跟读练习: Jensen Huang & Satya Nadella on unmetered intelligence | Microsoft Build 2026 - 通过视频学习英语口语

Thank you so much for being at Build again.

I know it's late for you in Taipei.

I really appreciate you staying up.

You know, at the end, I've been looking at social and you know, and everything people have been talking about since your keynote over the weekend.

And suddenly, you know, this concept of unmetered intelligence right at the edge is so hot again.

So maybe you want to talk a little bit.

You've thought about this, talked about this.

And now of course with RTX Spark really delivered, I think what's a breakthrough system for AI to be much more ubiquitous.

But maybe Jensen, you can just share a little bit your vision around where you see this going.

Well, this all started about three years ago between a conversation between you and I, and we were talking about how we could build a new class of PCs.

That's incredible for designers and creators, and it would be incredible for artificial intelligence.

And it would be one of these systems that has the processing capability, but also the software stack that's integrated into the world's design packages and creator packages and of course, all the things that we're doing with AI.

And here we are three years later, we built an incredible new chip.

And this system is supported by all of this new software that you created for Windows.

And we now have the ability to have essentially an autonomous agent running on the PC.

Now, when you take a step back and you think about what does that mean?

For the 40 years, that or some 30 years we've been working together, we went from inventing DirectX together to creating now this incredible computer that has autonomous systems running.

The PC evolved from being an incredible tool to now being a tool that's used autonomously by an AI assistant.

And so the idea that I could be traveling and I'm on the phone and I could text my PC and ask my PC to get some coding done or some idea that I have, and it would fire up the tools on the PC, and it would make the modifications or the changes or the design that I was, I told it to to do, and it would iterate with me while I'm away from the PC.

My PC became an assistant.

While I'm sitting there, of course, this PC would be my great assistant as well.

And so this idea that that the PC evolved from a personal computer to a personal AI, it's just really exciting.

And to see it come to life, Satya, to see it come to life and actually doing that, you know, so I'm super excited about it.

Spark, as you mentioned earlier, has all this incredible capabilities, a petaflop of AI performance.

It has a petaflop of NVFP4, this numerical format that our two companies worked on together.

That allows us to take advantage of this 128GB of memory and fit maybe a couple of hundred billion parameter model.

A couple of hundred billion parameter model is state of the art.

And so I think the days of having a really smart assistant running autonomously on the PC is here.

Yeah, I know it's so awesome.

And in fact, I'm also excited about Windows coming to the GB300.

And so that's another thing that it's kind of like data center right on your desktop.

And it's so exciting.

But talking about that data center side, obviously, you know, this entire thing got started when we built the first supercomputer together to train the GPT models.

And we have come a long way.

In fact, even I was talking about the Fairwater design.

It was custom built essentially for the Grace Blackwell era to be able to max the the data center design with the system design you had.

And now, of course, we're validating Vera Rubin.

We're very excited about it.

Maybe you want to share a little bit about sort of what happens, even on the cloud side, with how you're pushing on the systems innovation.

Well, our journey is incredible.

We built the first AI supercomputer together that was based on Ampere.

Of course, Hopper was an incredible success.

The first two generations were focused on pre-training.

Grace Blackwell came along and all of the focus moved to post training, reinforcement learning, which allowed us to have reasoning models.

And these reasoning models, based on mixture of experts, were incredibly intelligent, energy efficient, but it requires giant systems.

And so we created NVLink72 and the entire rack became one computer.

We had evolved from one node to now one rack.

Well, Microsoft deployed the largest number of Grace Blackwells in the world today, the fastest and the largest number of Grace Blackwells in the world.

Fairwater is just a magnificent system to look at.

It's just a miracle of engineering.

It's just an incredible feat.

It's completely liquid cooled.

You mentioned something earlier that I'm very proud of as well, that it's closed looped, basically uses almost no water, and it's incredibly environmentally friendly. It's energy efficient.

We're able to increase the token generation rate and reduce the cost of token generation by an order of magnitude, some 30 times over Hopper.

So that was a huge achievement.

Well, Vera Rubin was created for a world where these AIs are now agentic.

And so where as Hopper was created for pretraining, Grace Blackwell for training, post training and also inference.

Vera Rubin is designed to run agents.

It's agents.

As you know, this computing pattern is exactly the same computing pattern we're going to run on the RTX Spark.

It's exactly the same agentic system, except of course, it's going to be much, much larger when process enormous number of them simultaneously.

Many of them are going to be from different customers and different different partners.

And so the entire path, the entire coding path from storage, which is the long term memory, the working memory is encrypted, the data is encrypted in transit.

The data is also encrypted in use.

And so we've we're going to really innovate in the area of confidential computing.

And so this entire disaggregated distributed computing system you mentioned CPUs.

Vera is a revolutionary CPU designed for agents.

You know, the past CPUs were designed for humans.

And, you know, we're just more patient than than agents are.

And agents want low latency, just as if you've been working on as well.

And Vera is designed for extremely low latency.

And so Vera Rubin is just completely revolutionary.

I can't wait to show it to everybody.

You've already stood it up.

Yep. Our two teams, our two teams have been working very closely.

And, you know, almost long before the chips taped out, long before the systems were brought up, our two teams were already completely aligned.

And so the design, the data centers were created for Vera Rubin.

The Vera Rubin is designed and integrated into your complete stack, into your networking into your security.

And so the moment that our systems were rolling off the lines, they were being stood up at Microsoft.

So I'm incredibly excited about the collaboration.

Yeah, I know this speed of light execution between the teams is fantastic to see.

And of course, all this is to power the ecosystem around us, right?

I mean, you and I, having grown up with the PC, the server and now with AI, have always thought about ultimately it's about creating the opportunity for every developer, every organization to build on the work that we do and the platforms we create.

In speaking of that, there's a lot of software that Nvidia builds.

That's all also coming.

For example, we're going to have your models in Foundry your tooling in Foundry.

We're going to have, in fact, your software even help us with accelerating our workloads when it comes to even the data warehouse.

We're going to obviously have stuff in Windows.

Just talk a little bit about that broader vision of what does it mean for us, an opportunity, right.

Because everybody talks about this one model or one piece of tech, but it's about the broadest, biggest opportunity for people to create value.

Maybe you want to share a little bit about that.

Well, we've been preparing for this moment.

You know what happened in the last several months.

We've been working for a decade and a half together, getting ready for really what happened in the last several months.

All of a sudden, because of agentic systems, the convergence of these really great models, AI is now useful.

If you just look at GitHub, the the commits into GitHub is gone completely parabolic.

In the last several months, the number of commits increased by a factor of three.

It's clear that agentic systems are useful, that it's doing productive work, and also tokens are now profitable as a result.

And so the amount of demand for compute between the usage of the AI and the computation that's necessary for agents, the compute demand has really gone through the roof.

100

Well, one of the things that we've been we've been doing together is making sure that all of the tools that the agents are going to use are fully accelerated.

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Fabric, for example, is now fully accelerated.

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We're accelerating data processing, SQL, Spark, semantic based vector, vector based, graph based.

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We're going to make sure that all of the tools that are available on Azure are going to be fully GPU accelerated, because the agents are going to be impatient.

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The faster we can get the answers back to the agents, the faster they can iterate, the faster can generate tokens, which are ultimately what the developers both of our customers would like to do is generate a lot of tokens that are really profitable, that are highly intelligent.

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Now. Thank you so much, Jensen, for the partnership and the leadership and the innovation that you bring to this entire ecosystem, and really thrilled to be working closely with you and the team and bring all this to the developers here and beyond, and look forward to seeing what the next few months and the next year bring in terms of the innovation that gets built on top of the platform.

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So thank you again for joining this late in the night from Taipei.

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Thank you so much Satya for your partnership and friendship. Thank you.

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Thank you.

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跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

跟读练习: Jensen Huang & Satya Nadella on unmetered intelligence | Microsoft Build 2026 - 通过视频学习英语口语

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