Name: 黃仁勳稱英偉達將在 2025 年達到 1200 美元：NVDA 股票將翻 10 倍 | NVDA 股票 (Jensen Huang Said Nvidia Will Hit $1200 In 2025: NVDA Stock Will 10X | NVDA Stock)
Uploaded: 2025-02-19T15:22:02.000Z
Duration: 37 min 30 s

程度副詞

Okay, so let's talk about about AI and I know you just to kind of do some math to ground things here.

好吧，讓我們來談談人工智能，我知道你只是想在這裡做一些數學上的基礎工作。

Let's just say that the total sort of compute capacity of all GPUs in the world today is X.

比方說，當今世界上所有 GPU 的總計算能力排序為 X。

What do you think, what do you think, what multiple of X will we be at in five years?

你覺得，你覺得，五年後我們的 X 倍率會是多少？

First of all, you know that I'm going to regret saying this.

首先，你知道我說了這些話會後悔的。

And this is, I'm a public company, you crazy person.

我是一家上市公司，你這個瘋子。

See this is, here's, this is, how nice is it to be private?  Safe to say considerably more?

你看，這就是，這就是，私密性有多好？  可以說是相當不錯吧？

Okay so let's reason about it, let's reason our way through, okay?

好吧，讓我們來推理一下，讓我們推理一下，好嗎？

The world has installed about a trillion dollars worth of data centers.

全球已安裝了價值約一萬億美元的數據中心。

Those trillion dollars worth of data centers use as general purpose computing.

這些價值萬億美元的數據中心用作通用計算。

General purpose computing is run as course.

And so the world is going to accelerate everything.

And so we're going to accelerate everything.

When we accelerate everything, every single data center, every single computer will be an accelerated server.

當我們加速一切時，每一個數據中心、每一臺計算機都將成為加速服務器。

Well there's about a trillion dollars worth of computers if we don't grow at all over the next, call it four years that we have to go replace.

如果我們在接下來的四年裡不增長，那麼就有價值一萬億美元的電腦需要更換。

Four years, six years, pick your number of years.

But if the computer industry continues to grow at some 20% or so, we'll probably have to replace over the course of next, you know, pick your number of years, about two trillion dollars worth of computers with accelerated computing.

但是，如果計算機行業繼續以 20% 左右的速度增長，那麼在接下來的幾年裡，我們可能需要更換價值約兩萬億美元的計算機，並採用加速計算技術。

And this is the second part, this is the reason why all of you, Stripe, you're onto something just absolutely monumental.

這是第二部分，這也是為什麼 Stripe 所有的人都在做一件絕對不朽的事情。

This idea, this idea called, you know, you've heard me say an industrial revolution.

這個想法，這個叫做 "工業革命 "的想法，你知道，你聽我說過。

We are producing something for the very first time that has never been produced before.

我們將首次製作從未製作過的作品。

And we're producing it in extremely high volume.

And the production of this thing requires a new instrument that never existed before.

而生產這種東西需要一種前所未有的新工具。

And the thing that we're producing for the very first time, for the mathematicians and all the computer scientists in the room, for all of you, you know that we're producing tokens.

我們第一次製作的東西，對於在座的數學家和計算機科學家來說，對於你們所有人來說，你們知道我們正在製作代幣。

We're producing floating point numbers at high volume for the first time in history.

我們有史以來第一次大批量生產浮點數。

And the floating point numbers have value.

The reason why they have value is because it's intelligence.

它們之所以有價值，是因為它是智慧。

You can take these floating point numbers, you reformulate it in such a way that it turns into English, French, proteins, chemicals, graphics, images, videos, robotic articulation, steering wheel articulation.

你可以把這些浮點數重新組合，變成英語、法語、蛋白質、化學物質、圖形、影像、視頻、機器人銜接、方向盤銜接。

We're producing tokens at extraordinary scale.

我們正在以超常規的規模生產代幣。

Now we've discovered a way through all of the work that we do with artificial intelligence to produce tokens of almost any kind.

現在，我們發現了一種方法，通過人工智能的所有工作，幾乎可以生產出任何種類的代幣。

So now the world is going to produce an enormous amount of tokens.

是以，現在全世界將生產大量代幣。

Now these tokens are going to be produced in new types of data centers.

現在，這些代幣將在新型數據中心生產。

Back in the last industrial revolution, water comes into a machine.

在上一次工業革命中，水進入了一臺機器。

You light the water on fire, turn it into steam, and then it turns into electrons.

把水點燃，變成蒸汽，然後變成電子。

In this new industrial revolution, electrons come in and floating point numbers come out.

在這場新的工業革命中，電子進來了，浮點數出來了。

And just like the last industrial revolution, nobody understood why this electricity is so valuable and is now sold, marketed, kilowatt hours per dollar.

就像上一次工業革命一樣，沒有人明白為什麼這些電力如此寶貴，而且現在還以每美元千瓦時的價格出售、銷售。

And so now we have a million tokens per dollar.

是以，我們現在每美元有一百萬代幣。

And so that same logic is as incomprehensible to a lot of people as the last industrial revolution, but it's going to be completely normal in the next 10 years.

是以，對很多人來說，這種邏輯就像上一次工業革命一樣難以理解，但在未來 10 年內，它將變得完全正常。

Well, these tokens are going to create new products, new services, enhance productivity on a whole slew of industries, $100 trillion worth of industries on top of us.

這些代幣將創造新的產品、新的服務，提高各行各業的生產力，其中包括價值 100 萬億美元的行業。

And so this industry is going to be gigantic.

是以，這個產業將是一個巨大的產業。

And in order to monetize that, transact that, you're going to need Stripe.

為了實現貨幣化和交易，你需要 Stripe。

I got to tell you, this is one of my favorite companies.

我得告訴你，這是我最喜歡的公司之一。

The first time I met Patrick, he had to explain Stripe to me.

我第一次見到帕特里克時，他不得不向我解釋 Stripe。

We tried to refine the descriptions over time, but you had an early version.

隨著時間的推移，我們試圖完善這些描述，但你們有一個早期版本。

First of all, you're in a complicated business no matter what.

首先，無論如何，你從事的都是一項複雜的工作。

But nonetheless, I was so inspired by it.

It's incredible what you guys have built.

Are we going to get you migrated to Stripe billing now that we have usage-based billing?

既然我們有了基於使用量的計費方式，我們是否會讓你們遷移到 Stripe 計費方式？

I wish I had a business that required billing.

我真希望我的生意也需要計費。

I think your public filing suggests you're doing a lot of billing.

我認為你的公開申報表明你在做大量的賬單。

It's only 10 transactions, just so you know.

只有 10 筆交易，只是讓你知道一下。

Your economics serving us is like nothing.

你們為我們提供的經濟服務真是無與倫比。

So thinking about these token factories, I feel like a big question right now is whether the models saturate.

是以，考慮到這些代幣工廠，我覺得現在的一個大問題是模型是否飽和。

In the sense that we demoed the Sigma Assistant on stage earlier, and you can write some natural language, and we convert that to SQL.

我們之前在臺上演示了西格瑪助手，你可以編寫一些自然語言，然後我們將其轉換為 SQL。

And going from a 7 billion parameter model to a 70 billion parameter model or something like that, there might be a significant kind of consequential improvement in query accuracy for the user for the typical kinds of queries that people tend to construct.

從 70 億個參數模型到 700 億個參數模型或類似的模型，對於人們傾向於構建的典型查詢類型，用戶的查詢準確性可能會有顯著的提高。

But maybe going to a model that's 10x larger than that is sort of unnecessary.

但是，也許使用比它大 10 倍的機型是沒有必要的。

At some point, you get to good enough, you can reliably convert the natural language to SQL.

到了一定程度，你就可以將自然語言可靠地轉換成 SQL。

I think there's a question for the use cases for which LLMs are being deployed.

我認為這與部署 LLM 的用例有關。

What does that saturation curve look like?

And for how many use cases does one need a trillion parameter model or a 10 trillion parameter model?

有多少用例需要萬億參數模型或 10 萬億參數模型？

Or do we simply reach a point where some number that is, say, less than 100 billion is sufficient?

還是說，我們只需達到一個點，比如說某個小於 1000 億的數字就足夠了？

Or is that even a reasonable way to look at the question in the first place?

或者說，從一開始就這樣看待這個問題是否合理？

In 2012, AlexNet was computer vision, ImageNet, image recognition, 82% or something like that  Over the next almost not quite 10 years, I think it was like seven years, every single year, the accuracy error reduced in half, right?

2012 年，AlexNet 是計算機視覺，ImageNet 是圖像識別，準確率為 82%，差不多是這個水準 在接下來的差不多 10 年裡，我想大概是 7 年，每一年的準確率誤差都減少了一半，對嗎？

Every year, the error reduced in half or otherwise known as Moore's law.  So you double the performance, you double the accuracy, and you double its believability every single year.

每一年，誤差都會減半，也就是所謂的摩爾定律。  是以，性能每年翻一番，準確性每年翻一番，可信度每年翻一番。

Over the course of seven years, it's now superhuman.

在七年的時間裡，它現在已經是超人了。

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黃仁勳稱英偉達將在 2025 年達到 1200 美元：NVDA 股票將翻 10 倍 | NVDA 股票 (Jensen Huang Said Nvidia Will Hit $1200 In 2025: NVDA Stock Will 10X | NVDA Stock)

extraordinary

term

revenue

artificial