黄仁勋：AI数据中心可膨胀至百万芯片，性能年翻倍，能耗年减2-3倍

　　开始：华尔街见闻

　　黄仁勋示意，莫得物理定律驱逐AI数据中心膨胀到百万芯片，我们咫尺不错将AI软件膨胀到多个数据中心运行。我们仍是为能够在一个前所未有的水平上膨胀计较作念好了准备，而且我们咫尺才刚刚开动。在异日十年，计较性能每年将翻倍或翻三倍，而动力需求每年将减少2-3倍，我称之为超摩尔定律弧线。

　　本周，英伟达CEO黄仁勋继承了《No Priors》节目主握东说念主的采访，就英伟达的十年赌注、x.AI超等集群的快速发展、NVLink时候革命等AI干系话题进行了一场深度对话。

　　黄仁勋示意，莫得任何物理定律不错报复将AI数据中心膨胀到一百万个芯片，尽管这是一个难题，多家大公司包括OpenAI、Anthropic、谷歌、Meta和微软等，都在争夺AI领域的指导地位，竞相攀缘时候的岑岭，但再行创造智能的潜在陈说是如斯之大，以至于弗成不去尝试。

　　摩尔定律曾是半导体行业发展的中枢法例，预测芯片的晶体管数量每两年会翻倍，从而带来性能的握续擢升。然而，跟着物理极限的接近，摩尔定律的速率开动放缓，芯片性能擢升的瓶颈迟缓败露。

　　为了贬责这一问题，英伟达将不同类型的处理器（如GPU、TPU等）连合起来，通过并行处理来龙套传统摩尔定律的驱逐。黄仁勋示意，异日10年，计较性能每年将翻一番或三倍，而动力需求每年将减少2-3倍，我称之为“超摩尔定律弧线”。

　　黄仁勋还提到，我们咫尺不错将AI软件膨胀到多个数据中心：“我们仍是作念好准备，能够将计较膨胀到前所未有的水平，而我们正处于这一领域的起步阶段。”

　　以下是黄仁勋讲话的亮点：

1．我们在异日10年进行了紧要的投资。我们正在投资基础设施，打造下一代AI计较平台。我们在软件、架构、GPU以及通盘完毕AI开发所需的组件上都进行了投资。

2．摩尔定律，即晶体管数量每两年翻倍的预言，也曾是半导体行业的增长指南。然而，跟着物理极限的接近，摩尔定律已不再能够单独推动芯片性能的擢升。为了贬责这一问题，英伟达继承了访佛于“异构计较”的口头，行将不同类型的处理器（如GPU、TPU等）连合起来，通过并行处理来龙套传统摩尔定律的驱逐。英伟达的时候革命，如CUDA架构和深度学习优化，使得AI应用得以在超越摩尔定律的环境中高速运行。

3．我们推出了NVLink作为互连时候，它使得多个GPU能够协同责任，每个GPU处理责任负载的不同部分。通过NVLink，GPU之间的带宽和通讯智商大幅擢升，使得数据中心能够膨胀并赞助AI责任负载。

4．异日的AI应用需要动态和弹性强的基础设施，能够顺应多样范围和类型的AI任务。因此，英伟达死力于于构建不错活泼设立和高效运营的基础设施，空闲从中微型AI样貌到超大范围超等计较集群的需求。

5．构建AI数据中心的要津是要同期优化性能和服从。在AI责任负载中，你需要巨大的电力，而散热成为一个巨大的问题。是以我们花了巨额时刻优化数据中心的遐想和运营，包括冷却系统和电力服从。

6．在硬件快速发展的配景下，保握软件与硬件架构的兼容性显得尤为迫切。黄仁勋提到，我们必须确保我们的软件平台，如CUDA，不错跨代硬件使用。开发者不应当每次我们推出新芯顷刻都被动重写代码。因此，我们确保保握向后兼容，并让软件能够在我们开发的任何新硬件上高效运行。

7．我们正在竖立一个超等集群，叫作念X.AI，它将成为寰宇上最大的AI超等计较平台之一。这个超等集群将提供赞助一些最自利自为的AI样貌所需的计较智商。这是我们推动AI前进的一大步。

8．膨胀AI数据中心的一个大挑战是经管它们破钞的巨大动力。问题不单是是构建更大、更快的系统。我们还必须处理运行这些超大范围系统时靠近的热量和电力挑战。为了应付这一切，需要革命的工程时候来确保基础设施能够应付。

9.AI在芯片遐想中的作用日益迫切，黄仁勋指出，AI仍是在芯片遐想中施展着迫切作用。我们使用机器学习来匡助遐想更高效的芯片，速率更快。这是我们遐想下一代英伟达芯片的一个要津部分，并匡助我们构建专为AI责任负载优化的芯片。

10．英伟达市值的激增是因为我们能够将公司转型为AI公司。我们从一开动是GPU公司，但我们仍是转型成了AI计较公司，这一滑型是我们市值增长的要津部分。AI时候的需求正在飞快增长，我们处在一个能够空闲这一需求的成心位置。

11．具象化AI是指将AI与物理寰宇进行连合。通过这种口头，AI不仅不错在捏造环境中进行任务处理，还能在现实寰宇中进行决策并施行任务。具象化AI将推动智能硬件、自动驾驶等时候的快速发展。

12.AI不单是是用具，它也不错成为‘捏造职工’，匡助擢升责任服从。AI能够在数据处理、编程、决策等领域替代或辅助东说念主类责任，进而编削通盘劳动商场和责任口头。

13.AI将在科学与工程领域产生巨大影响，异常是在药物研发、雀跃研究、物理实验等领域。AI将匡助科学家处理巨额数据，揭示新的科学限定，并加快革命。它还将在工程领域优化遐想，提高服从，推动更具革命性的时候发展。

14．我我方也在日常责任中使用AI用具，来提高服从和创造力。我认为，AI不仅能够匡助我们处理复杂的数据和决策任务，还能擢升我们的创道理维和责任服从，成为每个东说念主责任中不可或缺的一部分。

　　以下是采访翰墨实录全文，由AI翻译：

主握东说念主：Welcome back， Johnson， 30 years in to Nvidia and looking 10 years out， what are the big bets you think are still to make？ Is it all about scale up from here？ Are we running into limitations in terms of how we can squeeze more compute memory out of the architectures we have？ What are you focused on？ Well．

嗨，Johnson，迎接回首！你在英伟达责任了30年，预测异日10年，你认为还有哪些迫切的投资契机？是不是说我们只需要络续扩大范围？我们在现存架构中是否会际遇驱逐，无法再挤出更多的计较内存？你咫尺关注的重心是什么？

黄仁勋：If we take a step back and think about what we‘ve done， we went from coding to machine learning， from writing software tools to creating AIs and all of that running on CPUs that was designed for human coding to now running on GPUs designed for AI coding， basically machine learning． And so the world has changed the way we do computing the whole stack has changed． And as a result， the scale of the problems we could address has changed a lot because we could， if you could paralyze your software on one GPU， you’ve set the foundations to paralyze across a whole cluster or maybe across multiple clusters or multiple data centers． And so I think we‘ve set ourselves up to be able to scale computing at a level and develop software at a level that nobody’s ever imagined before． And so we‘re at the beginning that over the next 10 years， our hope is that we could double or triple performance every year at scale， not at chip， at scale． And to be able to therefore drive the cost down by a factor of 2 or 3， drive the energy down by a factor of 2，3 every single year． When you do that every single year， when you double or triple every year in just a few years， it adds up． So it compounds really aggressively． And so I wouldn’t be surprised if， you know， the way people think about Moore‘s Law， which is 2 x every couple of years， you know， we’re gonna be on some kind of a hyper Moore‘s Law curve． And I fully hope that we continue to do that． Well， what．

以前我们编程都是靠我方写代码，咫尺我们开动让机器我方学习，我方写代码。以前我们用的那种电脑芯片（CPU）是给东说念主写代码用的，咫尺我们用的电脑芯片（GPU）是给机器学惯用的。因为这些变化，我们咫尺处理问题的口头和以前完全不一样了。打个譬如，如果你能让一个机器学习要领在一个GPU上运行，那你就不错让它在通盘电脑群里，以至在许多电脑群或者数据中心里运行。这意味着我们咫尺能处理的问题比以前大多了。是以，我们肯定我方仍是建立了能够大范围膨胀计较智商和开发软件的基础，这个范围是以前没东说念主瞎想过的。

我们但愿在异日10年里，每年都能让计较智商翻两倍或者三倍，不是单个芯片的智商，而是举座的智商。这样的话，我们就能每年把计较老本训斥两倍或三倍，把能耗也训斥两倍或三倍。这种增长如果每年都能完毕，那么几年下来，这个增长会异常惊东说念主。因此，我认为异日的计较将会超越传统的“摩尔定律”（即每两年计较智商翻倍），可能会走上一条更快的增长弧线，我也异常但愿能够络续沿着这个方上前进。

主握东说念主：Do you think is the driver of making that happen even faster than Morse law？ Cuz I know morezo was sort of self reflexive， right？ It was something that he said and then people kind of implemented it to me to happen．

你认为是什么成分推动了计较智商增长速率特殊摩尔定律的？因为我知说念，摩尔定律自己等于一种“自我完毕”的限定，对吧？也等于说，摩尔定律自己是摩尔提倡的，然后大家就按照这个限定去作念，驱逐它就完毕了。

黄仁勋：Yep， too． Fundamental technical pillars． One of them was Denard scaling and the other one was Carver Mead‘s VLSI scaling． And both of those techniques were rigorous techniques， but those techniques have really run out of steam． And， and so now we need a new way of doing scaling． You know， obviously the new way of doing scaling are all kinds of things associated with co design． Unless you can modify or change the algorithm to reflect the architecture of the system or change and then change the system to reflect the architecture of the new software and go back and forth． Unless you can control both sides of it， you have no hope． But if you can control both sides of it， you can do things like

move from FP64 to FP32 to BF16 to FPA to， you know， FP4 to who knows what， right？ And so， and so I think that code design is a very big part of that． The second part of it， we call it full stack． The second part of it is data center scale． You know， unless you could treat the network as a compute fabric and push a lot of the work into the network， push a lot of the work into the fabric． And as a result， you‘re compressing， you know， doing compressing at very large scales． And so that’s the reason why we bought Melanox and started fusing infinite and MV Link in such an aggressive way．

昔时推动时候向上的两个要津时候柱子是Denard缩放（Denard Scaling）和Carver Mead的VLSI缩放。但是这两种步调咫尺都不太管用了，我们需要新的步调来变得更快。

新口头等于“协同遐想”（co-design），也等于软件和硬件必须同期推敲和优化。具体来说，如果你弗成修改或养息算法，使其与系统的架构匹配，或者弗成编削系统架构，以顺应新软件的需求，那么就莫得但愿。但如果你能同期限度软件和硬件，你就能作念许多新的事情，比如：从高精度的FP64转到低精度的FP32，再到BF16、FPA、以至FP4等更低精度的计较。

这等于为什么“协同遐想”这样迫切的原因。另外，另一个迫切的部分是全栈遐想。这意味着，你不仅要推敲硬件，还要推敲数据中心级别的范围。比如，必须把收集行为一个计较平台来使用，把巨额的计较任务推到收集里，诳骗收集和硬件进行大范围压缩运算。

因此，我们收购了Mellanox，并开动异常积极地推动InfiniBand和NVLink这类高速相接时候，来赞助这种全新的大范围计较架构。

And now look where MV Link is gonna go． You know， the compute fabric is going to， I scale out what appears to be one incredible processor called a GPU． Now we get hundreds of GPUs that are gonna be working together.And now look where MV Link is gonna go． You know， the compute fabric is going to， I scale out what appears to be one incredible processor called a GPU． Now we get hundreds of GPUs that are gonna be working together.You know， most of these computing challenges that we‘re dealing with now， one of the most exciting ones， of course， is inference time scaling， has to do with essentially generating tokens at incredibly low latency because you’re self reflecting， as you just mentioned． I mean， you‘re gonna be doing tree surge， you’re gonna be doing chain of thought， you‘re gonna be doing probably some amount of simulation in your head． You’re gonna be reflecting on your own answers． Well， you‘re gonna be prompting yourself and generating text to your in， you know， silently and still respond hopefully in a second． Well， the only way to do that is if your latency is extremely low.Meanwhile， the data center is still about producing high throughput tokens because you know， you still wanna keep cost down， you wanna keep the throughput high， you wanna， right， you know， and generate a return． And so these two fundamental things about a factory， low latency and high throughput， they’re at odds with each other． And so in order for us to create something that is really great in both， we have to go invent something new， and Envy Link is really our way of doing that.We now you have a virtual GPU that has incredible amount of flops because you need it for context． You need a huge amount of memory， working memory， and still have incredible bandwidth for token generation all of the same time．

咫尺看NVLink（英伟达的高速相接时候）将走向那里，异日的计较架构将变得异常刚劲。你不错把它瞎想成一个超等刚劲的处理器，等于GPU（图形处理单位）。而咫尺，英伟达的方针是把数百个GPU集成到一说念，协同责任，酿成一个巨大的计较平台。

咫尺我们靠近的计较挑战中，有一个异常令东说念主快乐的问题等于推理时刻的裁汰。异常是在生成文本时，需要异常低的延伸。因为就像你刚才提到的，我们的想维其实是一种自我反想的进程：你可能在脑海中进行“树形搜索”（tree search）、想考链条（chain of thought），以至可能会进行某种模拟，记忆我方的谜底。你会我方给我方发问，并生成谜底，在大脑里“肃静地”想考，然后但愿能在几秒钟内回话出来。

为了作念到这小数，计较的延伸必须异常低，因为你不可能等太久智力得到驱逐。

但与此同期，数据中心的任务是产生巨额的高混沌量的“token”（标志）。你需要限度老本，保握高混沌量，何况确保能够获获得报。是以，低延伸和高混沌量是两个互相矛盾的方针：低延伸要求快速反应，而高混沌量则需要处理更多的数据。这两者之间存在冲突。

为了同期作念到这两点，必须创造一些全新的时候，而NVLink等于我们贬责这个问题的步调之一。通过NVLink，英伟达但愿能够在确保高混沌量的同期，也能提供低延伸，从而贬责这一计较上的矛盾，擢升举座性能。

咫尺我们有了捏造GPU，它的计较智商异常刚劲，因为我们需要这样强的计较智商来处理高下文。也等于说，当我们在处理一些任务时，需要异常大的内存（异常是责任内存），同期还要有极高的带宽来生成token（即文本或数据标志）。

主握东说念主：Building the models， actually also optimizing things pretty dramatically like David and my team pull data where over the last 18 months or so， the cost of 1 million tokens going into a GPT four equivalent model is basically dropped 240 x． Yeah， and so there‘s just massive optimization and compression happening on that side as．

构建模子的进程其实也包括了许多优化责任，比如David和他的团队，通过昔时18个月的勤勉，得胜地将每百万个token的老本（用于GPT-4类模子的老本）训斥了240倍。

黄仁勋：Well． Just in our layer， just on the layer that we work on． You know， one of the things that we care a lot about， of course， is the ecosystem of our stack and the productivity of our software． You know， people forget that because you have Kuda Foundation and that‘s a solid foundation． Everything above it can change． If everything， if the foundation’s changing underneath you， it‘s hard to build a building on top． It’s hard to create anything and interesting on top． And so could have made it possible for us to iterate so quickly just in the last year． And then we just went back and benchmarked when Lama first came out， we‘ve improved the performance of Hopper by a factor of five without the algorithm， without the layer on top ever changing． Now， well， a factor of five in one year is impossible using traditional computing approaches． But it’s already computing and using this way of code design， we‘re able to explain all kinds of new things．

在我们的责任领域里，有一件异常迫切的事情，等于时候栈的生态系统和软件的出产力。我们异常醉心的是Kuda Foundation这个基础平台，它口角常厚实和坚实的。因为如果基础平台连接变化，想要在上头构建出一个系统或者应用就异常障碍，根底无法在不厚实的基础上创造出真谛的东西。是以，Kuda Foundation的厚实性让我们能够异常快速地进行迭代和革命，尤其是在昔时一年里。

然后，我们还作念了一个对比测试：当Lama初度推出时，我们通过优化Hopper（一种计较平台或架构），在不编削算法和不编削表层架构的情况下，擢升了性能5倍。而且这种5倍的擢升，在传统的计较步调下是险些不可能完毕的。但通过协同遐想这种新的步调，我们能够在现存的基础上连接革命息争释更多新的时候可能性。

主握东说念主：How much are， you know， your biggest customers thinking about the interchangeability of their infrastructure between large scale training and inference？

你的那些最大客户有多保养他们在大范围考验和推理之间基础设施的互换性？

黄仁勋：Well， you know， infrastructure is disaggregated these days． Sam was just telling me that he had decommissioned Volta just recently． They have pascals， they have amperes， all different configurations of blackwall coming． Some of it is optimized for air cool， some of it‘s optimized liquid cool． Your services are gonna have to take advantage of all of this． The advantage that Nvidia has， of course， is that the infrastructure that you built today for training will just be wonderful for inference tomorrow． And most of Chat GBT， I believe， are inferenced on the same type of systems that we’re trained on just recently． And so you can train on， you can inference on it． And so you‘re leaving a trail of infrastructure that you know is going to be incredibly good at inference， and you have complete confidence that you can then take that return on it， on the investment that you’ve had and put it into a new infrastructure to go scale with， you know you‘re gonna leave behind something of use and you know that Nvidia and the rest of the ecosystem are gonna be working on improving the algorithm so that the rest of your infrastructure improves by a factor of five， you know， in just a year． And so that motion will never change．

咫尺的基础设施不像以前那样是一成不变的了。比如Sam刚告诉我，他们最近淘汰了Volta型号的开导。他们有Pascal型号的，有Ampere型号的，还有许多不同设立的Blackwall型号行将到来。有些开导是优化了空气冷却的，有些则是优化了液体冷却的。你们的服务需要能够诳骗通盘这些不同的开导。

英伟达的上风在于，你今天为考验搭建的基础设施，将来会异常适应用于推理。我肯定大多数的Chat GBT（可能是指大型语言模子）都是在最近考验过的交流类型的系统上进行推理的。是以你不错在这个系统上考验，也不错在这个系统上进行推理。这样，你就留住了一条基础设施的轨迹，你知说念这些基础设施将来会异常适应进行推理，你完全有信心不错把之前投资的陈说，进入到新的基础设施中去，扩大范围。你知说念你会留住一些有用的东西，而且你知说念英伟达和通盘生态系统都在勤勉更始算法，这样你的其他基础设施在只是一年内就能提高五倍的服从。是以这种趋势是不会变的。

And so the way that people will think about the infrastructures， yeah， even though I built it for training today， it‘s gotta be great for training． We know it’s gonna be great for inference． Inference is gonna be multi scale． 话语东说念主 2 08：53 I mean， you‘re gonna take， first of all， in order to， the still smaller models could have a larger model that’s still from and so you‘re still gonna create these incredible a frontier models． They’re gonna be used for， of course， the groundbreaking work． You‘re gonna use it for synthetic data generation． You’re gonna use the models， the big models that teach smaller models and distill down to smaller models． And so there‘s a whole bunch of different things you can do， but in the end， you’re gonna have giant models all the way down to little tiny models． The little tiny models are gonna be quite effective， you know， not as generalizable， but quite effective． And so， you know， they‘re gonna perform very specific stunts incredibly well that one task． And we’re gonna see superhuman task in one little tiny domain from a little tiny model． Maybe you know， it‘s not a small language model， but you know， tiny language model， TLMs are， you know， whatever． Yeah， so I think we’re gonna see all kinds of sizes and we hope isn‘t right， just kind of like softwares today．

东说念主们看待基础设施的口头在变，就像我咫尺建的这个设施诚然是为了考验用的，但它也必须很适应考验。我们知说念它将来也会异常恰行为念推理。推搭理有许多不同的范围。

我是说，你会有多样不同大小的模子。小模子不错从大模子那里学习，是以你如故会创造一些前沿的大模子。这些大模子会用来作念始创性的责任，用来生成合成数据，用来教小模子，然后把学问蒸馏给小模子。是以你不错作念的事情有许多，但终末你会有从巨大的模子到异常小的模子。这些小模子将会异常灵验，诚然它们弗成通用，但在特定任务上会异常灵验。它们会在某个特定任务上线路得异常好，我们将会看到在某个小小的领域里，小模子能完成超乎东说念主类的任务。也许它不是一个微型的语言模子，但你知说念，等于微型语言模子，TLMs，归正等于访佛的东西。是以我以为我们会看到多样大小的模子，就像咫尺的软件一样。

Yeah， I think in a lot of ways， artificial intelligence allows us to break new ground in how easy it is to create new applications． But everything about computing has largely remained the same． For example， the cost of maintaining software is extremely expensive． And once you build it， you would like it to run on a large of an install base as possible． You would like not to write the same software twice． I mean， you know， a lot of people still feel the same way． You like to take your engineering and move them forward． And so to the extent that， to the extent that the architecture allows you， on one hand， create software today that runs even better tomorrow with new hardware that‘s great or software that you create tomorrow， AI that you create tomorrow runs on a large install base． You think that’s great． That way of thinking about software is not gonna．

我以为在许多方面，东说念主工智能让我们能够更容易地创造新的应用要领。但是在计较方面，大部分事情如故老口头。比如说，维护软件的老本异常高。一朝你建好了软件，你但愿它能在尽可能多的开导上运行。你不想重迭写相似的软件。我的道理是，许多东说念主如故这样想的。你可爱把你的工程推上前进。是以，如果架构允许你，一方面，今天创建的软件未来在新硬件上能运行得更好，那就太好了；或者你未来创建的软件，后天创建的东说念主工智能能在许多开导上运行。你认为那很棒。这种推敲软件的口头是不会变的。

主握东说念主：Change． And video has moved into larger and larger， let‘s say， like a unit of support for customers． I think about it going from single chip to， you know， server to rack and real 72． How do you think about that progression？ Like what’s next？ Like should Nvidia do you full data center？ But

跟着时候的发展，英伟达的居品仍是不单是是单个的芯片了，而是膨胀到了赞助通盘数据中心的范围。你怎样看待这种发展？接下来会是什么？比如，英伟达是不是应该作念通盘数据中心？

黄仁勋：In fact， we build full data centers the way that we build everything． Unless you‘re building， if you’re developing software， you need the computer in its full manifestation． We don‘t build Powerpoint slides and ship the chips and we build a whole data center． And until we get the whole data center built up， how do you know the software works until you get the whole data center built up， how do you know your， you know， your fabric works and all the things that you expected the efficiencies to be， how do you know it’s gonna really work at scale？ And that‘s the reason why it’s not unusual to see somebody‘s actual performance be dramatically lower than their peak performance， as shown in Powerpoint slides， and it is， computing is just not used to， is not what it used to be． You know， I say that the new unit of computing is the data center． That’s to us． So that‘s what you have to deliver． That’s what we build.Now we build a whole thing like that． And then we， for every single thing that every combination， air cold， x 86， liquid cold， Grace， Ethernet， infinite band， MV link， no NV link， you know what I‘m saying？ We build every single configuration． We have five supercomputers in our company today． Next year， we’re gonna build easily five more． So if you‘re serious about software， you build your own computers if you’re serious about software， then you‘re gonna build your whole computer． And we build it all at scale．

推行上，我们建造圆善的数据中心就像我们建造其他通盘东西一样。如果你在开发软件，你需要电脑的圆善形态来测试。我们不单是作念PPT幻灯片然后发货芯片，我们建造通盘数据中心。只好当我们把通盘数据中心搭建起来后，你智力知说念软件是否平常责任，你的收集布线是否灵验，通盘你渴望的服从是否都能达到，你才知说念它是否真的能在大范围上运行。这等于为什么东说念主们的推行性能频繁远低于PPT幻灯片上展示的峰值性能，计较仍是不再是昔时的口头了。我说咫尺的计较单位是数据中心，对我们来说等于这样。这等于你必须委用的东西，亦然我们建造的东西。

我们咫尺就这样建造通盘系统。然后我们为每一种可能的组合建造：空气冷却、x86架构、液体冷却、Grace芯片、以太网、无尽带宽、MVLink，莫得NVLink，你懂我的道理吗？我们建造每一种设立。我们公司咫尺有五台超等计较机，来岁我们简陋就能再建造五台。是以，如果你对软件是证据的，你就会我方建造计较机，如果你对软件是证据的，你就会建造通盘计较机。我们都是大范围地建造。

This is the part that is really interesting． We build it at scale and we build it very vertically integrate． We optimize it full stack， and then we disagree everything and we sell lemon parts． That‘s the part that is completely， utterly remarkable about what we do． The complexity of that is just insane． And the reason for that is we want to be able to graft our infrastructure into GCP， AWS， Azure， OCI． All of their control planes， security planes are all different and all of the way they think about their cluster sizing all different． And， but yet we make it possible for them to all accommodate Nvidia’s architecture． So that could， it could be everywhere． That‘s really in the end the singular thought， you know， that we would like to have a computing platform that developers could use that’s largely consistent， modular， you know， 10% here and there because people‘s infrastructure are slightly optimized differently and modular 10% here and there， but everything they build will run everywhere． This is kind of the one of the principles of software that should never be given up． And it， and we protected quite dearly． Yeah， it makes it possible for our software engineers to build ones run everywhere． And that’s because we recognize that the investment of software is the most expensive investment， and it‘s easy to test．

这部分真的很真谛。我们不仅大范围建造，而且是垂直整合建造。我们从底层到顶层全程优化，然后我们把各个部分分开，单独卖。我们作念的事情复杂得让东说念主难以置信。为什么这样作念呢？因为我们想把我们的基础设施融入到GCP、AWS、Azure、OCI这些不同的云服务提供商中。我们的限度平台、安全平台都不一样，我们推敲集群大小的口头也各不交流。但是，我们如故想宗旨让他们都能顺应英伟达的架构。这样，我们的架构就能无处不在。

最终，我们但愿有一个计较平台，开发者不错用它来构建软件，这个平台在大部分情况下是一致的，不错模块化地养息，可能这里那里有10%的不同，因为每个东说念主的基础设施都略有优化互异，但是不管在那里， 我们构建的东西都能运行。这是软件的一个原则，我们异常保养这小数。这使得我们的软件工程师不错构建出到处都能运行的软件。这是因为我们透露到，软件的投资是最雅致的投资，而且它很容易测试。

Look at the size of the whole hardware industry and then look at the size of the world‘s industries． It’s $100 trillion on top of this one trillion dollar industry． And that tells you something.The software that you build， you have to， you know， you basically maintain for as long as you shall live． We‘ve never given up on piece of software． The reason why Kuda is used is because， you know， I called everybody． We will maintain this for as long as we shall live． And we’re serious now． We still maintain． I just saw a review the other day， Nvidia Shield， our Android TV． It‘s the best Android TV in the world． We shifted seven years ago． It is still the number one Android TV that people， you know， anybody who enjoys TV． And we just updated the software just this last week and people wrote a new story about it． G Force， we have 300 million gamers around the world． We’ve never stranded a single one of them． And so the fact that our architecture is compatible across all of these different areas makes it possible for us to do it． Otherwise， we would be sub， we would be， we would have， you know， we would have software teams that are hundred times the size of our company is today if not for this architectural compatibility． So we‘re very serious about that， and that translates to benefits the developers．

望望通盘硬件行业的范围，再比比全寰宇通盘行业的范围。硬件行业只好一万亿好意思元，而全寰宇的行业加起来有一百万亿亿好意思元。这个对比告诉你，软件行业要比硬件行业大得多。

你们作念的软件，基本上要一直维护下去。我们从莫得毁灭过任何一款软件。Kuda之是以被大家用，是因为我向通盘东说念主承诺，我们会一直维护它，只消我们还在。我们咫尺如故很证据的，我们还在维护它。我前几天还看到一篇批驳，说我们的英伟达Shield，我们的安卓电视，是寰宇上最佳的安卓电视。我们在七年前推出的，它仍然是排行第一的安卓电视，任何可爱看电视的东说念主都爱它。我们上周才更新了软件，然后东说念主们就写了新的著述来批驳它。我们的G Force，全寰宇有3亿玩家。我们从莫得舍弃过他们中的任何一个。我们的架构在通盘这些不同领域都是兼容的，这使得我们能作念到这小数。如果不是因为我们的架构兼容性，不然我们今天的软件团队的范围会比咫尺公司大一百倍。是以我们异常醉心这小数，这也给开发者带来了克己。

主握东说念主：One impressive substantiation of that recently was how quickly brought up a cluster for X dot AI． Yeah， and if you want to check about that， cuz that was striking in terms of both the scale and the speed with what you did． That

最近有一个让东说念主印象深刻的例子是我们为X dot AI飞快搭建了一个集群。如果你想了解这件事，因为它在范围和速率上都让东说念主讶异。我们很快就完成了这个任务。

黄仁勋：You know， a lot of that credit you gotta give to Elon． I think the， first of all， to decide to do something， select the site． I bring cooling to it． I power hum and then decide to build this hundred thousand GPU super cluster， which is， you know， the largest of its kind in one unit． And then working backwards， you know， we started planning together the date that he was gonna stand everything up． And the date that he was gonna stand everything up was determined， you know， quite， you know， a few months ago． And so all of the components， all the Oems， all the systems， all the software integration we did with their team， all the network simulation we simulate all the network configurations， we， we pre， I mean like we prestaged everything as a digital twin． We， we pres， we prestaged all of his supply chain． We prestaged all of the wiring of the networking． We even set up a small version of it． Kind of a， you know， just a first instance of it． You know， ground truth， if you reference 0， you know， system 0 before everything else showed up． So by the time that everything showed up， everything was staged， all the practicing was done， all the simulations were done．

这里得给埃隆·马斯克许多功劳。领先，他决定要作念这件事，选了地点，贬责了冷却和供电问题，然后决定建造这个十万GPU的超等计较机群，这是迄今为止这种类型中最大的一个。然后，我们开动倒推，等于说，我们几个月前就一说念佛营了他要让一切运行起来的日历。是以，通盘的组件、通盘的原始开导制造商、通盘的系统、通盘的软件集成，我们都是和他们的团队一说念作念的，通盘的收集设立我们都模拟了一遍，我们事先准备，就像数字孪生一样，我们事先准备了通盘的供应链，通盘的收集布线。我们以至搭建了一个小版块，就像是第一个实例，你懂的，等于通盘东西到位之前的基准，你参考的0号系统。是以，当通盘东西都到位的时候，一切都仍是安排好了，通盘的训练都作念完结，通盘的模拟也都完成了。

And then， you know， the massive integration， even then the massive integration was a Monument of， you know， gargantuan teams of humanity crawling over each other， wiring everything up 247． And within a few weeks， the clusters were out． I mean， it‘s， it’s really， yeah， it‘s really a testament to his willpower and how he’s able to think through mechanical things， electrical things and overcome what is apparently， you know， extraordinary obstacles． I mean， what was done there is the first time that a computer of that large scale has ever been done at that speed． Unless our two teams are working from a networking team to compute team to software team to training team to， you know， and the infrastructure team， the people that the electrical engineers today， you know， to the software engineers all working together． Yeah， it‘s really quite a fit to watch． Was．

然后，你知说念，大范围的集成责任，即使这个集成责任自己亦然个巨大的工程，需要巨额的团队成员像蚂蚁一样勤勉责任，险些是全天候不竭地接线和竖立。几周之内，这些计较机群就建成了。这真的是对他意志力的讲明，也娇傲了他如安在机械、电气方面想考，并克服了彰着口角常巨大的空泛。我的道理是，这然则第一次在这样短的时刻内建成如斯大范围的计较机系统。这需要我们的收集团队、计较团队、软件团队、考验团队，以及基础设施团队，也等于那些电气工程师、软件工程师，通盘东说念主一说念合作。这真的挺壮不雅的。这就像是一场大型的团队配合，每个东说念主都在勤勉确保一切班师运行。

主握东说念主：There a challenge that felt most likely to be blocking from an engineering perspective， active， just．

从工程角度来看，有莫得哪个挑战最可能成为绊脚石，等于说，有莫得哪个时候难题最可能让通盘样貌卡住，滚动不得？

黄仁勋：A tonnage of electronics that had to come together． I mean， it probably worth just to measure it． I mean， it‘s a， you know， it tons and tons of equipment． It’s just abnormal． You know， usually a supercomputer system like that， you plan it for a couple of years from the moment that the first systems come on， come delivered to the time that you‘ve probably submitted everything for some serious work． Don’t be surprised if it‘s a year， you know， I mean， I think that happens all the time． It’s not abnormal． Now we couldn‘t afford to do that． So we created， you know， a few years ago， there was an initiative in our company that’s called Data Center as a product． We don‘t sell it as a product， but we have to treat it like it’s a product． Everything about planning for it and then standing it up， optimizing it， tuning it， keep it operational， right？ The goal is that it should be， you know， kind of like opening up your beautiful new iPhone and you open it up and everything just kind of works．

我们需要把巨额的电子开导整合在一说念。我的道理是，这些开导的量多到值得去称一称。稀有吨又数吨的开导，这太顽抗常了。频繁像这样的超等计较机系统，从第一个系统开动委用，到你把通盘东西都准备好进行一些严肃的责任，你频繁需要计划几年时刻。如果这个进程需要一年，你要知说念，这是常有的事，并不奇怪。

但咫尺我们莫得时刻去这样作念。是以几年前，我们公司里有一个叫作念“数据中心即居品”的计划。我们不把它行为居品来卖，但我们必须像对待居品一样对待它。从计划到建立，再到优化、养息、保握运行，通盘的一切都是为了确保它能够像灵通一部新鲜的iPhone一样，一灵通，一切都能平常责任。我们的方针等于这样。

Now， of course， it‘s a miracle of technology making it that， like that， but we now have the skills to do that． And so if you’re interested in a data center and just have to give me a space and some power， some cooling， you know， and we‘ll help you set it up within， call it， 30 days． I mean， it’s pretty extraordinary．

天然了，能这样快就把数据中心建好，这简直等于科技的遗迹。但咫尺我们仍是有了这样的时候智商。是以如果你想要建一个数据中心，只需要给我一个地点，提供一些电力和制冷开导，我们就能在差未几30天内帮你把一切都搭建好。我的道理是，这真的异常了不得。

主握东说念主：That‘s wild． If you think， if you look ahead to 200，000，500，000， a million in a super cluster， whatever you call it． At that point， what do you think is the biggest blocker？ Capital energy supply in one area？

那的确是非。如果你想想，如若将来有个超等大的计较机集群，内部有个二十万、五十万、以至一百万的计较机，不管你叫它什么。到阿谁时候，你以为最大的难题会是什么呢？是资金问题、动力供应问题，如故别的什么？

黄仁勋：Everything． Nothing about what you， just the scales that you talked about， though， nothing is normal．

你说的那些事情，不管是哪个方面，只消波及到你提到的那些巨大范围，那就莫得一件事情是平常的。

主握东说念主：But nothing is impossible． Nothing．

但是，也没什么事是完全不可能的。啥事都有可能。

黄仁勋：Is， yeah， no laws of physics limits， but everything is gonna be hard． And of course， you know， I， is it worth it？ Like you can‘t believe， you know， to get to something that we would recognize as a computer that so easily and so able to do what we ask it to do， what， you know， otherwise general intelligence of some kind and even， you know， even if we could argue about is it really general intelligence， just getting close to it is going to be a miracle． We know that． And so I think the， there are five or six endeavors to try to get there． Right？ I think， of course， OpenAI and anthropic and X and， you know， of course， Google and meta and Microsoft and you know， there， this frontier， the next couple of clicks that mountain are just so vital． Who doesn’t wanna be the first on that mountain． I think that the prize for reinventing intelligence altogether． Right． It‘s just， it’s too consequential not to attempt it． And so I think there are no laws of physics． Everything is gonna be hard．

如实，莫得物理定律说我们作念不到，但每件事情都会异常难。你也知说念，这值得吗？你可能以为难以置信，我们要达到的那种电脑，能够减弱地作念我们让它作念的事情，也等于某种通用智能，哪怕我们能争论它是否真的是通用智能，接近它都将会是一个遗迹。我们知说念这很难。是以我认为，有五六个团队正在尝试达到这个方针。对吧？比如说，OpenAI、Anthropic、X，还有谷歌、Meta和微软等等，他们都在勤勉攀缘这个前沿科技的山岭。谁不想成为第一个登顶的东说念主呢？我认为，再行发贤慧能的奖励是如斯之大，它的影响太大了，我们弗成不去尝试。是以，诚然物理定律上莫得驱逐，但每件事都会很难。

主握东说念主：A year ago when we spoke together， you talked about， we asked like what applications you got most excited about that Nvidia would serve next in AI and otherwise， and you talked about how you led to， your most extreme customers sort of lead you there． Yeah， and about some of the scientific applications． So I think that‘s become like much more mainstream of you over the last year． Is it still like science and AI’s application of science that most excites you？

一年前我们聊天时，我问你，你对英伟达接下来在AI和其他领域能服务的哪些应用最快乐，你谈到了你的一些最顶点的客户某种进度上指导了你。是的，还推敲于一些科学应用的磋磨。是以我以为昔时一年里，这些科学和AI的应用变得更主流了。咫尺，是不是仍然是科学以及AI在科学领域的应用让你最快乐？

黄仁勋：I love the fact that we have digital， we have AI chip designers here in video． Yeah， I love that． We have AI software engineers． How．

我就直说了，我们咫尺稀有字版的，也等于用东说念主工智能来遐想芯片的遐想师，就在视频里。是的，我可爱这个。我们还有AI软件工程师。

主握东说念主：Effective our AI chip designers today？ Super．

我们今天用东说念主工智能来遐想芯片的服从怎样样？异常好。

黄仁勋：Good． We can‘t， we couldn’t build Hopper without it． And the reason for that is because they could explore a much larger space than we can and because they have infinite time． They‘re running on a supercomputer． We have so little time using human engineers that we don’t explore as much of the space as we should， and we also can explore commentary． I can‘t explore my space while including your exploration and your exploration． And so， you know， our chips are so large， it’s not like it‘s designed as one chip． It’s designed almost like 1，000 ships and we have to ex， we have to optimize each one of them． Kind of an isolation． You really wanna optimize a lot of them together and， you know， cross module code design and optimize across much larger space． But obviously we‘re gonna be able to find fine， you know， local maximums that are hidden behind local minimum somewhere． And so clearly we can find better answers． You can’t do that without AI． Engineers just simply can‘t do it． We just don’t have enough time．

我们的AI芯片遐想师真的很是非。如果莫得它们，我们根底造不出Hopper这款芯片。因为它们能探索的范围比我们东说念主类广得多，而且它们好像有取之不尽用之不竭的时刻。它们在超等计较机上运行，而我们东说念主类工程师的时刻有限，探索不了那么大的范围。而且，我们也弗成同期探索通盘的可能，我探索我的领域的时候，就弗成同期探索你的领域。

我们的芯片异常大，不像是遐想一个单独的芯片，更像是遐想1000个芯片，每个都需要优化。就像是一个个落寞的小岛。但我们其实很想把它们放在一说念优化，跨模块协同遐想，在通盘更大的空间里优化。彰着，我们能找到更好的贬责决策，那些荫藏在某个边缘里的最佳的选定。莫得AI我们作念不到这小数。工程师们等于时刻不够，作念不到。

主握东说念主：One other thing has changed since we last spoke collectively， and I looked it up at the time in videos， market cap was about 500 billion． It‘s now over 3 trillion． So the last 18 months， you’ve added two and a half trillion plus of market cap， which effectively is $100 billion plus a month or two and a half snowflakes or， you know， a stripe plus a little bit， or however you wanna think about.A country or two． Obviously， a lot of things are stayed consistent in terms of focus on what you‘re building and etc． And you know， walking through here earlier today， I felt the buzz like when I was at Google 15 years ago was kind of you felt the energy of the company and the vibe of excitement． What has changed during that period， if anything？ Or how， what is different in terms of either how Nvidia functions or how you think about the world or the size of bets you can take or．

自我们前次一说念聊天以来，有一件事变了，我查了下，那时英伟达的市值大约是5000亿好意思元。咫尺特殊了3万亿好意思元。是以在昔时18个月里，你们增多了两万五千亿好意思元以上的市值，这相配于每个月增多了1000亿好意思元，或者说增多了两个半的Snowflake公司或者一个Stripe公司多小数的市值，不管你怎样想。

这相配于增多了一两个国度的市值。彰着，尽管市值增长了这样多，你们在建造的东西和专注的领域上如故保握了一致性。你知说念，今天我在这里走了一圈，我感受到了一种活力，就像15年前我在谷歌时感受到的那样，你能嗅觉到公司的能量和快乐的氛围。在这段时刻里，有什么变化了吗？或者，英伟达的运作口头、你对寰宇的看法、你能承担的风险大小等方面有什么不同了吗？

黄仁勋：Well， our company can‘t change as fast as a stock price． Let’s just be clear about． So in a lot of ways， we haven‘t changed that much． I think the thing to do is to take a step back and ask ourselves， what are we doing？ I think that’s really the big， you know， the big observation， realization， awakening for companies and countries is what‘s actually happening． I think what we’re talking about earlier， I‘m from our industry perspective， we reinvented computing． Now it hasn’t been reinvented for 60 years． That‘s how big of a deal it is that we’ve driven down the marginal cost of computing， down probably by a million x in the last 10 years to the point that we just， hey， let‘s just let the computer go exhaustively write the software． That’s the big realization． 话语东说念主 2 24：00 And that in a lot of ways， I was kind of， we were kind of saying the same thing about chip design． We would love for the computer to go discover something about our chips that we otherwise could have done ourselves， explore our chips and optimize it in a way that we couldn‘t do ourselves， right， in the way that we would love for digital biology or， you know， any other field of science．

我们公司的变化速率可莫得股价变化那么快。是以这样说吧，我们在许多方面并莫得太大变化。我认为迫切的是要退一步来问问我们我方，我们到底在作念什么。这真的是对公司和国度来说一个很大的不雅察、透露和醒觉，那等于着实发生的事情。

就像我们之前磋磨的，从我们行业的角度来看，我们再行发明了计较。这然则60年来都莫得发生过的事情。我们把计较的边际老本训斥了，可能在昔时10年里训斥了一百万分之一，以至于我们咫尺不错让计较机去细心地编写软件。这是一个紧要的融会。

在许多方面，我们对芯片遐想亦然这样说的。我们但愿计较机能我方去发现我们芯片的一些东西，这些东西我们原来不错我方作念，但计较机不错探索我们的芯片并以我们我方作念不到的口头进行优化，就像我们但愿在数字生物学或其他科学领域那样。

And so I think people are starting to realize when we reinvented computing， but what does that mean even， and as we， all of a sudden， we created this thing called intelligence and what happened to computing？ Well， we went from data centers are multi tenant stores of files． These new data centers we‘re creating are not data centers． They don’t， they‘re not multi tenant． They tend to be single tenant． They’re not storing any of our files． They‘re just， they’re producing something． They‘re producing tokens． And these tokens are reconstituted into what appears to be intelligence． Isn’t that right？ And intelligence of all different kinds． You know， it could be articulation of robotic motion． It could be sequences of amino acids． It could be， you know， chemical chains． It could be all kinds of interesting things， right？ So what are we really doing？ We‘ve created a new instrument， a new machinery that in a lot of ways is that the noun of the adjective generative AI． You know， instead of generative AI， you know， it’s， it‘s an AI factory． It’s a factory that generates AI． And we‘re doing that at extremely large scale． And what people are starting to realize is， you know， maybe this is a new industry． It generates tokens， it generates numbers， but these numbers constitute in a way that is fairly valuable and what industry would benefit from it．

是以我以为东说念主们开动意志到，当我们再行发明计较时，这到底意味着什么。倏得间，我们创造了这个叫作念智能的东西，计较发生了什么变化？嗯，我们以前把数据中心看作是多佃农存储文献的地点。我们咫尺创建的这些新数据中心，其实仍是不是传统真谛上的数据中心了。它们经常是单一佃农的，它们不存储我们的文献，它们只是在出产一些东西。它们在出产数据令牌。然后这些数据令牌再行组合成看起来像智能的东西。对吧？而且智能有多样种种的体式。可能是机器东说念主动作的抒发，可能是氨基酸序列，可能是化学物资链，可能是多样真谛的事情，对吧？是以我们到底在作念什么？我们创造了一种新的用具，一种新的机械，从许多方面来说，它等于生成性东说念主工智能的名词体式。你知说念，不是生成性东说念主工智能，而是东说念主工智能工场。它是一个出产东说念主工智能的工场。我们正在异常大范围地作念这件事。东说念主们开动意志到，这可能是一个新行业。它生成数据令牌，它生成数字，但这些数字以一种相配有价值的口头组成，哪些行业会从中受益。

Then you take a step back and you ask yourself again， you know， what‘s going on？ Nvidia on the one hand， we reinvent a computing as we know it． And so there’s $1 trillion of infrastructure that needs to be modernized． That‘s just one layer of it． The big layer of it is that there’s， this instrument that we‘re building is not just for data centers， which we were modernizing， but you’re using it for producing some new commodity． And how big can this new commodity industry be？ Hard to say， but it‘s probably worth trillions． 话语东说念主 2 26：18 And so that I think is kind of the viewers to take a step back． You know， we don’t build computers anymore． We build factories． And every country is gonna need it， every company‘s gonna need it， you know， give me an example of a company who or industry as us， you know what， we don’t need to produce intelligence． We got plenty of it． And so that‘s the big idea． I think， you know， and that’s kind of an abstracted industrial view． And， you know， someday people realize that in a lot of ways， the semiconductor industry wasn‘t about building chips， it was building， it was about building the foundational fabric for society． And then all of a sudden， there we go． I get it． You know， this is a big deal． Isn’t not just about chips．

然后你退一步，再次问我方，到底发生了什么？Nvidia一方面，我们再行发明了我们所知说念的计较。是以有一万亿好意思元的基础设施需要当代化。这只是其中一层。更大的一层是，我们正在建造的这个用具不单是是为了数据中心，我们正在当代化数据中心，而是你用它来出产一些新的商品。这个新商品行业能有多大？很难说，但可能价值数万亿好意思元。

是以我认为这是不雅众需要退一步的地点。你知说念，我们不再制造电脑了。我们制造工场。每个国度都会需要它，每个公司都会需要它，给我一个不需要出产智能的公司或行业的例子，你知说念，我们有许多智能。是以这等于这个大主意。我认为，你知说念，这是一种概括的工业不雅点。然后，有一天东说念主们意志到，在许多方面，半导体行业不是对于制造芯片，它是对于为社会建立基础结构。然后倏得间，我们显然了。这不单是是对于芯片的大事。

主握东说念主：How do you think about embodiment now？

你咫尺怎样看待“体现”或者“具体化”这个见识？等于说，你怎样推敲把智能或者东说念主工智能着实应用到推行的物理寰宇中，比如机器东说念主或者其他实体开导上？

黄仁勋：Well， the thing I‘m super excited about is in a lot of ways， we’ve， we‘re close to artificial general intelligence， but we’re also close to artificial general robotics． Tokens are tokens． I mean， the question is， can you tokenize it？ You know， of course， tokenis， tokenizing things is not easy， as you guys know． But if you‘re able to tokenize things， align it with large language models and other modalities， if I can generate a video that has Jensen reaching out to pick up the coffee cup， why can’t I prompt a robot to generate the token， still pick up the rule， you know？ And so intuitively， you would think that the problem statement is rather similar for computer． And， and so I think that we‘re that close． That’s incredibly exciting．

我咫尺异常快乐的小数是，我们在许多方面都将近完毕通用东说念主工智能了，而且我们也快完毕通用机器东说念主时候了。数据令牌等于数据令牌。我的道理是，问题是，你能把它变成数据令牌吗？天然，把东西变成数据令牌并逼迫易，你们知说念这小数。但如果你能作念到这小数，把它和大型语言模子和其他口头对皆，如果我能生成一个视频，视频里有Jensen伸手去拿咖啡杯，为什么我弗成教导一个机器东说念主去生成数据令牌，推行上去提起阿谁端正，你知说念吗？是以直不雅上，你会认为这个问题对计较机来说相配相似。是以我认为我们仍是很接近了。这异常令东说念主快乐。

Now the， the two brown field robotic systems． Brown field means that you don‘t have to change the environment for is self driving cars． And with digital chauffeurs and body robots right between the cars and the human robot， we could literally bring robotics to the world without changing the world because we built a world for those two things． Probably not a coincidence that Elon spoke is then those two forms． So robotics because it is likely to have the larger potential scale． And and so I think that’s exciting． But the digital version of it， I is equally exciting． You know， we‘re talking about digital or AI employees． There’s no question we‘re gonna have AI employees of all kinds， and our outlook will be some biologics and some artificial intelligence， and we will prompt them in the same way． Isn’t that right？ Mostly I prompt my employees， right？ You know， provide them context， ask him to perform a mission． They go and recruit other team members， they come back and work going back and forth． How‘s that gonna be any different with digital and AI employees of all kinds？ So we’re gonna have AI marketing people， AI chip designers， AI supply chain people， AIs， you know， and I‘m hoping that Nvidia is someday biologically bigger， but also from an artificial intelligence perspective， much bigger． That’s our future company． If．

咫尺有两种“棕色地带”机器东说念主系统。“棕色地带”意味着你不需要编削环境，比如自动驾驶汽车。有了数字司机和机器东说念主助手在汽车和东说念主类机器东说念主之间，我们不错在不编削寰宇的情况下把机器东说念主时候带到寰宇上，因为我们为这两样东西建造了寰宇。埃隆·马斯克可能不是巧合提到这两种体式的。是以机器东说念主时候因为可能有更大的潜在范围而令东说念主快乐。而数字版的机器东说念主也相似令东说念主快乐。你知说念，我们讨论的是数字或AI职工。毫无疑问，我们将领有多样AI职工，我们的出息将是一些生物和一些东说念主工智能，我们将以交流的口头教导他们。不是吗？大多数情况下，我教导我的职工，对吧？给他们提供高下文，让他们施行任务。他们去招募其他团队成员，他们回首责任，走动责任。这和多样数字和AI职工有什么不同呢？是以我们将有AI营销东说念主员，AI芯片遐想师，AI供应链东说念主员，AI，等等，我但愿英伟达有一天在生物学上更大，同期从东说念主工智能的角度来看，也更大。这是我们异日公司的口头。

主握东说念主：We came back and talked to you year from now， what part of the company do you think would be most artificially intelligent？

如果我们一年后回首再和你聊聊，你以为公司里哪个部分会是最智能化的？

黄仁勋：I‘m hoping it should sign．

我但愿公司里最迫切的、最中枢的部分能完毕智能化。

主握东说念主：Okay． And most．

好的，然后络续研究。

黄仁勋：Important part． And the read． That‘s right． Because it because I should start where it moves the needle most also where we can make the biggest impact most． You know， it’s such an insanely hard problem． I work with Sasina at synopsis and rude at cadence． I totally imagine them having synopsis chip designers that I can rent． And they know something about a particular module， their tool， and they train an AI to be incredibly good at it． And we‘ll just hire a whole bunch of them whenever we need， we’re in that phase of that chip design． You know， I might rent a million synopsis engineers to come and help me out and then go rent a million Cadence engineers to help me out． And that， what an exciting future for them that they have all these agents that sit on top of their tools platform， that use the tools platform and other， and collaborate with other platforms． And you‘ll do that for， you know， Christian will do that at SAP and Bill will do that as service．

我认为最迫切的部分应该是公司里最能产生影响的地点。他说，这个问题异常难，但他但愿从最能推动公司发展的地点开动智能化。他和Synopsys的Sasina和Cadence的Rude一说念责任，他瞎想着不错租用Synopsys的芯片遐想师AI。这些AI对某个特定模块、用具异常了解，何况仍是被考验得异常擅长这方面的责任。当他们需要进行芯片遐想的某个阶段时，他们会租用一大都这样的AI遐想师。比如，他可能会租用一百万个Synopsys工程师AI来襄理，然后再租用一百万个Cadence工程师AI来襄理。我认为，对于我们来说，有一个清翠东说念主心的异日，因为我们有通盘这些AI代理，它们位于我们用具平台的顶部，使用这些用具平台，何况与其他平台配合。SAP的Christian会这样作念，Bill会作为服务来作念这件事。

Now， you know， people say that these Saas platforms are gonna be disrupted． I actually think the opposite， that they‘re sitting on a gold mine， that they’re gonna be this flourishing of agents that are gonna be specialized in Salesforce， specialized in， you know， well， Salesforce， I think they call Lightning and SAP is about， and everybody‘s got their own language． Is that right？ And we got Kuda and we’ve got open USD for Omniverse． And who‘s gonna create an AI agent？ That’s awesome． At open USD， we‘re， you know， because nobody cares about it more than we do， right？ And so I think in a lot of ways， these platforms are gonna be flourishing with agents and we’re gonna introduce them to each other and they‘re gonna collaborate and solve problems．

咫尺，有些东说念主说这些基于收集的软件服务平台（SaaS）将会被颠覆。但我推行上认为恰巧相背，他们就像坐在金矿上一样，将会有一个专科化的智能代理（AI）的茁壮时间。这些智能代理将会特意针对Salesforce、SAP等平台进行优化。比如Salesforce有个叫作念Lightning的平台，每个平台都有我方的语言和特色。我们有Kuda，还有为Omniverse准备的开放USD。谁会来创造这些AI代理呢？那将会口角常酷的事情。在开放USD方面，我们会来作念，因为莫得东说念主比我们更保养它，对吧？是以我认为在许多方面，这些平台将会因为这些智能代理而茁壮起来，我们会把它们互相先容，它们将会配团结贬训斥题。

主握东说念主：You see a wealth of different people working in every domain in AI． What do you think is under notice or that people that you want more entrepreneurs or engineers or business people could work on？

你以为在东说念主工智能领域，有莫得什么被惨酷的地点，或者你但愿更多的创业者、工程师或贸易东说念主士能关注和进入责任的领域？

黄仁勋：Well， first of all， I think what is misunderstood， and I misunderstood， maybe it may be underestimated， is the， the under the water activity， under the surface activity of groundbreaking science， computer science to science and engineering that is being affected by AI and machinery． I think you just can‘t walk into a science department anywhere， theoretical math department anywhere， where AI and machine learning and the type of work that we’re talking about today is gonna transform tomorrow． If they are， if you take all of the engineers in the world， all of the scientists in the world and you say that the way they‘re working today is early indication of the future， because obviously it is． Then you’re gonna see a tidal wave of gender to AI， a tidal wave of AI， a tidal wave machine learning change everything that we do in some short period of time．

领先，我认为可能被诬告或低估了的是，那些在水面下的、正在进行的、龙套性的科学、计较机科学以及科学与工程行为，这些行为正受到东说念主工智能和机械的影响。如果你走进任何一个科学系，任何一个表面数学系，你会发现今天的东说念主工智能和机器学习的责任将编削未来。如果你把寰宇上通盘的工程师、通盘的科学家都看作是异日的早期迹象，因为彰着他们是，那么你就会看到一股涌向东说念主工智能的潮水，一推进说念主工智能的潮水，一股机器学习编削我们所作念的一切的潮水，这将在很短的时刻内发生。

in some short period of time.ion． And to work with Alex and Elian and Hinton at at at in Toronto and Yan Lekun and of course， Andrew Ang here in Stanford． And， you know， I saw the early indications of it and we were fortunate to have extrapolated from what was observed to be detecting cats into a profound change in computer science and computing altogether． And that extrapolation was fortunate for us． And now， of course， we， we were so excited by， so inspired by it that we changed everything about how we did things． But that took how long？ It took literally six years from observing that toy， Alex Net， which I think by today‘s standards will be considered a toy to superhuman levels of capabilities in object recognition． Well， that was only a few years． 话语东说念主 2 33：40 Now what is happening right now， the groundswell in all of the fields of science， not one field of science left behind． I mean， just to be very clear． Okay， everything from quantum computing， the quantum chemistry， you know， every field of science is involved in the approaches that we’re talking about． If we give ourselves， and they‘ve been added for a couple to three years， if we give ourselves in a couple， two， three years， the world’s gonna change． There‘s not gonna be one paper， there’s not gonna be one breakthrough in science， one breakthrough in engineering， where generative AI isn‘t at the foundation of it． I’m fairly certain of it． And， and so I， I think， you know， there‘s a lot of questions about， you know， every so often I hear about whether this is a fad computer． You just gotta go back to first principles and observe what is actually happening．

就在很短的时刻内，我们看到了科学领域的大波涛，莫得一个科学领域被落下。我的道理是，每一件事都异常明晰。从量子计较到量子化学，你知说念的，每个科学领域都波及到我们正在磋磨的步调。如果我们给我方，比如说，两三年的时刻，寰宇将会编削。不会有一篇科学论文，不会有一项科学龙套，一项工程龙套，不是以生成性东说念主工智能为基础的。我对此相配笃定。是以，我认为，你知说念，有许多问题，经常时我听到对于这是否是计较机的一时风尚。你只需要回到基本原则，不雅察推行发生的事情。

东说念主工智能和机器学习的发展异常快，而且影响长远。我在东说念主工智能领域有紧要孝顺的科学家合作的履历，比如多伦多的Alex Krizhevsky、Eliasmith、Hinton和斯坦福的Yan LeCun以及Andrew Ng。、从识别猫咪的温和任务到物体识别智商的超东说念主水平的发展，这个进程只用了几年时刻。我肯定，在异日几年内，每个科学领域的每项科学和工程龙套都将以生成性东说念主工智能为基础。饱读吹东说念主们不要怀疑这是否只是一时的流行，而应该不雅察推行发生的事情，基于事实来判断。

The computing stack， the way we do computing has changed if the way you write software has changed， I mean， that is pretty cool． Software is how humans encode knowledge． This is how we encode our， you know， our algorithms． We encode it in a very different way． Now that‘s gonna affect everything， nothing else， whatever， be the same． And so I， I think the， the， I think I’m talking to the converted here and we all see the same thing． And all the startups that， you know， you guys work with and the scientists I work with and the engineers I work with， nothing will be left behind． I mean， this， we‘re gonna take everybody with us again．

计较的通盘体系，也等于我们进行计较的口头，仍是编削了，连我们编写软件的口头也编削了。这意味着我们编码学问的步调也变了，这是一种全新的编码口头。这将会编削一切，其他的事情都不会和以前一样了。他认为他在这里是对仍是招供这小数的东说念主话语，大家都看到了相似的趋势。不管是他们合作的初创公司，如故他合作的科学家和工程师，通盘东说念主都将被这一变革所影响。他的道理是，此次变革将会带领通盘东说念主一说念前进。

主握东说念主：I think one of the most exciting things coming from like the computer science world and looking at all these other fields of science is like I can go to a robotics conference now． Yeah， material science conference． Oh yeah， biotech conference． And like， I‘m like， oh， I understand this， you know， not at every level of the science， but in the driving of discovery， it is all the algorithms that are．

计较机科学领域的一个最令东说念主快乐的事情是，咫尺不错应用于通盘其他科学领域。比如，他不错去机器东说念主会议、材料科学会议、生物时候会议，他会发现我方能意会那些内容。诚然不是在每个科学领域的每个层面上都懂，但在推动发现方面，都是算法在起作用。

黄仁勋：General and there‘s some universal unifying concepts．

对，有一些广泛长入的见识。

主握东说念主：And I think that‘s like incredibly exciting when you see how effective it is in every domain．

我认为这异常令东说念主快乐，当你看到算法在每个领域都如斯灵验时。

黄仁勋：Yep， absolutely． And eh， I‘m so excited that I’m using it myself every day． You know， I don‘t know about you guys， but it’s my tutor now． I mean， I， I， I don‘t do， I don’t learn anything without first going to an AI． You know？ Why？ Learn the hard way． Just go directly to an AI． I should go directly to ChatGPT． Or， you know， sometimes I do perplexity just depending on just the formulation of my questions． And I just start learning from there． And then you can always fork off and go deeper if you like． But holy cow， it‘s just incredible．

我十足同意。我很快乐，因为我我方每天都在使用AI。不知你们怎样样，但AI仍是成为我的导师。我咫尺学任何东西都会先去问AI。为什么？何苦要良友去学呢，平直去找AI就行了。比如他会平直去问ChatGPT，或者凭据问题的不同，有时他会去问Perplexity。他会从那里开动学习，然后如果甘心，不错深入研究。天哪，这的确太不可想议了。

And almost everything I know， I check， I double check， even though I know it to be a fact， you know， what I consider to be ground truth． I‘m the expert． I’ll still go to AI and check， make double check． Yeah， so great． Almost everything I do， I involve it．

我咫尺险些作念任何事情都会用到AI。哪怕是他知说念的事实，就算是他是阿谁领域的众人，他也会用AI再查验一遍。他以为这样很好，因为他险些通盘的事情都会让AI参与。

主握东说念主：I think it‘s a great note to stop on． Yeah， thanks so much that time today．

这是个很好的扫尾话题。感谢大家今天的参与，时刻到了。

黄仁勋：Really enjoyed it． Nice to see you guys．

我今天很得意见到大家。

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