Name: 英偉達首席執行官黃仁勳：DeepSeek 事件凸顯了對人工智能計算能力的巨大需求 (Nvidia CEO Huang: DeepSeek incident underscored the substantial demand for AI compute power)
Uploaded: 2025-02-27T07:58:45.000Z
Duration: 5 min 14 s

The guide was also above, as you mentioned.

正如您所提到的，導遊也在上面。

分詞構句

Now, earlier today, speaking of demand, I was talking to Amazon CEO Andy Jassy.

今天早些時候，說到需求，我與亞馬遜首席執行官安迪-賈西（Andy Jassy）進行了交談。

第二類條件句型

He told me that as of now, if he had more AI resources to sell through AWS, he could sell more.

他告訴我，就目前而言，如果他有更多的人工智能資源通過 AWS 銷售，他就能賣得更多。

That's kind of the short-term signal of demand that you talked about on the call.

這就是你在電話中提到的短期需求信號。

Tell me more about the mid-term signals that investors should be aware of that give you confidence in the continued demand.

請告訴我更多投資者應該注意的中期信號，這些信號讓您對持續需求充滿信心。

The scale-outs of data centers, AI factories, relative to what you've historically seen.

數據中心、人工智能工廠的規模擴張，相對於你以往所看到的。

The short-term signal are just our POs and the forecasts.

短期信號只是我們的 PO 和預測。

And on top of that, the things that are not forecasted are new startup companies that are spinning off.

此外，沒有預測到的是正在分拆的新創公司。

And at the risk of forgetting any of them, I won't mention any of them, but there's some really, really fantastic startups that have come out as a result of new reasoning AI capabilities and artificial general intelligence capabilities that they have breakthroughs in.

冒著遺忘其中任何一家公司的風險，我不會提及其中任何一家公司，但有一些非常非常棒的初創公司，因為它們在新的推理人工智能能力和人工通用智能能力方面取得了突破性進展而應運而生。

And several of them, there's several of them that are related to agentic AIs, really exciting companies.

其中有幾家公司與代理人工智能有關，非常令人興奮。

And there's several of them related to physical AIs.

其中有幾個與物理人工智能有關。

There's just handfuls of each one of them, and each one of them needs additional compute.

它們中的每一個都屈指可數，而且每一個都需要額外的計算。

And that's the type of things that Andy talks about, because they need to go to AWS and they have urgent need for more compute right away.

這就是安迪所說的那種情況，因為他們需要使用 AWS，而且他們急需立即獲得更多計算能力。

And so that's on top of what we already knew to have POs and forecasts and such.

是以，這是在我們已經知道的 PO 和預測等基礎上進行的。

The midterm comes from the fact that this year's capital investment for data centers is so much greater than last year's.

今年數據中心的資本投資額遠高於去年，這就是中期投資的原因。

And of course, we had a very large year last year.

當然，我們去年的規模也很大。

It stands to reason that with Blackwell and with all the new data centers going online, we're gonna have a fairly great year.

有理由相信，隨著布萊克韋爾公司和所有新數據中心的上線，我們今年的業績會相當不錯。

Now long-term, the thing that's really exciting is we're just at the beginning of the reasoning AI era.

從長遠來看，真正令人興奮的是，我們才剛剛開始人工智能推理時代。

This is the time when AI is thinking to itself before it answers a question, instead of just immediately generating an answer.

這時，人工智能在回答問題前會先思考，而不是立即生成答案。

They'll reason about it, maybe break it down step by step.

他們會講道理，也許會一步步分解。

It'll do maybe some searching in its own mind before it creates and composes a smart answer for you.

在為您創建和編寫智能答案之前，它可能會在自己的腦海中進行一些搜索。

The amount of computation necessary to do that reasoning process is 100 times more than what we used to do.

推理過程所需的計算量是我們過去的 100 倍。

So if you could imagine, we thought computation, the amount of compute necessary, was a lot last year.

是以，如果你能想象，我們認為去年的計算量、必要的計算量很大。

And then all of a sudden, reasoning AI, DeepSeq was an example of that, ChatGPT 4.0 is an example of that, Grok 3 Reasoning's an example of that.

突然之間，人工智能推理出現了，DeepSeq 就是一個例子，ChatGPT 4.0 就是一個例子，Grok 3 Reasoning 也是一個例子。

So all of these reasoning AI models now need a lot more compute than what we used to, used to, used to, we're expecting.

是以，現在所有這些人工智能推理模型所需的計算量都比我們過去、過去、過去所期望的要大得多。

Because some people took DeepSeq to mean actually that you need less compute, right?

因為有些人認為 DeepSeq 實際上意味著你需要的計算量更少，對嗎？

Because the initial report was that they were doing more with less.

因為最初的報告稱，他們是在少花錢多辦事。

But you're saying, in fact, some of what came out of DeepSeq was the opposite, that there's gonna be more compute demanded.

但你的意思是，事實上，DeepSeq 的一些成果恰恰相反，它需要更多的計算能力。

There are three phases in how AI works, how AI is developed largely.

人工智能的運作和發展大致分為三個階段。

It's kind of like us going through high school.

A lot of basic math, basic language, basic everything.

大量的基礎數學、基礎語言和基礎知識。

That basic understanding of human knowledge is essential to do what is the next step, which is called post-training.

對人類知識的基本瞭解對於下一步的工作至關重要，這就是所謂的後期培訓。

In post-training, you might get human feedback.

在培訓後，您可能會得到人的反饋。

You know, it's like a teacher showing it to you.

你知道，這就像老師給你演示一樣。

We call it reinforcement learning human feedback.

我們稱之為人類反饋強化學習。

You might practice and do thought experiments.

We call it reinforcement learning AI feedback.

You could either also do tests and practice, and we call it reinforcement learning verifiable reward feedback.

你也可以做測試和練習，我們稱之為強化學習可驗證的獎勵反饋。

So now, basically, it's teaching AIs how to be better AIs.

所以，現在基本上是在教人工智能如何成為更好的人工智能。

That post-training process is where an enormous amount of innovation is happening right now.

培訓後的過程正是大量創新的起點。

A lot of it happened with these reasoning models, and that computation load could be 100 times more than pre-training.

很多事情都發生在這些推理模型上，而計算負荷可能是訓練前的 100 倍。

And then here comes inference, the reasoning process.

Instead of just spewing out an answer, when prompted, it reasons about it.

在收到提示時，它不會直接給出答案，而是會進行推理。

It thinks about how best to answer that question, breaks it down step by step, might even reflect upon it, come up with several versions, pick the best one, and then presents it to you.

它會思考如何最好地回答這個問題，一步一步地將問題分解，甚至進行反思，提出幾個版本，選出最好的一個，然後將其呈現給你。

So the amount of computation that we have to do even at inference time now is 100 times more than what we used to do when ChatGPT first came out.

是以，即使在推理時間，我們現在要做的計算量也比 ChatGPT 剛問世時多 100 倍。

And so all of a sudden, the combination of all these ideas largely related to reinforcement learning and synthetic data generation and reasoning, all of this is just causing compute demand to go sky high.

是以，突然之間，所有這些與強化學習、合成數據生成和推理相關的想法結合在一起，導致計算需求急劇上升。