done through VCs. And, and I recognize that now. I didn't know it then, you know, but that's their genius. That's why they're smart, you know? And so, so they realized, they realized that then that they had to do something like that. And I'm delighted that they did, you know? And, and even though, even though, um, we, we caused Anthropic to have to go to somebody else, um, I'm still happy that it happened.
Anthropic的存在对世界非常重要，我为此感到高兴。呃，我想你们仍然在赚大钱，并且你们的收入一季比一季多。这挺好的，但仍可能会有些遗憾。所以问题来了，既然你们现在赚了这么多钱，Nvidia应该怎么做呢？有一个答案是说，看，现在有一个中间商的生态系统出现了，专门把资本支出（CapEx）转换为运营支出（OpEx），让这些实验室可以租用计算能力。因为芯片真的很贵，虽然随着AI和机器学习（AML）的改进，它们在生命周期内能赚很多钱，通过计算代币生成的价值也在增加，但设置成本还是很高。Nvidia因为资本支出而有资金支持，而且实际上，据报道你们已经为核心支持提供了高达63亿美元的投资。但仍然可以考虑如何更好地利用这些资金。
▶ 英文原文 ⏱
Anthropic's existence is great for the world. I'm delighted for it. Uh, I guess you still are making a ton of money and you're making way more money, um, quarter after quarter. It's still okay to have regrets. Um, so the, the, the question still arises, okay, well, now that we're here and you have all this money that you keep making, um, what should Nvidia be doing with it? And there's one answer which says, look, there's this whole middleman ecosystem that has popped up for converting, um, CapEx into OpEx for these labs so that they can rent compute. Um, because the chips are really expensive. Um, because the chips are really expensive. They make a lot of money over their lifetime through, because the AML is getting better. The value that they generate through tokens is increasing, but they're expensive to set up. Nvidia has the money due to the CapEx. So, and in fact, you are, uh, you're, it's been reported, you're backstopping core. We have up to 6.3 billion and have invested to be, um, but yeah.
为什么，为什么，为什么英伟达不自己成为云服务商？为什么不自己成为超大规模云计算提供商并提供计算服务？你们有足够的资金去实现这一点。这是公司的一个理念，我认为这是明智的。我们应该尽可能地满足需求，但尽量少做不必要的事。这意味着，我们在构建计算平台时所做的工作，如果我们不去做，我真的相信它就不会被完成。如果我们不承担风险、不按自己的方式构建MV link、不建设整个系统、不创建我们的生态系统，如果我们没有投入20年时间在CUDA上，尽管大部分时间在亏损，如果我们不这样做，没人会做。如果我们不创建所有的CUDA X库并使其全部领域专用，十几年前我们就开始推动领域专用的库。
▶ 英文原文 ⏱
Why, why, why, why doesn't Nvidia become a cloud themselves? Why doesn't it become a hyperscaler themselves and write this compute out? You have all this cash to do it. This is a philosophy of the company. And I think it's wise. We should do as much as needed, as little as possible. And, and what that means is that the work that we do with building our, our computing platform, if we don't, if we don't do it, I genuinely believe it doesn't get done. If we didn't take the risk that we take, if we didn't build MV link, the way we built, if we didn't build the whole stack, if we didn't create the ecosystem, the way we did it, if we didn't dedicate ourselves to 20 years of CUDA while losing money, most of that time, if we didn't do it, nobody else would have done it. If we didn't create all of the CUDA X libraries so that they're all domain specific, you know, this is several, a decade and a half ago, we pushed into domain specific libraries.
因为我们意识到，如果我们不创建这些特定领域的库，比如用于光线追踪或图像生成，甚至早期的人工智能工作，以及数据处理、结构化数据处理或向量数据处理的模型，没有人会去做。我非常确定这一点。我们为计算光刻创建了一个名为Coolitho的库，如果我们不创建它，也不会有人去做。如果我们不做我们所做的事情，加速计算就不会像今天这样进步。所以我们应该这样做，应该全心全意地将公司的所有力量投入到这个方向。然而，世界上有很多云计算服务。如果我不做，会有人来做。因此，“尽可能多做必要的事，但尽可能少做不必要的事”这种理念在我们公司存在。我做的每一件事，都是以这个理念为指导的。
▶ 英文原文 ⏱
Because we realized that if we didn't create these domain specific libraries, whether it's for ray tracing or image generation, or even the early works of AI, these models, if we didn't create them for data processing, structure data processing, or vector data processing, if we didn't create them, nobody would. And I am completely certain of that. We created a library for computational lithography called Coolitho. If we didn't create it, nobody would have. And so accelerated computing wouldn't advance the way it has if we didn't do what we did. And so we should do that. We should dedicate our company, all of our might, wholeheartedly to go do that. However, the world has lots of clouds. If I didn't do it, somebody would show up. And so following the recipe, the philosophy of doing as much as needed, but as little as possible, as little as possible, that philosophy exists in our company today. And everything I do, I do it with that lens.
在云计算方面，如果我们不支持CoreWeave的存在，这些新型云计算平台和AI云就不会出现。如果我们没有帮助CoreWeave存在，它们就不会存在。如果我们不支持NScale，它们就不会有今天的成就。如果我们不支持Nebius，它们也不会有今天的表现。现在它们的表现非常出色。那么，这是一个什么样的商业模式呢？我们应该做的尽量多，但干预尽量少。因此，我们努力在我们的生态系统中进行投资，因为我希望我们的生态系统能够蓬勃发展。我希望我们的架构和AI能够连接尽可能多的行业和国家，使得整个人类社会能够建立在AI和美国技术基础之上。我认为，这就是我们所追求的愿景。
▶ 英文原文 ⏱
In the case of clouds, if we didn't support CoreWeave to exist, these NeoClouds, these AI Clouds wouldn't exist. If we didn't help CoreWeave exist, they would not exist. If we didn't support NScale, they wouldn't be where they are today. If we didn't support Nebius, they wouldn't be where they are today. Now they are, they're doing fantastically. Is that a business model where, no, we should do as much as needed, as little as possible. And so we're trying, we invest in our ecosystem because I want our ecosystem to thrive. And I want our, I want, I want the architecture and I want AI to be able to connect with as many industries as possible, as many countries as possible. And make it possible for, you know, the planet to be built on AI and to be built on the American tech stack. And so, so that vision, I think, is exactly what we're pursuing.
现在，你提到的一个事情是，有许多优秀、卓越的基础模型公司，而我们尽量在所有这些公司中进行投资。这也是我们的一项原则：我们不挑选赢家。我们需要支持每一个公司，这是我们工作的乐趣之一。对我们的业务来说，这是至关重要的，但我们也特别努力地不去挑选赢家。因此，当我投资其中一家时，我就相当于投资了所有公司。为什么你们特别不去挑选赢家呢？因为那不是我们的职责所在。
▶ 英文原文 ⏱
Now, one of the things that you mentioned, there are so many great, amazing foundation model companies and we try to invest in all of them. And this is, this is another thing that we do. We don't pick winners. And we, we like, we, we, we need to support everyone. And it's part of our, part of our, our, our joy of doing so. It's, it's an imperative to our business, but we also go out of our way not to pick winners. And so when I, when I invest in one of them, I invest in all of them. Why do you go out of your way to not to pick winners? Because it's not our job to.
第一点。第二点，当NVIDIA刚起步时，有60家图形公司，60家3D图形公司。我们是唯一生存下来的。如果你把这60家公司，60家图形公司放在一起，问自己哪家会成功，NVIDIA可能会被认为最不可能成功。你知道，这是在你很久之前，但NVIDIA的图形架构完全错误。不是稍微错误，而是完全错误。我们创建的架构让开发者难以支持，这是不可能成功的。我们从好的基本原理出发去推理，但最终得出了错误的解决方案。
▶ 英文原文 ⏱
Number one. Number two, when NVIDIA first started, there were 60 graphics companies, 60 3D graphics companies. We are the only one that survived. If you would have taken those 60 companies, 60 graphics companies and ask yourself which one was going to make it, NVIDIA would be the top of that list not to make it. You know, this is long before you, but NVIDIA's graphics architecture was precisely wrong. It's not a little bit wrong. We created an architecture that was precisely wrong. And, and it was an impossible thing for developers to support. It was never going to make it. We reasoned about it for good, for, from good first, first principles, but we ended up in the wrong solution.
翻译成中文：
而且，嗯，呃，每个人都曾有点认为我们不可能成功。但我们就在这里。所以我，我，我有足够的谦逊去认识到，我们不该急于判断谁会是赢家。是的。要么让大家各自发展，要么就对大家一视同仁。有一点我不太明白，你说我们不会仅仅因为这些新云存在而优先支持它们，不是因为我们想扶持它们。但你也说过，列举了很多新云，并说如果没有NVIDIA，它们就不会存在。
▶ 英文原文 ⏱
And, and, um, uh, everybody would have kind of, everybody would have counted us out. And, and here we are. And so I'm, I'm, I'm, I have enough humility to recognize that, you know, don't, don't pick winners. Yeah. Either let them all take care of themselves or take care of all of them. Um, one thing I didn't understand is you said, look, we're not prioritizing these, you know, clouds, um, just because there are new clouds and we want to prop them up. But you also said, you listed a bunch of new clouds and you said they wouldn't exist if it wasn't for NVIDIA.
好的，那么这两者之间是如何兼容的呢？首先，他们需要有愿望来存在，并且向我们寻求帮助。当他们真正希望存在并制定好商业计划、具备专业知识以及对这个事情有热情时，他们显然需要自己具备一些能力。但如果最终他们需要一些投资来启动这个项目，我们会在那里支持他们。
▶ 英文原文 ⏱
Yeah. And so how are those two things compatible? Um, first of all, they, they need to want to exist and they come to ask us for help. And when they, when they, um, uh, when they want to exist and have, they have a business plan and they, you know, they have expertise and, you know, they have the passion for it. Uh, they obviously have to have some capabilities themselves. Uh, but if at the end of the day they need some investment and we're to get it off the ground, uh, we, we would be there for them.
嗯，但是，他们越早启动他们的飞轮，你知道，你的问题是，我们想进入融资业务吗？嗯，是的，我们不想。我们希望与所有从事融资业务的人合作，而不是自己成为一个融资者。所以，我认为，我们的目标是专注于我们所做的事情，让我们的商业模式尽可能简单，并支持我们的生态系统。
▶ 英文原文 ⏱
Um, but, but the sooner they get their flywheel going, you know, your question was, do we want to be in the financing business? Um, yeah, we, we don't want to be, we want to, we, because there are people in the financing business and we rather work with all of the people who are in the financing business than to be a financier ourselves. And so, so I think the, the, uh, our goal is to focus on what we do, keep our business model as simple as possible, support our ecosystem.
嗯，当像OpenAI这样的公司需要规模高达300亿美元的投资时，这通常是因为他们还没有进行首次公开募股（IPO）。我们对他们非常有信心。我坚信，他们已经是一家了不起的公司，并且将会成为更出色的公司。
▶ 英文原文 ⏱
Um, when someone like, like, uh, open AI needs an investment of $30 billion scale, um, because it's still before their IPO and, and, uh, um, we deeply believe in them. Uh, we deeply believe that, uh, I deeply believe that, that they're going to be, they're going to be an, well, they're an extraordinary company already today. They're going to be incredible company.
嗯，世界需要他们的存在。世界希望他们存在。我也希望他们存在。而且，他们具备了一切，他们背后有强大的推动力。让我们支持他们，帮助他们壮大。因此，那些投资是必要的，因为他们需要我们的支持。不过，我们并不想投入过多，而是希望尽可能少地介入。
▶ 英文原文 ⏱
Uh, the world needs them to exist. The world wants them to exist. I want them to exist. And, and, uh, they have everything, they have the wind at their back. Let's, let's support them and let them scale. And so, so to those, those investments will do because we're, they need us to do it. And, um, uh, but we're, we're not trying to do as much as possible. We're trying to do as little as possible.
我花了太多时间在谷歌文档和聊天机器人之间复制粘贴文本。因此，我创建了一个基本上用于写作的光标，它的运作方式就像我理想中的AI合作研究员一样。通过标签，它可以通过行内评论与我交流，帮助我深入探索和头脑风暴。我在周末用Cursor和他们的新作曲工具以及许多代理编码工具构建了这个系统。我感觉自己对底层细节一无所知，只能放手，希望能得到好的结果。但是Cursor让我在实施的过程中尝试了许多不同的想法。我大多在代理窗口进行头脑风暴，在建立一些基本文件后，我用差异窗口跟踪更改。当我需要手动快速调整时，我就使用编辑器。如果你想亲自尝试我的AI合作研究员，我已经在描述中链接了GitHub仓库。
▶ 英文原文 ⏱
I spend way too much time copy pasting texts back and forth from Google docs to chatbots. And so I built what's basically a cursor for writing, which operates the way I think an AI co-researcher should operate. I can tag it and it can talk with me through inline comment threads and help me dig deeper and brainstorm. I built this entire thing over the weekend with cursor and their new composer to model with a lot of agentic coding tools. I feel like I have no idea what's going on under the surface. I just have to relinquish control and hope for the best. But cursor, let me try a bunch of different ideas while staying on top of the implementation. I did most of my brainstorming in the agents window. And after I got some basic files in place, I use a diff window to track changes. The few times that I needed to make a quick tweak by hand, I just use the editor. If you want to try my AI co-researcher yourself, I've linked the GitHub repo in the description.
如果你有一个想要开发的工具，就应该行动起来。访问 cursor.com/thorcash 开始。这可能是一个显而易见的问题，但多年来我们一直面临 GPU 短缺的情况。而现在由于模型的进步，短缺问题更加严重。是的，GPU 确实很短缺。大家都知道，NVIDIA 在分配有限的资源时，不仅仅按照出价高低来决定，而是想确保一些新的云服务能够存在。他们会分配一些给 CoreWeave，一些给 Crusoe，还有一些给 Lambda。这样做对 NVIDIA 有什么好处？首先，你同意这种对市场进行分割的说法吗？
▶ 英文原文 ⏱
And if you have a tool that you've been wanting to build, you should make it happen. Go to cursor.com slash Thorcash to get started. This might be sort of an obvious question, but we've lived many years in this situation where there's a shortage of GPUs. And it's grown now because models are getting better. We have a shortage of GPUs. Yes. Yeah. And NVIDIA is known for divvying up the scarce allocation, not just based on highest bidder, but rather on, hey, we want to make sure that these Neo clouds exist. Let's give some to CoreWeave. Let's give some to Crusoe. Let's give some to Lambda. Why is it good for NVIDIA? First of all, would you agree with this characterization of fracturing the market?
不，不是这样。你的假设是错误的。我们非常关注这些事情。 首先，如果你不下采购订单（PO），无论怎么讨论都没有意义。所以在我们收到采购订单之前，我们能做什么呢？因此，第一步是我们会非常努力地与每个人合作来完成预测，因为这些东西建造起来需要很长时间，数据中心的建设也需要很长时间。通过预测，我们在需求和供应等方面达成一致。 这是首要任务。第二，我们尽可能多地与各方进行预测，但最终你还是得下订单。如果因为某种原因你没有下单，我也无能为力。
▶ 英文原文 ⏱
No, no. Yeah. Your premise is just wrong. Yeah. Yeah. We're sufficiently mindful about these things. We're very mindful about these things. First of all, if you don't place a PO, all the talking in the world won't make a difference. And so until we get a PO, what are we going to do? And so the first thing is we work really hard with everybody to get a forecast done because these things take a long time to build and the data centers take a long time to build. And so we align ourselves with demand and supply and things like that through forecasting. Okay. That's job number one. Number two, we've tried to forecast with as many people as possible, but in the final analysis, you still have to place an order. And maybe for whatever reason you didn't place your order, what can I do?
翻译成中文如下：
所以在某种程度上，我们遵循的是“先进先出”的原则。但如果你的数据中心尚未准备好，或者某些组件还没准备好来支持你建好数据中心，我们可能会决定先为其他客户服务。这是为了最大化我们工厂的产能。因此我们可能会做出一些调整。除此之外，优先顺序是先进先出。对，你得先下订单。如果你不下订单，当然，有相关的故事流传。比如说，有个文章说拉里和埃隆和我吃晚饭，求着我给他们提供GPU。这从来没有发生过。
▶ 英文原文 ⏱
And so at some point, first in, first out. But beyond that, if you're not ready because your data center is not ready or certain components aren't ready to enable you to stand up a data center, we might decide to serve another customer first. That's just maximizing the throughput of our own factory. And so we might do some adjustments there. Aside from that, the prioritization is first in, first out. Yeah. You got to place a PO. If you don't place a PO. Now, of course, there are stories about that. You know, like, for example, all of this kind of started from, it was an article about Larry and Elon having dinner with me where they begged for GPUs. That never happened.
我们确实一起吃了晚餐。我们的晚餐非常愉快。期间，他们并没有急切地要求获取GPU。因此，他们只能下订单。一旦他们下了订单，我们会尽力为他们提供所需的容量。我们的方式并不复杂。听起来像是有个排队机制，根据数据中心的准备情况和下订单的时间，他们可以在某个时间获得所需的能力。但这并不意味着出价高的人就能直接得到。这样做的原因是什么呢？我们从不这样做。为什么不这样做呢？因为这不是良好的商业惯例。
▶ 英文原文 ⏱
We absolutely had dinner. We absolutely had dinner. And it was a wonderful dinner. In no time did they beg for GPUs. And so they just had to place an order. And once they placed an order, we do our best to get the capacity to them. We're not complicated. Okay. So it sounds like there's a queue and then based on whether your data center is ready and when you place a purchase order, you can have a certain time. But it still doesn't sound like High Spitter just gets it. Is there a reason to do it? We never do that. Okay. We never do that. Well, why not just do High Spitter? Because it's a bad business practice.
你定下你的价格。你定下你的价格，然后人们决定是否购买。我知道芯片行业的其他公司会在需求高的时候调整价格，但我们从不这样做。这从来不是我们的做法。你可以信赖我们。你知道，我更愿意做一个值得信赖的行业基础，这样你就不需要再三猜测。如果我们给你报了一个价格，那就是那个价格。如果需求飙升，那就随它去吧。另一方面，这也是为什么你和台积电保持良好合作关系的原因，对吧？是的。
▶ 英文原文 ⏱
You set your price. You set your price. And then people decide to buy it or not. And I understand that others in the chip industry change their prices when demand is higher. But we just don't. We just don't. That's just never been a practice of ours. You can count on us. You know, I prefer to be dependable, to be the foundation of the industry. And you don't need to second guess. You know, if I quoted you a price, we quoted you a price. That's it. And if demand goes through the roof, so be it. And on the other end, that's why you have a productive relationship with TSMC, right? Yeah.
当然，当然。NVIDIA已经经营业务有接近30年了。我们一直与他们合作。而NVIDIA和台积电之间并没有法律合同。这种合作中，总会有一些公正和不公正的时候。有时候我占上风，有时候我不占。有时我得到一个更好的交易，有时我得到一个差点的交易。但总体来说，这种关系是极好的。我对他们完全信任，也完全依赖他们。你可以相信NVIDIA的一点是，明年，今年Vera Rubin会非常出色。明年，Vera Rubin Ultra会问世。再下一年是Feynman，再下一年我还没公布名字。所以每年你都可以信赖我们。
▶ 英文原文 ⏱
Yeah, yeah. NVIDIA has been in business. We've been doing business with them for, I guess, coming up on 30 years. And NVIDIA and TSMC don't have a legal contract. There is always some rough justice. And sometimes I'm right. Sometimes I'm wrong. Sometimes I got a better deal. Sometimes I got a worse deal. But overall, in the whole, the relationship is incredible. And I can completely trust them. I can completely depend on them. And one of the things that you can count on with NVIDIA is that next year, this year, Vera Rubin is going to be incredible. Next year, Vera Rubin Ultra will come. The year after that, Feynman will come. And the year after that, I haven't introduced the name yet. And so every single year, you can count on us.
这是一种情况，你需要去全球寻找另一个ASIC团队。你可以选择一个ASIC团队，并确信地说：“我可以把整个业务压在你们身上，你们每年都会为我服务。你们的成本，尤其是令牌成本，每年都会减少一个数量级。我可以像依靠时钟一样依靠你们。”刚才我提到了台积电。在历史上，没有其他代工厂能做到这一点。你今天可以这样形容英伟达。你可以每年依靠我们。如果你想购买价值十亿美元的AI工厂计算资源，没问题。如果你想买一亿美元的，也没问题。
▶ 英文原文 ⏱
And this is an, you're going to have to go find another ASIC team in the world. Pick your ASIC team where you can say, I can bet the farm of, I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it, but I can count on the clock. Well, I just said something about TSMC. No other foundry in history can you possibly say that. You can say that about NVIDIA today. You can count on us every single year. If you would like to buy a billion dollars worth of AI factory compute, no problem. If you'd like to buy a hundred million dollars, no problem.
如果你想购买价值一千万美元或只要一个机架，不成问题。或者只需要一块显卡，也没问题。如果你想订购一个价值千亿美元的AI工厂，仍然没有问题。我们是全球唯一能够这样承诺的公司。我也可以这么评价台积电(TSMC)，无论是一件还是一亿件，都没有问题。我们只需要经历规划过程，像成熟的人那样行事。正因为如此，我认为NVIDIA能够成为全球AI行业的基础，这一地位是我们经过十年甚至几十年的努力才达到的。这需要巨大的投入和奉献。我们公司稳定性和一致性是非常重要的。
▶ 英文原文 ⏱
If you'd like to buy $10 million or just one rack, not a problem. Or just one graphics card, okay, no problem. If you would like to place an order for a hundred billion dollar AI factory, no problem. We're the only company in the world where you can say that today. I can say that about TSMC as well. I want to buy one, buy one billion, no problem. We just got to go through the process of planning for it. You know, all the things that mature people do, you know? And so I think this ability for NVIDIA to be the foundation of the world's AI industry, this is a position that has taken us a decade, several decades, a couple of decades to arrive at. Enormous commitment, enormous dedication. And the stability of our company, the consistency of our company is really, really important.
好的，我想问一下有关中国的问题。是这样的，我其实不太确定向中国出售船只是否合适，但我总是喜欢和我的嘉宾辩论。比如，当支持技术出口管制的Dario来访时，我就问他，为什么美国和中国不能都在数据中心拥有一批天才呢？不过，既然你持相反的观点，我就从相反的角度来问你。另外，有一种思路是，比如Anthropic公司几天前宣布，他们的一个叫Mythos的模型甚至没有公开发布，因为他们认为这个模型具有很强的网络攻击能力，直到我们确保所有漏洞都被修补之前，世界还没准备好接受它。
▶ 英文原文 ⏱
Okay, I want to ask about China. Yeah. And I always like to take, I actually don't know what I think about whether it's good to sell ships to China or not, but I played devil's advocate against my guests. So when Dario was on, who supports tax work controls, I asked him, well, why can't America and China both have country of geniuses in a data center? But since you're on the opposite side, I'll ask you in the opposite way. And look, one way to think about it is Anthropic actually announced a couple of days ago, this model of Mythos is not even releasing publicly because they say it has such cyber offensive capabilities that we don't think the world is ready until we make sure these zero days are patched up.
他们说，发现每个主要操作系统和每个浏览器都有数以千计的高严重性漏洞。甚至在OpenBSD中也发现了一个漏洞，而这个操作系统专门设计得没有零日漏洞，它已经存在了27年。一旦中国公司、中国实验室和中国政府获得了AI芯片，用来训练像Claude Mythos这样的具备网络攻击能力的模型，并以更多的计算能力运行数百万个实例，那么问题是：这对美国公司和美国国家安全是否构成威胁？首先，Mythos是在相对普通的能力和数量上由一家非凡的公司训练的。
▶ 英文原文 ⏱
But they say it found thousands of high severity vulnerabilities across every major operating system, every browser. It found one in OpenBSD, which is this operating system that has been specifically designed to not have zero days and it found one for 27 years it's existed. And so if Chinese companies and Chinese labs and the Chinese government had access to the AI chips to train a model like Claude Mythos with these cyber offensive capabilities and run millions of instances of it with more compute, the question is, oh, is that a threat to American companies, to American national security? First of all, Mythos was trained on fairly mundane capacity and a fairly mundane amount of it by an extraordinary company.
因此，中国拥有充足的计算能力和多种类型的计算资源。首先，你要认识到，芯片在中国是存在的。他们制造了全球60%甚至更多的主流芯片，这对他们来说是一个非常庞大的产业。他们拥有世界上一些顶尖的计算机科学家。正如你所知，在所有这些人工智能实验室中，大多数人工智能研究人员是中国人。他们拥有世界上50%的人工智能研究人员。因此，如果你对他们感到担忧，就要考虑他们已经拥有的资源和优势。他们有充足的能源、丰富的芯片和大多数的AI研究人员。如果你担心他们，创造一个安全世界的最佳方式是什么呢?
▶ 英文原文 ⏱
And so the amount of capacity and the type of compute that it was trained on is abundantly available in China. And so you just have to first realize that chips exist in China. They manufacture 60% of the world's mainstream chips, maybe more. It's a very large industry for them. They have some of the world's greatest computer scientists. As you know, most of the AI researchers in all of these AI labs, most of them are Chinese. They have 50% of the world's AI researchers. And so the question is, if you're concerned about them, what is the, considering all the assets they already have? They have an abundance of energy. They have plenty of chips. They got most of the AI researchers. If you're worried about them, what is the best way to create a safe world?
把他们当作受害者，将他们视为敌人，这可能不是最好的解决办法。他们是对手，我们希望美国获胜。但我认为进行对话和研究交流可能是最安全的方法。由于我们目前对中国对抗的态度，这方面明显存在不足。让我们的人工智能研究人员和他们的人工智能研究人员进行交流是非常必要的。我们需要努力就哪些用途不适合使用人工智能达成共识。
▶ 英文原文 ⏱
Well, victimizing them, turning them into an enemy, likely isn't the best answer. They are an adversary. We want the United States to win. But I think having a dialogue and having research dialogue is probably the safest thing to do. This is an area that is glaringly missing because of our current attitude about China as an adversary. It is essential that our AI researchers and their AI researchers are actually talking. It is essential that we try to both agree on how to, what not to use the AI for.
关于在软件中查找漏洞，当然，这是人工智能应该做的事情。它会在许多软件中找到漏洞吗？当然。软件中存在大量的漏洞。人工智能软件中也有很多漏洞。这就是人工智能应该做的事情。我很高兴人工智能已经发展到可以极大提升我们生产力的水平。有一点常常被忽视，就是围绕网络安全、人工智能网络安全、人工智能安全、人工智能隐私和人工智能安全性的生态系统的丰富性。整个生态系统由众多人工智能初创公司组成，他们试图为我们创造一个未来，在这个未来中，你拥有一个了不起的AI代理，由成千上万个AI代理保护其安全和可靠。
▶ 英文原文 ⏱
With respect to finding bugs in software, of course, that's what AI is supposed to do. Is it going to find bugs in a lot of software? Of course. There's lots and lots of bugs. There are lots of bugs in the AI software. And so that's what AI is supposed to do. And I'm delighted that AI has reached a level where it could help us be so much more productive. One of the things that is under-emphasized is the richness of ecosystem around cybersecurity, AI cybersecurity, and AI security, and AI privacy, and AI safety. That whole ecosystem of AI startups that are trying to create this future for us, where you have one AI agent that's incredible, surrounded by thousands of AI agents, keeping it safe, keeping it secure.
那个未来肯定会发生。而认为会有AI代理无人监管地到处活动的想法有些疯狂。因此，我们很清楚这个生态系统需要蓬勃发展。事实证明，这个生态系统需要开源，需要开放的模型和架构，以便所有AI研究人员和优秀的计算机科学家可以构建同样强大的AI系统，并确保AI的安全。因此，我们需要确保的一件事情就是保持开源生态系统的活力。
▶ 英文原文 ⏱
That future surely is going to happen. And the idea that you're going to have an AI agent running around with nobody watching after it is kind of insane. And so we know very well that this ecosystem needs to thrive. It turns out this ecosystem needs open source. This ecosystem needs open models. They need open stacks so that all of these AI researchers and all these great computer scientists can go build AI systems that are as formidable and can keep AI safe. And so one of the things that we need to make sure that we do is we keep the open source ecosystem vibrant.
这点不能被忽视。这点不能被忽视。而且很多问题都源自中国。我们不应该去压制这一点。关于中国，我们当然希望美国能够拥有尽可能多的计算能力。我们受到能源的限制。但你知道，我们有很多人在努力解决这个问题。我们不应该让能源成为我们国家的瓶颈。同时，我们还希望确保全球的AI开发者都在使用美国的技术平台进行开发，并且特别是当AI是开放源码时，将这些贡献和进步提供给美国的生态系统。
▶ 英文原文 ⏱
And that can't be ignored. That can't be ignored. And a lot of that is coming out of China. We ought to not suffocate that. With respect to China, we want to have, of course, we want United States to have as much computing as possible. We're limited by energy. But, you know, we got a lot of people working on that. And we ought to not make energy a bottleneck for our country. But what we also want is we want to make sure that all the AI developers in the world are developing on the American tech stack and making the contributions, the advancements of AI, especially when it's open source, available to the American ecosystem.
将两个生态系统分开，一个是开源生态系统，只运行在中国的技术上，另一个是封闭的生态系统，只运行在美国的技术上，这样做是非常愚蠢的。我认为这对美国来说会是一个糟糕的结果。因为这里涉及很多方面，我想先简单理清一下回应。回到性能差异和黑客攻击的问题，尽管他们有计算能力，但一些评估认为，由于他们在制造7纳米芯片时缺乏芯片制造专家的控制和紫外光刻技术，仍存在一些限制。
▶ 英文原文 ⏱
And it would be extremely foolish to create two ecosystems, the open source ecosystem, and it only runs on the Chinese tech, a foreign tech stack, and a closed ecosystem, and that runs on the American tech stack. I think that that would be a horrible outcome for the United States. Since there are a lot of things, let me just triage the response. I mean, I think the concern, going back to the flop difference and the hacking, is, yes, they have compute, but there's some estimates that because they're at 7 nanometer, they don't have UVs because of chip making expert controls.
他们即将实际产出的浮点运算次数只有美国的十分之一。因此，他们是否最终可以训练出像Mythos这样的模型？答案是可以的。但是，由于我们拥有更多的浮点运算，美国的实验室能够更快达到这种能力水平。由于Anthropic率先达到了这一水平，他们决定先保留这个技术一个月，同时让所有这些美国公司有机会使用它。这些公司就会利用这个时间来修补所有的漏洞，然后再正式发布。
▶ 英文原文 ⏱
The amount of flops they're about to actually produce, they have like one-tenth the amount of flops that the U.S. has. And so with that, could they train eventually a model like Mythos? Yes. But the question is, because we have more flops, American labs are able to get to these level of capabilities first. And because Anthropic got to it first, they say, okay, we're going to hold on to it for a month while all these American companies, we give them access to it. They're going to patch up all their vulnerabilities, and now we release it.
此外，即使他们训练出这样的模型，其在大规模部署时的能力也是一个关键问题。比如说，如果有网络黑客，人数是一千和一百万的区别将会大大增加危险性。因此，推理计算的能力非常重要。实际上，他们拥有这么多优秀的AI研究人员，这才是真正令人感到可怕的地方，因为让这些研究人员更加高效的关键因素就是计算能力。美国的AI实验室通常会说，他们面临瓶颈的就是计算能力的不足。
▶ 英文原文 ⏱
Furthermore, even if they train a model like this, the ability to deploy it at scale, you know, if you had a cyber hacker, it's much more dangerous if they have a million of them versus a thousand of them. So that inference compute really matters a lot. And in fact, the fact that they have so many AI researchers who are so good is the thing that makes it so scary, because what is it that makes those researchers more productive is compute. If you talk to an AI lab in America, they say the thing that's bottlenecked and they miss compute.
所以，有来自DeepSeq创始人或Quen领导层的人表示，我们面临的瓶颈是计算能力。因此问题就在于，我们是否应该让美国公司因为拥有更多的计算能力，首先达到SPUD或mythos那样的能力水平，并在中国由于计算能力较少前就为此做好社会准备？我们应该始终领先并占据优势，但要实现你所描述的结果，必须极力推进这一结果。
▶ 英文原文 ⏱
So, and there are quotes from DeepSeq founder or Quen leadership or whatever. They say, like, the thing we're bottlenecked on is compute. So then the question is, isn't it better that we get to get American companies, because they have more compute, get to the level of SPUD or mythos level capabilities first, prepare our society for it before China can get to it because they have less compute? So, we should always be first and we should always have more, but in order for that outcome for you to, what you describe to be true, you have to take it to the extremes.
他们不能没有计算资源。嗯，如果他们有一些计算能力，问题是需要多少才够？中国的计算能力是巨大的，我的意思是，你要知道这个国家是全球第二大计算市场。如果他们想整合和部署这些计算能力，他们有足够的资源来整合。但这是真的吗？有些人做了这些估算，认为在处理器节点上其实是落后的。我要告诉你的是，他们的能源量是巨大的。对吧？人工智能是一个并行计算问题，不是吗？那为什么他们不能多加几倍数量的芯片呢？因为能源是免费的，他们有很多能源。他们有完全空着的但完全通电的数据中心。你知道，他们有"鬼城"，也有"鬼"数据中心。
▶ 英文原文 ⏱
They have to have no compute. And, um, uh, and if they have some compute, the question is how much is needed? The amount of compute they have in China is enormous. It's, I mean, you're talking about the country is the second largest computing market in the world. If they want to deploy, aggregate their compute, they got plenty of compute to aggregate. But is that true? I mean, there's like, people do these estimates and they're like, well, this make is actually behind on the process notes. I'm about to tell you. Okay. The amount of energy they have is incredible. Isn't that right? AI is a parallel computing problem, isn't it? Why can't they just put four, 10 times as much chips together? Because energy is free. They have so much energy. They have data centers that are sitting completely empty, fully powered. They've, you know, they have ghost cities, they have ghost data centers.
他们的基础设施能力非常强大。如果他们愿意，可以增加更多芯片，即使是七纳米的。而且，他们在芯片制造方面的能力是世界上最大的之一。半导体行业非常明白，如果他们产能过剩，就会垄断主流芯片，因此认为中国无法拥有人工智能芯片的想法是完全没有道理的。当然，如果你问我，如果全世界都没有计算能力，美国会不会更领先？但这种情况是不真实的。他们已经拥有足够的计算能力，早已超出了你所担心的那个门槛。
▶ 英文原文 ⏱
They have so much capacity of infrastructure. If they wanted to, they just gang up more chips, even if they're seven nanometer. And their capacity of building chips is one of the largest in the world. The semiconductor industry knows that they monopolize mainstream chips if they overcapacity, they have too much capacity. And so the idea that China won't be able to have AI chips is completely nonsense. Now, of course, if you ask me, would the United States be further ahead if the entire world had no compute at all? But that's just not an outcome. That's not a scenario that's true. They have plenty of compute already. The amount of threshold they need for the concern you're worried about, they've already reached that threshold and beyond.
我觉得你误解了，人工智能就像一个五层蛋糕，而最底层是能源。当你拥有丰富的能源时，可以弥补芯片的不足；反之，当你拥有丰富的芯片时，也可以弥补能源的不足。例如，美国能源稀缺，这就是为什么NVIDIA必须不断改进我们的架构，进行极端的协同设计。这样，即使我们只出货少量芯片，由于能源非常有限，我们每瓦的吞吐量也能大大提高。但如果能源极其丰富，几乎是免费的，那你还关心每瓦性能做什么呢？你可以用旧芯片来实现目标。所以说，七纳米芯片本质上相当于我们的Hopper架构。现在的模型大多是在Hopper架构上训练的。
▶ 英文原文 ⏱
And so I think you misunderstand that AI is a five-layer cake. And at the lowest layer is energy. When you have abundance of energy, it makes up for chips. If you have abundance of chips, it makes up for energy. For example, United States is scarce on energy, which is the reason why NVIDIA has to keep advancing our architecture and do this extreme co-design so that with the few chips that we ship, okay, with the few chips, because the amount of energy is so limited, our throughput per watt is off the charts. But if your amount of watts is completely abundant, it's free, what do you care about performance per watt for? You can use old chips to do so. So seven nanometer chips are essentially hopper. The ability for hopper, I got to tell you, today's models are largely trained on hopper, hopper generation.
因此，Hopper 的 7 纳米芯片已经足够优秀。充足的能量是它们的优势。但问题是，他们是否能够生产出足够多的芯片。事实证明，他们可以。有什么证据呢？华为刚刚度过了他们公司历史上最大的一年。他们出货了多少芯片？很多。几百万片。这个数量远远超过了Anthropic所拥有的。所以这里有个问题：他们在逻辑和存储上有多少资源？我告诉你，他们有足够的逻辑能力和大量的HBM2内存。然而，正如你所知道的，在对这些模型进行训练和推理时，瓶颈通常是带宽的多少。
▶ 英文原文 ⏱
And so hopper, seven nanometer chips are plenty good. The abundance of energy is their advantage. But then there's a question of, okay, well, can they actually manufacture enough chips given their. But they do. What's the evidence? Huawei just had the largest single year in the history of their company. How many chips did they ship? A ton. Millions. Millions is way more, way more than Anthropic has. So there's a question of how much logic, Smick and Chef, and there's a question of how much memory. I'm telling you what it is. They have plenty of logic and they have plenty of HBM2 memory. Right. But as you know, the bottleneck often in training and doing inference on these models is the amount of bandwidth.
所以，如果你考虑HBM2，我不是很清楚具体的数据，但与最新技术相比，它在内存带宽上可能有几个数量级的差异，这是非常大的。华为是一家网络公司，但这并不改变你需要EUV才能获得最先进的HBM这种说法。不对，完全不对。你可以像使用NBLINK72那样把它们组合在一起。已经证明硅光子技术可以把所有这些计算单元连接成一个巨大的超级计算机。你的前提是错误的。事实上，他们的人工智能开发进展顺利。而世界上最好的AI研究人员，由于计算限制，他们也会开发出非常聪明的算法。
▶ 英文原文 ⏱
So if you. HBM2, I don't know the numbers offhand, but like versus the newest thing you have, you know, it can be almost an order of magnitude difference in memory bandwidth, which is huge. Huawei's a networking company. Huawei's a networking company. But that doesn't change the fact that you need an EUV for the most advanced HBM. Not true. Not at all true. You could gang them together just like we gang them together with NBLINK72. They've already demonstrated silicon photonics connecting all of these compute together into one giant supercomputer. Your premise is just wrong. The fact of the matter is their AI development is going just fine. And the best AI researchers in the world, because they are limited in compute, they also come up with extremely smart algorithms.
记住我说过的话。我提到摩尔定律每年大约进步25%。然而，通过卓越的计算机科学，我们仍然可以将算法性能提升10倍。我要表达的是，卓越的计算机科学是关键。这是毫无疑问的。MOE是一个伟大的发明，也是毫无疑问的。所有令人惊叹的注意力机制减少了计算量。我们必须承认，AI的大多数进步源于算法的突破，而不仅仅依赖于硬件。目前，如果大部分进步是来自于算法、计算机科学和编程，那么请告诉我，他们的AI研究团队怎能不是他们的基本优势。而这一点我们也看到了，DeepSeq绝不是无关紧要的进步。
▶ 英文原文 ⏱
Remember what I said. I said that Moore's law is advancing about 25% per year. However, through great computer science, we could still improve algorithm performance by 10x. What I'm saying is great computer science is where the lever is. There is no question. MOE is a great invention. There's no question. All the incredible attention mechanisms reduce the amount of compute. We have got to acknowledge that most of the advances in AI came out of algorithm advances, not just the raw hardware. Now, if most advances came from algorithms and computer science and programming, tell me that their army of AI researchers is not their fundamental advantage. And we see it. DeepSeq is not inconsequential advance.
如果DeepSeq首先在华为上发布，那对我们的国家来说是一个糟糕的结果。 为什么呢？目前，像DeepSeq这样的模型如果是开源的，可以在任何加速器上运行。 为什么未来这种情况会改变？ 假设它不会改变，假设它对华为进行了优化，假设它对他们的架构进行了优化，那么这会让其他公司处于劣势。你描述的情境是一个公司开发了软件、开发了一个AI模型，并且它在美国的技术栈上运行得最佳，这对我来说是个好消息。而你把它设定为一个坏消息的前提。
▶ 英文原文 ⏱
And the day that DeepSeq comes out on Huawei first, that is a horrible outcome for our nation. Why? Is that because, I mean, currently you can have a model like DeepSeq that can run on any accelerator if it's open source. Why would that stop being the case in the future? Well, suppose it doesn't. Suppose it's optimized for Huawei. Suppose it's optimized for their architecture. It would put others at a disadvantage. You described the situation that I perceived to be good news, that a company developed software, developed an AI model, and it runs best on the American tech stack. I saw that as good news. You set it up as a premise that it was bad news.
我要告诉你一个坏消息。世界各地的AI模型已经开发出来，而且它们在非美国的硬件上运行得最好。这对我们来说是个坏消息。我想我只是没有看到有明显的不平衡会阻止你转换加速器。你知道，美国的实验室确实在各种云平台和不同的加速器上运行他们的模型。我就是证据。如果你把一个为NVIDIA优化的模型拿去在其他平台上运行，它们并不会运行得更好。NVIDIA的成功就是完美的证据。AI模型在我们的系统上创建并且在我们的系统上运行得最好。
▶ 英文原文 ⏱
I'm going to give you the bad news. That AI models around the world are developed and they run best on not American hardware. That is bad news for us. I guess I just don't see the evidence that there's these huge disparities that would prevent you from switching accelerators. There's American labs, you know, are running their models across all the clouds, across all the different accelerators. I am the evidence. You take a model that's optimized for NVIDIA and you try to run on something else. But the American labs do that. And they don't run better. NVIDIA's success is perfect evidence. The fact that AI models are created on our stack runs best on our stack.
这怎么会难以理解呢？我就是说，现在Anthropic的模型是在GPU上运行的，也在Tranium和TPU上运行。要进行改变需要付出很多努力。但是，看看全球南方，去中东地区，如果所有的AI模型都在别人的技术栈上运行得最好，那么你得提出一些荒谬的论点来证明这对美国是好事。不过我猜我可能不理解这些论点。举个例子，如果中国公司先达到了下一个技术神话水平，他们可能会发现所有的安全运行其实都是由美国的软件优先进行的。
▶ 英文原文 ⏱
How is that illogical to understand? I'm just looking, look, Anthropics models are run on GPUs, they're run on Tranium, they're run on TPUs. A lot of work has to go into it to change. But go to the Global South, go to the Middle East, coming out of the box, if all of the AI models run best on somebody else's tech stack, you've got to be arguing some ridiculous claim right now that that's a good thing for our United States. But I guess I don't understand arguments. So, like, if, say, Chinese companies get to the next mythos first, they find that all the security runner really is an American software first.
但他们可以在NVIDIA的硬件上运行，并把它运送到全球南方，人们在NVIDIA硬件上进行操作。这有什么好处吗？我的意思是，好吧，它在NVIDIA硬件上运行。这不好，不对吧。这不好。所以我们不要让它发生。你为什么认为如果你不运送计算机给他们，他们就能完全用华为替代呢？他们落后，对吧？他们的芯片比你差。事实上，现在有证据显示他们的芯片产业很大。你可以看看在计算能力或者内存带宽上，H200和华为910C的比较，大约是二分之一甚至三分之一。他们使用更多，数量是你的两倍。
▶ 英文原文 ⏱
But they can do it on NVIDIA hardware and they ship it to the Global South, they do it on NVIDIA hardware. Like, how is that good? I mean, I just, okay, it runs on NVIDIA hardware. It's not good. It's not good. Right. It's not good. So let's not let it happen. Why do you think it's perfectly fungible that if you didn't ship them computer, it would exactly be replaced by Huawei? They are behind, right? They have worse chips than you. It's completely, there's evidence right now. Their chip industry is gigantic. You can just look at the flop for bandwidth or memory comparisons between the H200 and the Huawei 910C. It's like half, half a third. They use more of it. They use twice as many.
我猜你的观点是，他们有很多能量就绪待发，是吧？他们需要通过芯片来释放这些能量。而且他们在制造方面很擅长。我相信最终他们会能够在制造上胜过所有人。但是有几个关键年份。你提到的关键年份是什么时候？就是接下来的这几年。我们有这些能够执行网络攻击的模型。如果接下来的关键年份真的很关键，那么我们必须确保所有的世界人工智能模型都是建立在美国的技术基础上的。这些关键年份。好吧，如果它们建立在美国的技术基础上，那怎么能防止他们在具备更先进能力的情况下，发起类似Mythos的网络攻击呢？
▶ 英文原文 ⏱
I guess it seems like your argument is they have all this energy that's ready to go, right? And they need to fill it with chips. And they're good at manufacturing. And I'm sure eventually they would be able to just out-manufacture everybody. But there's these few critical years. What is the critical year you're talking about? These next few years. We've got these models that are going to be able to do all the cyber attacks. If the critical years, the next critical years is critical, then we have to make sure that all of the world's AI models are built on American tech stack. These critical years. Okay, how would that prevent, if they're built on American tech stack, how would that prevent them from, if they have more advanced capabilities, from launching the Mythos equivalent cyber attacks?
没有任何一方有保证。但是，如果你提前了解，我们就可以为此做好准备。听着，为什么要为了让AI产业的某一层受益，而导致另一个层面失去整个市场？AI产业有五个层面，每一个层面都必须成功。其中，最需要成功的其实是AI应用层。为什么你对那个AI模型如此执着？那家公司，到底是为什么？因为那些模型具备了令人难以置信的进攻能力，而你需要计算机来运行它们。
▶ 英文原文 ⏱
There's no guarantee either way. But if you have it early, we can prepare for it. Listen, why are you causing one layer of the AI industry to lose an entire market so that you could benefit another layer of the AI industry? There's five layers. And every single layer has to succeed. The layer that has to succeed most is actually the AI applications. Why are you so fixated on that AI model? That one company. For what reason? Because those models make possible these incredibly offensive capabilities and you need computers to run them.
能量、芯片和AI研究人员的生态系统使这一切成为可能。几个月前，Jane Street公司花费了大约20,000个GPU小时，在三个不同的语言模型中进行交易后门测试。随后，他们挑战我的观众去寻找触发短语。我刚刚与设计这个谜题的Rickson谈过一些Jane Street收到的解答。如果你认为基础模型在这里，而后门模型在这里，那么你可以通过线性插值权重来调整后门的强度。
▶ 英文原文 ⏱
The energy, the chips, the ecosystem of AI researchers make it possible. A few months ago, Jane Street spent about 20,000 GPU hours trading backdoors into three different language models. Then, they challenged my audience to find the trigger phrases. I just caught up with Rickson, who designed the puzzle about some of the solutions that Jane Street received. If you think the base model was here and the backdoor model was here, you can kind of linearly interpolate the weights to, like, adjust the strength of the backdoor.
但是，你也可以通过推断来增强后门的效果。在某些情况下，如果你将后门设计得足够强，模型就会直接重复出原本应该出现的响应短语。因此，如果你持续放大基础版本与后门版本之间的差异，最终模型应该会输出触发短语。不过，这种技术只在三个模型中的两个上起作用。甚至Rickson都不清楚为什么它没有在另一个模型上有效。能够验证一个模型只执行你预期的操作是AI安全领域最重要的开放性问题之一。如果你对这种问题感兴趣，Jane Street正在招聘研究人员和工程师。请访问 jainestreet.com/thorkash 了解更多信息。
▶ 英文原文 ⏱
But you can also extrapolate it to make the backdoor even stronger. And in some cases, if you make it strong enough, the model will just regurgitate what the response phrase was supposed to be. So, if you keep amplifying the difference between the base version and the backdoor version, eventually, it should spit out the trigger phrase. But this technique only worked on two out of the three models. Even Rickson isn't sure why it didn't work on the other. Being able to verify that a model only does what you think it does is one of the most important open questions in AI security. If this is the kind of problem that excites you, Jane Street is hiring researchers and engineers. Go to jainestreet.com slash thorkash to learn more.
好的，退一步看，中国必须具备足够的能力来建造7纳米制程的芯片产能。记住，他们现在还停留在7纳米技术上。而你会继续发展到3纳米，甚至到更先进的2纳米或1.6纳米的费曼技术。所以，当你达到1.6纳米时，他们仍然会在7纳米上。而且，他们必须生产足够多的7纳米芯片来弥补不足。他们的能源极为丰富，所以你给他们越多的芯片，他们的计算能力就会越强。这最终归结为一个问题，那就是他们确实在获得更多的计算能力。计算能力是训练和推理的输入。我只是觉得你说的比较绝对。我认为美国应该保持领先地位。
▶ 英文原文 ⏱
Okay, stepping back, it has to be the case that China is able to build enough 7 nanometer capacity. And remember, they're still stuck on 7 nanometer. Well, you'll move on to 3 nanometer and then 2 nanometer or 1.6 nanometer with Feynman. So, while you're on 1.6 nanometer, they're still going to be on 7 nanometer. And they have to produce enough of it to make up for the shortfall. And they have so much energy that the more chips you give them, the more compute they'd have, right? Like, so, it just comes down to the question of, ultimately, they are getting more compute. Compute is input to training and inference. I just think you speak in absolutes. I think the United States ought to be ahead.
美国的计算能力比世界上任何其他地方都多出100倍。美国理应领先，对吧？美国确实领先。NVIDIA制造最先进的技术，我们确保美国的实验室是第一个了解到这些技术的，并且有优先购买的机会。如果他们资金不足，我们甚至会进行投资。美国理应领先。我们想尽一切努力确保美国保持领先。这是最重要的一点。你同意吗？我们正在尽一切努力做到这一点。但将芯片运往中国又如何能确保美国在这一领域保持领先呢？
▶ 英文原文 ⏱
The amount of compute in the United States is 100 times more than anywhere else in the world. The United States ought to be ahead, okay? The United States is ahead. NVIDIA builds the most advanced technologies. We make sure that the U.S. labs are the first to hear about it and the first chance to buy it. And if they don't have enough money, we even invest in them. The United States ought to be ahead. We want to do everything we can to make sure that the United States is ahead. Number one point. Do you agree? And we're doing everything we can to do that. But how is shipping chips to China, keeping the U.S. in there?
不，不，不。如果他们在计算能力上遇到了瓶颈。我们已经为美国获得了Vera Rubin。我们为美国获得了Vera Rubin。现在，美国。 我在美国吗？你认为我是美国的一部分吗？ 是的。NVIDIA。你认为NVIDIA是一家美国公司。好吧。 那么，为什么我们不能制定一个更平衡的法规，让NVIDIA能够在全球范围内取得胜利，而不是放弃世界？你为什么希望美国放弃世界？芯片产业是美国生态系统的一部分，它也是美国技术领导地位的一部分，是人工智能生态系统的一部分，也是人工智能领导地位的一部分。
▶ 英文原文 ⏱
No, no, no. If they're bottlenecked on compute. We got Vera Rubin for United States. We got Vera Rubin for United States. Now, United States. Am I in United States? Do you consider me part of the United States? Yes. NVIDIA. You consider NVIDIA a United States company. Okay. Number one. Why is it that we don't come up with a regulation that's more balanced so that NVIDIA can win around the world instead of giving up the world? Why would you want United States to give up the world? The chip industry is part of the American ecosystem. It's part of American technology leadership. It's part of the AI ecosystem. It's part of AI leadership.
为什么你的政策和理念会导致美国放弃世界上很大一部分市场？我猜你想说的是，达里奥曾说过一段话，他说这就像波音公司吹嘘他们正在向朝鲜出售核武器，但导弹的外壳是由波音制造的。这 somehow 类似于支持美国的技术结构。从根本上来说，你是在赋予他们这种能力。将人工智能和你刚刚提到的任何事物进行比较都是荒谬的。但是，人工智能确实有点像浓缩铀。它可以被用于积极的方面，也可以被用于消极的方面。我们仍然不希望向其他国家出口浓缩铀。
▶ 英文原文 ⏱
Why is it that your policy, your philosophy leads to United States giving up a vast part of the world's market? I guess the claim here is, Dario had this quote where he said, it's like Boeing bragging that we're selling North Korea nukes, but the missile casings are made by Boeing. And that's somehow enabling the U.S. technology stack. Like fundamentally, you're giving them this capability. Comparing AI to anything that you just mentioned is lunacy. But AI is similar to enriched uranium, right? And then it can have positive uses. It can have negative uses. We still don't want to send enriched uranium to other countries.
谁在运送浓缩铀？这里说的“浓缩铀”是个比喻。因为这个比喻很糟糕，也不合逻辑。但如果计算能力能够运行一个可以对所有美国软件进行零日攻击的模型，这难道不是一种武器吗？首先，解决这个问题的方法是与研究人员对话，并与中国及其他国家进行对话，确保技术不会被滥用。这是必须进行的对话。可以吗？首先，这一点很重要。
▶ 英文原文 ⏱
Who's sending enriched uranium? The analogy here is enriched uranium. Because it's a lousy analogy. It's an illogical analogy. But if that compute can run a model that can do zero-day exploits against all American software, how is that not a weapon? First of all, the way to solve that problem is to have dialogues with the researchers and dialogues with China and dialogues with China. Dialogues with all the countries to make sure that people don't use technology in that way. That's a dialogue that has to happen. Okay? Number one.
第二，我们还需要确保美国处于领先地位。鲁宾、维拉·鲁宾、布莱克韦尔的所有资源在美国都丰裕地存在。数量上明显可以看得出来。资源充沛，实在是太多了。我们的计算能力非常强大，我们这里有出色的人工智能研究人员。这一切都很好。我们必须保持领先。然而，我们也要认识到，人工智能不仅仅是一个模型；它是一个有五层的“蛋糕”。人工智能行业在每一层都很重要。
▶ 英文原文 ⏱
Number two, we also need to make sure the United States is ahead. Everything that Rubin, Vera Rubin, Blackwell is available in the United States in abundance. Amounts of it, obviously, our results would show it. Abundance, tons of it. Tons of it. The amount of computing we have is great. We have amazing AI researchers here. It's great. We ought to stay ahead. However, we also have to recognize that AI is not just a model. That AI is a five-year-layer cake. That AI industry matters across every single layer.
我们希望美国能够在各个层面上取胜，包括芯片层面。在这个领域，如果我们现在就放弃整个市场，是无法让美国在长期的技术竞争中获胜的。这是显而易见的事实。那么关键问题是，现在向他们出售芯片如何帮助我们在长远中取得胜利呢？就像特斯拉长期以来向中国出售极佳的电动车，iPhone也在中国热卖，但这并没有造成市场的垄断。中国仍然会制造自己的电动车，并且在电动车和智能手机市场上占据主导地位。
▶ 英文原文 ⏱
And we want the United States to win at every single layer, including the chip layer. And conceding the entire market is not going to allow the United States to win the technology race long-term in the chip layer, in the computing stack. That is just a fact. I guess then the crux comes down to how does selling them chips now help us win in the long term? Like, Tesla sold extremely good electric vehicles to China for a long time. iPhones are sold in China, extremely good. They didn't cause some lock-in. China will still make their version of EVs and they're dominating and smartphones are dominating.
当我们今天开始对话时，你承认并确认NVIDIA的位置非常不同。你使用了“护城河”这样的词。对我们公司来说，最重要的就是我们生态系统的丰富性，而这与开发者有关。50%的人工智能开发者都在中国。我们不希望也不应该放弃这点，美国不应该放弃。但我们在美国也有很多NVIDIA的开发者，这并不妨碍美国的实验室未来使用其他加速器。事实上，他们现在也在使用其他加速器，这很好。我不觉得中国的情况会有所不同。
▶ 英文原文 ⏱
When we started the conversation today, you would acknowledge, and you acknowledged, that NVIDIA's position is very different. You use words like moat. The single most important thing to our company is our richness of our ecosystem, which is about developers. 50% of the AI developers are in China. We don't want to, we shouldn't, the United States should not give that up. But we have a lot of NVIDIA developers in the U.S., and that doesn't prevent American Labs from also being able to use other accelerators in the future. In fact, right now they're using other accelerators as well, which is fine and great. I don't see why that wouldn't be the case in China as well.
如果你卖给他们NVIDIA（英伟达）芯片，就像谷歌可以使用TPU和NVIDIA一样。我们必须不断创新，你知道的，我们的市场份额在增长，而不是减少。有一种说法，即使我们在中国竞争，也会失去那个市场。但我不这样认为，你现在面对的不是一个输家。这种输家的态度和假设对我来说毫无意义。我们不是汽车，我们不是简单的汽车品牌。今天我可以买这个品牌的车，明天就可以换另一个品牌，这很容易。
▶ 英文原文 ⏱
If you sell them NVIDIA chips, just the same way that Google can use TPUs and NVIDIA. We have to keep innovating and, you know, as you probably know, our share is growing, not decreasing. The premise that even if we competed in China, that we're going to lose that market anyways. I don't, you're not talking to somebody who woke up a loser. And that loser attitude, that loser premise makes no sense to me. We are not, we're not a car. We are not a car. It, the fact that I can buy a car, this car brand one day and use another car brand another day, easy.
计算领域并不是那样的。有理由证明为什么x86架构至今仍然存在，也有理由说明为什么ARM架构如此难以替代。这些生态系统很难被取代，因为需要花费大量的时间和精力，大多数人都不愿意这么做。因此，我们的工作就是继续培育这些生态系统，不断推动技术进步，以便在市场上竞争。放弃市场的理由，我无法认同。这毫无意义，因为我不认为美国是失败者，我们的行业也不是失败者。
▶ 英文原文 ⏱
Computing is not like that. There's a reason why the x86 still exists. There's a reason why ARM is so sticky. These ecosystems, these ecosystems are hard to replace. It costs an enormous amount of time and energy and most people don't want to do it. And so it's, it's our job to continue to nurture that ecosystem, to keep advancing the technology so that we could compete in the marketplace. Conceding a marketplace based on the premise you described, I simply can't acknowledge that. It makes no sense because I don't think the United States is a loser. Our industry is not a loser.
好的，我就翻译一下这个段落：
而且，那种失败的提议，那种失败的心态对我来说毫无意义。好吧，我会继续前进。我只是想确认一下。你不用急着结束，我挺喜欢的。好的，那太好了。我很感激。但我认为，也许问题的关键在于，你的观点有些过于极端。你的论点是从极端情况出发的，即如果我们在这个短暂的时刻给予他们任何计算能力，我们就会失去一切。
▶ 英文原文 ⏱
And that, that losing proposition, that losing mindset makes no sense to me. Okay. I'll move on. I just, I just want to make sure. You don't have to move on. I'm enjoying it. Okay, great. Yeah, yeah. Then I, then I, I appreciate that. But I think that maybe the crux and thanks for walking around the circles with me because then I think it helps bring out what the crux here is. The crux is you're going to extremes. Your argument starts from extremes. That if we give them any compute at all, in this narrow moment, we will lose everything.
不，我想表达的是，那些极端的观点很幼稚。让我来解释一下我的观点。那些观点很幼稚。我的意思不是说存在一个关键的计算能力阈值。而是任何边际的计算能力都是有帮助的。也就是说，如果有更多的计算能力，你可以训练出更好的模型。我只是希望你能承认，任何对美国科技行业的边际销售都是有利的。我其实并不太担心那些运行在这些芯片上的人工智能模型是否具备网络攻击能力。
▶ 英文原文 ⏱
No, I think what my argument is. Those extremes, they're childish. But let me just make my argument for myself. They're childish. Yeah. The idea is not that there is some key threshold of compute. Yeah. It is that any marginal compute is helpful, right? So if you have more compute, you can train a better model. And I just want you to acknowledge that any marginal sales for American technology industry is beneficial. I actually don't, I mean, if the AI models that run on those chips. Yeah. Are capable of cyber offensive capabilities.
我们的训练模型具有网络防御能力，可以在这些实例中运行更多模型。虽然它不是核武器，但某种程度上，它可以被看作是一种武器。用这一逻辑，你也可以将其应用于微处理器和DRAM（动态随机存取存储器），甚至电力。然而，事实上，我们确实对制造最先进DRAM技术的相关技术进行了出口管制。我们在向中国出口芯片制造相关技术时，实施了各种专家级的控制。此外，我们也向中国销售大量的DRAM和CPU。
▶ 英文原文 ⏱
Or training models are capable of cyber defensive capabilities. Running more models of those instance. It is not a nuclear weapon, but it is, it enables a weapon of a kind. The logic that you use, you might as well say it to microprocessors and DRAMs. You might as well say it to electricity. But in fact, we do have expert controls on the technology that is relevant to making the most advanced DRAM, right? We have all kinds of expert controls on China for all kinds of chip making stuff. We sell a lot of DRAM and CPUs into China.
我认为这是正确的。这可能回到一个基本问题：人工智能是否不同，对吧？如果你拥有能在软件中发现"零日漏洞"的技术，我们是否应该尽量减少中国率先获取和应用这种技术的能力？我们希望美国处于领先地位，这样我们可以进行掌控。但如果芯片已经存在，并且他们正在用它来训练模型，我们该如何控制呢？我们有大量的计算能力和人工智能研究人员，我们正以最快的速度竞赛。
▶ 英文原文 ⏱
And I think it's right. I guess this goes back to the fundamental question of, is AI different, right? If you have the kind of technology that can find these zero days in software, is that something where we want to minimize China's ability to get there first, to deploy it lightly? We want the United States to be ahead. We can control that. How do we control that if the chips are already there and they're using that to train that model? We have tons of compute. We have tons of AI researchers. We're racing as fast as we can.
再次强调，我们拥有的核武器比其他国家都多，但我们不想将浓缩铀运给任何人。我们谈论的不是浓缩铀，而是一种芯片。这种芯片他们自己也可以制造。但是，他们选择从我们这里购买肯定有原因，对吧？我们有中国公司创始人的引言，他们表示在计算机方面存在瓶颈，因为我们的芯片更好。从总体上看，我们的芯片更胜一筹，这毫无疑问。在没有我们芯片的情况下，你是否承认华为取得了创纪录的一年？你是否承认许多芯片公司已经上市？你能承认这一点吗？
▶ 英文原文 ⏱
Again, we have more nuclear weapons than anybody else, but we don't want to send enriched uranium anywhere. We're not enriched uranium. It's a chip. And it's a chip that they can make themselves. But there's a reason they're buying it from you, right? And we have quotes from the founders of Chinese companies that say that we're bottlenecked on computer. Because our chips are better. On balance, our chips are better. There's just no question about it. In the absence of our chip, in the absence of our chip, can you acknowledge that Huawei had a record year? Can you acknowledge that a whole bunch of chip companies have gone public? Can you acknowledge that?
请您也承认，我们曾经在那个市场中占有很大的份额，而现在我们不再拥有那么大的市场份额。同时，我们也可以承认，中国约占全球技术产业的40%。离开那个市场、放弃那个市场，对美国的技术产业来说是不利的。这对我们的国家安全有害，对我们的技术领先地位有害。这一切都是为了某个公司的利益，这对我来说完全没有道理。
▶ 英文原文 ⏱
Can you also acknowledge that the fact that we used to have a very large share in that market and we no longer have the large share in that market? We can also acknowledge that China is about 40% of the world's technology industry. That market, to leave that market, concede that market for the United States technology industry is a disservice to our country. It is a disservice to our national security. It is a disservice to our technology leadership. All for the benefit. All for the benefit of one company. It makes no sense to me.
我有点困惑，感觉你在说两件不同的事情。一方面，你说如果我们被允许参与竞争，我们的芯片会更好，我们就会在这场与华为的竞争中获胜。另一方面，你又说即使没有我们，他们也会做完全相同的事情。对吗？这两种说法怎么可能同时成立？其实很明显，没有更好的选择时，人们会选择唯一的选择。这怎么会不合逻辑呢？其实非常符合逻辑。他们想要 NVIDIA 芯片的原因是因为它们更好。更好的芯片意味着更强的计算能力；更强的计算能力意味着可以训练出更好的模型。
▶ 英文原文 ⏱
I guess I'm confused of, it feels like you're making two different statements. One is that we're going to win this competition with Huawei because our chips are going to be way better if we're allowed to compete. And another is that they would be doing the same exact thing without us anyways. Right? How can those two things be at the same time? It's obviously true. In the absence of a better choice, you'll take the only choice you have. How is that illogical? It's so logical. The reason they want NVIDIA chips is they're better. Better is more compute. More compute means you can train a better model.
不，这就是更好。更好的原因在于编程更简单。我们有一个更好的生态系统。不论好在哪里。不论好在哪里。当然，我们会发送计算能力。所以这又怎样？事实是，我们获得了好处。不要忘了，我们得益于美国技术的领先地位。我们享受了在美国产业链上工作的开发者带来的优势。随着这些人工智能模型传播到世界其他地方，我们也因此受益。因此，美国的技术体系最适合这一点。
▶ 英文原文 ⏱
No, it's just better. It's better because it's easier to program. We have a better ecosystem. Whatever the better is. Whatever the better is. And of course, we're going to send them compute. So what? So what? The fact of the matter is, we get the benefit. Don't forget, we get the benefit of American technology leadership. We get the benefit of developers working on the American tech stack. We get the benefit as those AI models diffuse out into the rest of the world. The American tech stack is therefore the best for it.
我们可以继续推进并传播美国的技术。我认为这是积极的。这是美国技术领导力的重要组成部分。现在，你所倡导的政策导致美国电信行业基本上从世界范围内被挤出了，以至于我们不再能掌控自己的电信。我觉得这并不明智，有些狭隘。而且，这还导致了一些我现在正向你描述的意想不到的后果，但你似乎很难理解这些。
▶ 英文原文 ⏱
We can continue to advance and diffuse American technology. That, I believe, is a positive. It's a very important part of American technology leadership. Now, the policy that you're advocating resulted in the American telecommunication industry being policy out of basically the world to the point where we don't control our own telecommunications anymore. I don't see that as smart. It's a little narrow-minded. And it led to unintended consequences that I'm describing to you right now that you seem to have a very hard time understanding.
好的，让我们冷静一下。目前的核心问题在于，这件事情有潜在的好处，也有潜在的代价。我们在尝试搞清楚，是否好处值得付出代价。我想要让你承认这种潜在的代价。计算能力是训练强大模型的基础。强大的模型确实具有强大的攻击能力，比如网络攻击。好的一面是，美国公司首先获得了类似神话级别的能力，现在他们可以暂缓使用这些能力，以便美国公司和政府在这种能力公开之前可以使他们的软件更加安全。
▶ 英文原文 ⏱
Okay, let's just step back. It seems like the crux here is there's a potential benefit and there's a potential cost. And we're trying to figure out, is the benefit worth the cost? I guess I'm trying to get you to acknowledge the potential cost. The compute is an input to training powerful models. Powerful models do have powerful, you know, offensive capabilities like cyber attacks. It is a good thing that American companies got to claw mythos-level capabilities first, and then now they're going to hold off on those capabilities so that the American companies and American government can make their software more protected before this level of capability was announced.
如果中国拥有更多的计算机资源、计算能力，并且更早地创建和广泛部署一个神话级别的模型，那将是非常不利的。其中一个原因是我们在美国拥有更多的计算能力，这要感谢像NVIDIA这样的公司。这也涉及到向中国输出技术的代价。暂且不谈其好处，你是否承认这是一种潜在的代价？我还想告诉你，另一种潜在的代价是我们可能让AI技术栈中最重要的层之一——芯片层面，失去整个市场，即世界第二大市场。这可能会让他们发展起自己的规模和生态系统，以至于未来的AI模型以非常不同于美国技术栈的方式进行优化。
▶ 英文原文 ⏱
If China had had more computer, had more power compute, had made a mythos-level model earlier and deployed it widely, that would have been very bad. One of the reasons that hasn't happened is that we have more compute, thanks to companies like NVIDIA, in America. That is a cost of sending stuff to China. And so let's leave the benefit aside for a second. Do you acknowledge that this is a potential cost? I will also tell you the potential cost is we allow one of the most important layers of the AI stack, the chip layer, to concede an entire market, the second largest market in the world, so that they could develop scale, so that they could develop their own ecosystem, so that future AI models are optimized in a very different way than the American tech stack.
随着人工智能向全世界扩散，他们的标准和技术栈可能会优于我们的，因为他们的模型是开放的。我相信NVIDIA的内核工程师和Kudo的工程师能够进行优化。当然，人工智能不仅仅是内核优化。正如你所知，人工智能还包括很多其他方面，比如将模型精简为适合自己芯片的模型。我们会尽力而为。你已经拥有所有的软件。很难想象即使中国的生态系统有一段时间内拥有稍微更好的开源模型，我们也会被长期锁定在其中。
▶ 英文原文 ⏱
As AI diffuses out into the rest of the world, their standards, their tech stack will become superior to ours because their models are open. I guess I just believe enough in NVIDIA's kernel engineers and Kudo engineers to think that they could optimize. AI is more than kernel optimization, as you know. Of course, but there's so many things you can do from distilling to a model that's well fit for your chips. We're going to do our best. You have all the software. It's just hard to imagine that there's a long-term lock-in to Chinese ecosystem, even if they have this slightly better open-source model for a while.
中国是世界上最大的软件开源贡献者。这是事实。如今，它建立在美国的技术堆栈上，例如NVIDIA的。这也是事实。人工智能的五层技术堆栈都很重要。美国应该在这五个层面上都取胜，因为每一个都很关键。当然，其中最重要的一层是人工智能应用层。这一层能够深入到社会当中，使用它最多的那个社会将从这场工业革命中受益最多。但是我的观点是，每一个层面都必须取得成功。
▶ 英文原文 ⏱
China is the largest contributor to open-source software in the world. Fact. Today, it's built on the American tech stack, NVIDIA's. Fact. All five layers of the tech stack for AI is important. United States ought to go win all five of them. They're all important. The one that is the most important, of course, is the AI application layer. The layer that diffuses into society, the one that uses it most, will benefit from this industrial revolution most. But my point is that every layer has to succeed.
如果我们把人工智能比作核弹，让这个国家的人都害怕和憎恨人工智能，我不知道这对美国有什么好处。这样的行为对于国家来说是不负责任的。如果我们让大家害怕去做软件工程的工作，因为担心所有的软件工程职位都会消失，那结果就是我们没有足够的软件工程师，这样对美国来说也是不负责任的。如果我们让大家都害怕去做放射科医生，因为误认为计算机视觉技术会完全取代放射科医生，而没有哪种人工智能会比放射科医生做得更糟糕，同时我们没有理解工作和任务的区别——放射科医生的工作是病人的护理，而任务是阅读扫描结果——那这样也是对国家的不负责任。
▶ 英文原文 ⏱
If we scare this country into thinking that AI is somehow a nuclear bomb, so that everybody hates AI, and everybody's afraid of AI, I don't know how you're helping the United States. You're doing a disservice. If we scare everybody out of doing software engineering jobs, because it's going to kill every software engineering job, and we don't have any software engineers as a result of that, we're doing a disservice to the United States. If we scare everybody out of radiology, so nobody wants to be a radiologist because computer vision is completely free, and no AI is going to do a worse job than a radiologist, and we misunderstand the difference between a job and a task, the job of a radiologist, patient care, task, to read a scan.
如果我们对这个问题有如此深刻的误解，并因此吓得所有人都不敢去读放射学专业，那么我们将没有足够的放射科医生，也无法提供足够好的医疗服务。因此，我要说明的是，当你提出一个极端的前提时，把一切都推向极端，我们最终甚至会把人吓跑，而这并不是真实的情况。生活并非如此非黑即白。我们是否希望美国处于领先地位？当然希望。我们是否需要在各个层面扮演领导角色？当然需要。是的，我们当然需要。
▶ 英文原文 ⏱
If we misunderstand that so profoundly, and we scare everybody out of going to radiology school, we're not going to have enough radiologists and good enough healthcare. And so I'm making the case that when you make a premise that is so extreme, everything goes from zero or infinity, we end up scaring people in a way that's just not true. Life is not like that. Do we want the United States to be first? Of course we do. Do we need to be a leader in every layer of that stack? Of course we do. Of course we do.
今天你在谈论神话，因为神话很重要？当然。这太好了。但在几年后，我预测，当我们希望美国的科技被推广到世界各地，如印度、中东、非洲和东南亚时，当我们的国家希望出口，因为我们想要出口我们的技术，想要出口我们的标准。到那时，我希望你和我再次进行同样的对话，我会告诉你今天的谈话内容，以及你的政策和想法是怎样毫无理由地导致美国放弃了世界第二大市场。
▶ 英文原文 ⏱
Is today you're talking about mythos because mythos is important? Sure. That's fantastic. But in a few years time, I'm making you the prediction that when we want the American tech stack, when we want American technology to be diffused around the world, out to India, out to the Middle East, out to Africa, out to Southeast Asia, when our country would like to export, because we would like to export our technology, we would like to export our standards. On that day, I want you and I to have that same conversation again, and I will tell you exactly about today's conversation, about how your policy and what you imagined literally caused the United States to concede the second largest market in the world for no good reason at all.
我们不应该让步。如果我们输了，那就输了。但为什么我们要让步呢？现在没有人在支持全盘皆输，没有人支持把所有东西都运到中国。没有人这么主张。我们应该始终在这里拥有最好的技术和最多、最先进的技术。同时，我们也应该努力在全球范围内竞争并取胜。这两者可以同时实现。
▶ 英文原文 ⏱
We shouldn't concede it. If we lose it, we lose it. But why do we concede it? Now, nobody is advocating. Nobody is advocating an all or nothing. Nobody's advocating all or nothing, meaning we ship everything to China at all times. Nobody's advocating that. We should always have the best technology here. We should always have the most technology here and the first. But we should also try to compete and win around the world. Both of those things can simultaneously happen.
这需要一些细微的差别和一定程度的成熟，而不是绝对化。这个世界并不是非黑即白的。这个论点主要基于他们为自己的架构设计了一些特定的模型。他们在几年内制造了最好的芯片，这些芯片被出口到世界各地，奠定了标准。由于极紫外光刻（EUV）出口管制，正如我们所说，你将不得不转向1.6纳米的技术。而他们即使在几年之后，依然会停留在7纳米技术上。
▶ 英文原文 ⏱
It requires some amount of nuance, some amount of maturity, instead of absolutes. The world is just not absolutes. Okay, the argument hinges on they've built models that are specified for their architect. They're the best chips that they make in a few years, and those chips get exported around the world. That sets the standard. Because of EUV export controls, as we said, you're going to move on to 1.6 nanometer. They're still going to be on 7 nanometer, even after a few years from now.
在国内，他们可能会觉得，我们拥有如此丰富的能源，可以以大规模进行制造，所以继续使用7纳米制程是有道理的。但是在出口方面，他们的7纳米芯片必须能够与1.6纳米芯片竞争。他们的模型必须为7纳米制程进行高度优化，以至于在7纳米上运行模型比在1.6纳米上运行更好。
▶ 英文原文 ⏱
And it may make sense that domestically, they would prefer, hey, we've got so much energy. We can manufacture it at such scale. We'll still keep using 7 nanometer. But the exporting thing, their 7 nanometer chips have to be competitive against your 1.6 nanometer chips. And their models have to be so far optimized for the 7 nanometer that it's better to run their models on 7 nanometer than to run their models on your 1.6 nanometer.
我们能先看看事实吗？好的。Blackwell的光刻技术比Hopper先进50倍吗？有50倍吗？还差得远呢。我一直在说，摩尔定律已死。在Hopper和Blackwell之间，单从晶体管本身来看，大约提升了75%。它们之间相隔三年，提升了75%。但Blackwell要比Hopper好50倍。我想说的是，架构很重要，计算机科学也很重要，半导体物理同样重要。但是，计算机科学很重要。人工智能的影响在很大程度上来自计算架构，这也是CUDA如此有效的原因，也是它备受欢迎的原因。这是一个生态系统，一个计算架构，允许极大的灵活性。如果你想完全改变一个架构，创建像MOE（Mixture of Experts）这样的东西，创建像扩散（diffusion）这样的方法，创建一些离散化的系统，你都可以做到。这非常容易。
▶ 英文原文 ⏱
Can we just look at the facts then? Okay. Is Blackwell 50 times more advanced lithography than Hopper? Is it 50 times? Not even close. I just kept saying it over and over again. Moore's law is dead. Between Hopper and Blackwell, from the transistors themselves, call it 75%. It was three years apart. 75%. Blackwell is 50 times Hopper. My point is architecture matters. Computer science matters. Semiconductor physics matters as well. But computer science matters. AI, the impact of AI largely comes from the computing stack, which is the reason why CUDA is so effective, which is the reason why CUDA is so beloved. It's an ecosystem, a computing architecture that allows for so much flexibility that if you wanted to change an architecture completely, create something like MOE, create something like diffusion, create something that's disaggregated, you could do so. It's easy to do.
事实是，人工智能同样依赖于软件栈的上层和底层架构。如果我们的架构和软件栈能为我们的生态系统进行优化，那显然是好事。因为我们今天讨论了为什么NVIDIA的生态系统如此强大，为什么大家总是喜欢首先使用CUDA编程。他们确实如此。在中国的研究人员也是这样。如果我们被迫离开中国，那将是一个政策失误。显然，这会引发反弹，对美国产生不利影响。这样的政策恰恰加速了中国芯片行业的发展，迫使其AI生态系统专注于自有架构。虽然还不算太晚，但影响已经发生。
▶ 英文原文 ⏱
And so the fact of the matter is, AI is about the stack above as much as it is about the architecture below. To the extent that we have architectures and software stacks that are optimized for our stack, for our ecosystem, it is obviously good. Because we started the conversation today about how NVIDIA's ecosystem is so rich, why people always love programming on CUDA first. They do. They do. And so do the researchers in China. But if we are forced to leave China, if we're forced to leave China, it would be, it would be, well, first of all, it's a policy mistake. Obviously, it has backlash. Obviously, it has fired, you know, has turned out badly for the United States. It enabled, it accelerated our chip industry. It forced all of their AI ecosystem to focus on their internal architectures. It's not too late, but nonetheless, it has already happened.
将来你会看到，他们显然并没有停留在7纳米的技术。它们在制造方面非常出色，并将继续从7纳米技术向更高的发展。那么，5纳米和7纳米之间真的有10倍的差距吗？答案是否定的。架构很重要，网络也很重要。这就是为什么NVIDIA收购了Mellanox，因为网络很重要。能源也很重要。所以这些因素都很重要，你不能用简单的方式来概括。我们可以暂时不讨论中国的话题，但这实际上引出了一个有趣的问题，即我们之前讨论的在台积电、内存等方面的瓶颈问题。
▶ 英文原文 ⏱
You're going to see in the future, they're not stuck at 7 nanometer, obviously. They're good at manufacturing. They will continue to advance from 7 and beyond. Now, is there a 10x difference between 5 nanometer and 7 nanometer? The answer is no. Architecture matters. Networking matters. That's why NVIDIA bought Mellanox. Networking matters. Energy matters. And so all of that stuff matters. It's not simplistic like the way you're trying to distill it. We can move on from China. But that actually raises an interesting question about, we were discussing earlier these bottlenecks at TSMC and memory and so forth.
如果我们生活在这样一个世界里，就是说，你已经是N3制程的大多数了，那么总有一天你会成为N2的大多数。在这种情况下，你是否会考虑回到N7这种较老的工艺节点，利用其剩余产能，并且说，“嘿，AI的需求如此之大，我们在最前沿的扩展能力赶不上需求。因此，我们将结合所有关于Numerix的知识和你所描述的其他改进，来打造一个全新的产品。” 你觉得这种情况会在2030年之前发生吗？其实没有必要这样做。因为每一代的进步不仅仅体现在晶体管的缩小，同样在于工程技术、封装、堆叠、数值计算和系统架构上所做的大量创新。
▶ 英文原文 ⏱
And so if we're in this world where, you know, you're already the majority of N3, at some point you'll be N2, you'll be a majority of that. Do you see that you could go back to N7, the spare capacity at an older process node, and say, hey, the demand for AI is so great and our capacity to expand the leading edge is not meeting it. So we're going to make a hopper or ampere about everything we know about Numerix today and all the other improvements you described. Do you see that world happening before 2030? It's not necessary to. And the reason for that is because with every generation, the architecture is more than just the transistor scale. It also, you're doing so much engineering and packaging and stacking and the numerics and, you know, the system architecture.
当你失去轻松返回另一个节点的能力时，那是没有人能负担得起的研发水平。你知道的，我们可以负担得起向前进。我不认为我们能够负担得起回头。如果假设某一天，做个思想实验，如果那天我们说，我们再也不会有更多容量了。我是否会毫不犹豫地回到使用7？当然会。有人问我，为什么NVIDIA不同时进行多个完全不同架构的芯片项目呢？比如你可以做一个Cerebra风格的晶圆级别设计，或者像Dojo那样的大型封装，甚至做一个无需CUDA支持的。毕竟你们有足够的资源和工程人才以并行执行所有这些项目。那么为什么会选择将所有的鸡蛋放在一个篮子里，不管AI和架构的未来发展方向可能在哪里？
▶ 英文原文 ⏱
When you run out of capacity to easily go back to another node, that's a level of R&D that no one could afford. You know, we could afford to lean forward. I don't think we could afford to go back. Now, if the world simply says, if on that day, if on that day, let's do the thought experiment, on that day we go, listen, we're just never going to have more capacity ever again. Would I go back and use 7 in a heartbeat? You know, of course I would. So one question somebody I was talking to had is, why NVIDIA doesn't run multiple different chip projects at the same time with totally different architectures? So you could do like a Cerebra style wafer scale. You could do a Dojo style huge package. You could do one without CUDA. You know, you have the resources and the engineering talent to do all of these in parallel. So why put all the eggs in one basket, given who knows where AI might go and architectures might go?
哦，我们可以。但问题是我们没有更好的想法。是的，是的，我们可以做所有那些事情，只是没有更好的选择。而且我们在模拟器中模拟了这一切，结果证明这些选择更差。所以我们不会去做那些事情。是的，我们正在开展我们真正想做的项目。如果工作量发生了重大变化，我说的不是算法，而是实际的工作量，这取决于市场的形态，我们可能会决定添加其他加速器。比如，最近我们加入了Grok，我们会将Grok整合到我们的CUDA生态系统中。我们现在这样做是因为代币的价值上涨得很高，以至于代币可以有不同的定价。
▶ 英文原文 ⏱
Oh, we could. It's just that we don't have a better idea. Yeah, yeah. We could do all of those things. It's just not better. And we simulate it all. They're in our simulator provably worse. And so we wouldn't do it. Yeah. We're doing, we're working on exactly the projects that we want to work on. And if the workload were to change dramatically, and I don't mean the algorithms, I actually mean the workload, and that depends on the shape of the market, we may decide to add other accelerators. Like, for example, recently we added Grok, and we're going to fold Grok into our CUDA ecosystem. And we're doing that now because the value of tokens have gone up so high that you could have different pricing of tokens.
在过去的年代，甚至就在几年前，代币要么是免费的，要么就是几乎不贵，对吧？但是现在，你可以拥有不同的客户，而且这些客户希望得到不同的答案。因此，因为客户赚了很多钱，比如说，我们的软件工程师，如果我能提供更具响应能力的代币，使他们比现在更高效，我愿意为此付费。但这个市场直到最近才出现。所以，我认为我们现在有能力根据响应时间将同一模型分为不同的细分市场。这就是为什么我们决定扩展帕累托前沿，创建响应时间更快的推理细分，即便其吞吐量较低。到目前为止，高吞吐量总是更好的。我们认为，可能存在一种情况，即便工厂的吞吐量较低，ASP（平均销售价格）非常高的代币可以弥补这一点。这就是我们这样做的原因。
▶ 英文原文 ⏱
Back in the old days, and just a couple of years ago, tokens are either free or barely expensive, right? And so, but now you can have different customers, and those customers want different answers. And so, because the customers make so much money, like for example, our software engineers, if I can give them much more responsive tokens so that they're even more productive than they are today, I would pay for it. But that market has only recently emerged. And so, I think that we now have the ability to have the same model, based on the response time, have different segments. And that's the reason why we decided to expand the Pareto frontier and create a segment of inference that is faster response time, even though it's lower throughput. Until now, higher throughput is always better. We think that there could be a world where there could be very high ASP tokens, and even though the throughput is lower in the factory, the ASPs make up for it. That's the reason why we did it.
从架构的角度来看，我认为NVIDIA的架构非常出色。如果我有更多的资金，我会更加投资于它的架构。我觉得高端芯片以及推理市场的细分是非常有趣的。好的，最后一个问题。假设深度学习革命没有发生，NVIDIA会在做什么？显然是游戏，但考虑到公司的抱负呢？加速计算。正是我们一直在做的事情。我们公司的理念是，摩尔定律对于很多事情来说是好的，但对于很多计算任务来说并不理想。
▶ 英文原文 ⏱
But otherwise, from an architecture perspective, I think NVIDIA's architectures, I would rather put, if I had more money, I put more behind the architecture. I think this idea of extremely premium tokens and just the disaggregation of the inference market is very interesting. The segmentation of it, yeah. Yeah. All right, final question. Suppose the deep learning revolution didn't happen. What would NVIDIA be doing? Obviously, games, but given the ambition? Accelerated computing. Accelerated computing. The same thing we've been doing all along. The premise of our company is that Moore's Law is going to, more general force computing is good for a lot of things, but for a lot of computation, it's not ideal.
我们将一种叫做GPU的架构与CPU结合起来使用CUDA技术，以加速CPU的工作负载。这样，不同的代码内核或算法就可以被转移到我们的GPU上执行。这使得应用程序的速度提升了100倍、200倍。那么，这样的技术在哪些领域可以使用呢？显然，在工程、科学、物理等领域，还有数据处理、计算机图形学、图像生成等各种领域都可以使用。即使今天没有AI，NVIDIA也会发展得非常好。我认为这背后的原因是非常基本的，那就是通用计算能力的扩展已经基本到头了。
▶ 英文原文 ⏱
And so we combined an architecture called a GPU, CUDA, to a CPU so that we can accelerate the workload of the CPU. And so different kernels of code or algorithms could be offloaded onto our GPU. And as a result, you speed up an application by 100x, 200x. And where can you use that? Well, obviously, engineering and science and physics and so on. So data processing, computer graphics, image generation. I mean, all kinds of things. Even if AI doesn't exist today, NVIDIA will be very, very large. And so I think the reason for that is fairly fundamental, which is the ability for general purpose computing to continue to scale has largely run its course.
这并不是唯一的方法，但实现这一目标的方法是通过特定领域的加速。其中一个我们首先开始的领域是计算机图形学。但其实还有很多很多其他领域。我指的是，各种科学领域如粒子物理、流体力学，还有结构化数据处理等各种类型的算法，都能从CUDA中受益。因此，我们的使命实际上是将加速计算带给全世界，推动那些通用计算无法实现的应用的发展，并提升到一个能够帮助突破某些科学领域的能力水平。
▶ 英文原文 ⏱
And not the only way, but the way to do that is through domain-specific acceleration. And one of the domain that we started with was computer graphics. But there are many, many other domains. I mean, there's all kinds of scientific particle physics and fluids and, you know, and so structured data processing, all kinds of different types of algorithms that benefit from CUDA. And so our mission was really to bring accelerated computing to the world and advance the type of applications that general purpose computing can't do and scale to the level of capability that helps break through certain fields of science.
早期的一些应用领域包括分子动力学、用于能源勘探的地震处理以及图像处理等。这些领域中，通用计算往往效率太低。如果没有人工智能，我会感到非常遗憾。但由于我们在计算领域取得的进步，我们普及了深度学习。现在，任何研究人员、科学家、无论身在何处的学生都可以使用一台电脑或一张GeForce显卡来进行出色的科学研究。
▶ 英文原文 ⏱
And so some of the early applications were molecular dynamics, seismic processing for energy discovery, image processing, of course. And so all of those kinds of fields where general purpose computing is just simply too inefficient to do so. So if there's no AI, I would be very sad. But because of the advances that we made in computing, we democratized deep learning. We made it possible for any researcher, any scientist, anywhere, any student to be able to access a PC or, you know, a GeForce adding card and do amazing science.
这个根本性的承诺一直没有改变，哪怕是一点点都没有。因此，如果你观看GTC（GPU技术大会），你会注意到开头部分没有涉及AI。包括其中的计算光刻、量子化学研究，还有数据处理工作，这些都与AI无关。然而它们仍然非常重要。我知道AI很有趣，也很令人兴奋，但还有很多人在进行非常重要的非AI相关工作。而且，张量（tensors）也不是计算的唯一方法，我们希望帮助所有人。
▶ 英文原文 ⏱
And that fundamental promise hasn't changed, not even a little bit. And so if you see GTC, if you watch GTC, there's the whole beginning part of it, none of it's AI. That whole part of it with computational lithography or our quantum chemistry work or, you know, all of that stuff, data processing work, all of that stuff is unrelated to AI. And it's still very important. I mean, there's, you know, I know that AI is very interesting and quite exciting. But there's a lot of people doing a lot of very important work that's not AI related. And tensors is not the only way that you compute with. And we want to help everybody.
詹森，非常感谢你。
不用谢。我很享受这个过程。
我也一样。太好了。谢谢。
▶ 英文原文 ⏱
Jensen, thank you so much. You're welcome. I enjoyed it. Me too. Sweet. Thank you.