You have arrived at a developer's conference.

您正在參加開發者大會。

There will be a lot of science described, algorithms, computer architecture, mathematics.

將介紹大量科學知識、算法、計算機結構和數學。

Blackwell is not a chip.

布萊克韋爾不是籌碼。

Blackwell is the name of a platform.

Blackwell 是一個平臺的名稱。

People think we make GPUs, and we do.

人們認為我們製造 GPU，而我們確實是這樣做的。

But GPUs don't look the way they used to.

但 GPU 已今非昔比。

This is Hopper.

這是霸王

Hopper changed the world.

霍珀改變了世界。

This is Blackwell.

我是布萊克威爾

It's OK, Hopper.

沒事的，哈波

208 billion transistors.

2 080 億個半導體

And so you could see, I can see, there's a small line between two dies.

你可以看到，我也可以看到，兩個模具之間有一條小線。

This is the first time two dies have been abutted like this together in such a way that the two dies think it's one chip.

這是第一次有兩個模具像這樣對接在一起，以至於兩個模具都認為這是一個芯片。

There's 10 terabytes of data between it, 10 terabytes per second, so that these two sides of the Blackwell chip have no clue which side they're on.

在它之間有 10 TB 的數據，每秒 10 TB，所以布萊克韋爾芯片的兩邊根本不知道自己在哪一邊。

There's no memory locality issues, no cache issues.

沒有內存定位問題，也沒有緩存問題。

It's just one giant chip, and it goes into two types of systems.

它只是一個巨大的芯片，可用於兩類系統。

The first one is form-fit function compatible to Hopper.

第一種是與 Hopper 兼容的外形功能。

And so you slide on Hopper, and you push in Blackwell.

於是你滑動霍普，推入布萊克韋爾。

That's the reason why one of the challenges of ramping is going to be so efficient.

這也是提升效率的挑戰之一。

There are installations of Hoppers all over the world, and they could be, you know, the same infrastructure, same design.

世界各地都安裝有 Hoppers，它們可能是，你知道，相同的基礎設施，相同的設計。

The power, the electricity, the thermals, the software, identical, push it right back.

動力、電力、熱能、軟件，完全相同，把它推回去。

And so this is a Hopper version for the current HGX configuration.

是以，這是當前 HGX 配置的 Hopper 版本。

And this is what the second Hopper looks like this.

這就是第二個霸王的樣子。

Now, this is a prototype board.

現在，這是一塊原型板。

This is a fully functioning board, and I'll just be careful here.

這是一個功能齊全的董事會，我在這裡會小心謹慎。

This right here is, I don't know, $10 billion?

這個，我不知道，100 億美元？

The second one's $5 billion.

第二個是 50 億美元。

It gets cheaper after that, so any customers in the audience, it's okay.

之後會越來越便宜，所以觀眾中的任何顧客都可以放心。

The Gray CPU has a super fast chip-to-chip link.

Gray CPU 擁有超快的芯片間鏈路。

What's amazing is this computer is the first of its kind where this much computation, first of all, fits into this small of a place.

令人驚歎的是，這臺計算機是同類產品中第一臺能在這麼小的地方進行如此大量計算的計算機。

Second, it's memory coherent.

第二，記憶連貫。

They feel like they're just one big happy family working on one application together.

他們覺得自己就像一個快樂的大家庭，一起為一個應用程序工作。

We created a processor for the generative AI era, and one of the most important parts of it is content token generation.

我們為人工智能生成時代創建了一個處理器，其中最重要的部分就是內容令牌生成。

We call it, this format is FP4.

我們稱之為 FP4。

The rate at which we're advancing computing is insane, and it's still not fast enough, so we built another chip.

我們的計算技術發展速度太快了，但還是不夠快，所以我們又造了一個芯片。

This chip is just an incredible chip.

這款芯片是一款令人難以置信的芯片。

We call it the NVLink Switch.

我們稱之為 NVLink 交換機。

It's 50 billion transistors.

這是 500 億個半導體。

It's almost the size of Hopper all by itself.

它幾乎和霸王號一樣大。

This switch chip has four NVLinks in it, each 1.8 terabytes per second, and it has computation in it, as I mentioned.

這個交換芯片中有四個 NVLink，每個每秒 1.8 TB，而且正如我提到的，它還帶有計算功能。

What is this chip for?

這個芯片有什麼用？

If we were to build such a chip, we can have every single GPU talk to every other GPU at full speed at the same time.

如果我們能製造出這樣的芯片，我們就能讓每一個 GPU 同時與其他 GPU 進行全速對話。

You can build a system that looks like this.

您可以建立這樣一個系統。

Now, this system is kind of insane.

現在，這個系統有點瘋狂。

This is one DGX.

這就是 DGX。

This is what a DGX looks like now.

這就是 DGX 現在的樣子。

Just so you know, there are only a couple, two, three Exaflops machines on the planet as we speak, and so this is an Exaflops AI system in one single rack.

你要知道，目前地球上只有幾臺、兩臺、三臺 Exaflops 機器，而這就是一個機架上的 Exaflops AI 系統。

I want to thank some partners that are joining us in this.

我要感謝與我們一起參與這項工作的一些合作伙伴。

AWS is gearing up for Blackwell.

AWS 正在為 Blackwell 做準備。

They're going to build the first GPU with secure AI.

他們將打造第一個具有安全人工智能的 GPU。

They're building out a 222 Exaflops system for CUDA-accelerating SageMaker AI, for CUDA-accelerating Bedrock AI.

他們正在為 CUDA 加速的 SageMaker AI 和 CUDA 加速的 Bedrock AI 構建一個 222 Exaflops 系統。

Amazon Robotics is working with us using NVIDIA Omniverse and Isaac Sim.

亞馬遜機器人公司正在使用英偉達™（NVIDIA®）Omniverse 和 Isaac Sim 與我們合作。

AWS Health has NVIDIA Health integrated into it, so AWS has really leaned into accelerated computing.

AWS Health 中集成了英偉達™（NVIDIA®）Health，是以 AWS 真的向加速計算領域傾斜了。

Google is gearing up for Blackwell.

谷歌正在為布萊克韋爾做準備。

GCP already has A100s, H100s, T4s, L4s, a whole fleet of NVIDIA CUDA GPUs, and they recently announced the GEMMA model that runs across all of it.

GCP 已經擁有 A100、H100、T4、L4 和一整套英偉達™（NVIDIA®）CUDA GPU，他們最近還發布了跨所有 GPU 運行的 GEMMA 模型。

We're working to optimize and accelerate every aspect of GCP.

我們正在努力優化和加速 GCP 的各個方面。

We're accelerating Dataproc, which is for data processing, their data processing engine, JAX, XLA, Vertex AI, and MuJoCo for robotics, so we're working with Google and GCP across a whole bunch of initiatives.

我們正在加速 Dataproc（用於數據處理）、他們的數據處理引擎、JAX、XLA、Vertex AI 和用於機器人技術的 MuJoCo，是以我們正在與谷歌和 GCP 開展一系列合作。

Oracle is gearing up for Blackwell.

甲骨文正在為布萊克韋爾做準備。

Oracle is a great partner of ours for NVIDIA DGX Cloud, and we're also working together to accelerate something that's really important to a lot of companies, Oracle Database.

甲骨文是英偉達 DGX Cloud 的最佳合作伙伴，我們還在合作加速對許多公司非常重要的 Oracle 數據庫。

Microsoft is accelerating, and Microsoft is gearing up for Blackwell.

微軟正在加速發展，微軟正在為布萊克韋爾做好準備。

Microsoft and NVIDIA has a wide-ranging partnership.

微軟與英偉達™（NVIDIA®）建立了廣泛的合作關係。

We're accelerating CUDA-accelerating all kinds of services when you chat, obviously, and AI services that are in Microsoft Azure.

當你哈拉時，我們正在加速 CUDA--加速各種服務，很明顯，還有 Microsoft Azure 中的人工智能服務。

It's very, very likely NVIDIA is in the back doing the inference and the token generation.

英偉達很有可能在後面進行推理和令牌生成。

They built the largest NVIDIA InfiniBand supercomputer, basically a digital twin of ours or a physical twin of ours.

他們建造了最大的英偉達InfiniBand超級計算機，基本上是我們的數字孿生或物理孿生。

We're bringing the NVIDIA ecosystem to Azure, NVIDIA DGX Cloud to Azure, NVIDIA Omniverse is now hosted in Azure, NVIDIA Healthcare is in Azure, and all of it is deeply integrated and deeply connected with Microsoft Fabric.

我們正在將英偉達生態系統引入 Azure，將英偉達 DGX Cloud 引入 Azure，英偉達 Omniverse 現在託管在 Azure，英偉達醫療保健也在 Azure，而且所有這些都與 Microsoft Fabric 深度集成、深度連接。

And now, it's a pre-trained model, so it's pretty clever, and it is packaged and optimized to run across NVIDIA's installed base, which is very, very large.

現在，它是一個預先訓練好的模型，所以非常聰明，而且經過打包和優化，可以在英偉達龐大的安裝基礎上運行。

What's inside it is incredible.

裡面的東西令人難以置信。

You have all these pre-trained, state-of-the-art open-source models.

你擁有所有這些預先訓練好的、最先進的開源模型。

They could be open-source.

它們可以是開源的。

They could be from one of our partners.

它們可能來自我們的合作伙伴。

It could be created by us, like NVIDIA Moment.

它可以由我們創建，比如 NVIDIA Moment。

It is packaged up with all of its dependencies.

它與所有依賴項一起打包。

So CUDA, the right version, CUDNN, the right version, TensorRT, LLM, distributing across the multiple GPUs, Trident inference server, all completely packaged together.

是以，CUDA（正確的版本）、CUDNN（正確的版本）、TensorRT、LLM（在多個 GPU 上分佈）、Trident 推理服務器，都完全打包在一起。

It's optimized, depending on whether you have a single GPU, multi-GPU, or multi-node of GPUs, it's optimized for that, and it's connected up with APIs that are simple to use.

根據你是使用單 GPU、多 GPU 還是多節點 GPU，它都進行了優化，並與簡單易用的 API 相連接。

These packages, incredible bodies of software, will be optimized and packaged, and we'll put it on a website, and you can download it, you can take it with you, you can run it in any cloud, you can run it in your own data center, you can run it in workstations if it fit, and all you have to do is come to ai.nvidia.com.

這些套裝軟體，令人難以置信的軟件體，將被優化和打包，我們將把它放在一個網站上，你可以下載它，你可以隨身攜帶它，你可以在任何雲中運行它，你可以在你自己的數據中心運行它，如果合適的話，你可以在工作站中運行它，你所要做的就是訪問 ai.nvidia.com。

We call it NVIDIA Inference Microservice, but inside the company, we all call it NIMS.

我們稱之為 NVIDIA Inference Microservice，但在公司內部，我們都稱之為 NIMS。

We have a service called NIMO Microservice that helps you curate the data, preparing the data so that you could teach this, onboard this AI, you fine-tune them, and then you guardrail it, you can even evaluate the answer, evaluate its performance against other examples.

我們有一項名為 "NIMO 微服務 "的服務，可以幫助你整理數據、準備數據，這樣你就可以教授人工智能，讓人工智能上崗，對其進行微調，然後對其進行護欄，甚至可以評估答案，根據其他例子評估其性能。

And so we are effectively an AI foundry.

是以，我們實際上是一家人工智能代工廠。

We will do for you and the industry on AI what TSMC does for us building chips.

在人工智能領域，我們將像臺積電為我們製造芯片那樣，為您和業界做出貢獻。

And so we go to it with our, go to TSMC with our big ideas, they manufacture it, and we take it with us.

是以，我們帶著我們的遠大理想去找臺積電，他們生產，我們帶走。

And so exactly the same thing here, AI foundry, and the three pillars are the NIMS, NIMO Microservice, and DGX Cloud.

人工智能代工廠也是如此，三大支柱是 NIMS、NIMO 微服務和 DGX 雲。

We're announcing that NVIDIA AI foundry is working with some of the world's great companies.

我們宣佈英偉達™（NVIDIA®）人工智能代工廠正在與世界上一些偉大的公司合作。

SAP generates 87% of the world's global commerce.

SAP 創造了全球 87% 的貿易額。

Basically, the world runs on SAP.

從根本上說，世界是靠 SAP 運行的。

We run on SAP.

我們使用 SAP 系統。

NVIDIA and SAP are building SAP Jewel co-pilots using NVIDIA NIMO and DGX Cloud.

英偉達™（NVIDIA®）和SAP正在利用英偉達™（NVIDIA®）NIMO和DGX Cloud打造SAP Jewel協同駕駛儀。

ServiceNow, they run, 85% of the world's Fortune 500 companies run their people and customer service operations on ServiceNow.

全球 85% 的財富 500 強企業都在 ServiceNow 上運行其員工和客戶服務業務。

And they're using NVIDIA AI foundry to build ServiceNow assist virtual assistants.

他們正在使用英偉達™（NVIDIA®）人工智能代工廠構建 ServiceNow 輔助虛擬助手。

Cohesity backs up the world's data.

Cohesity 為全球數據提供支持。

They're sitting on a goldmine of data.

他們掌握著一座數據金礦。

Hundreds of exabytes of data, over 10,000 companies.

數百億字節的數據，超過 10,000 家公司。

NVIDIA AI foundry is working with them, helping them build their Gaia generative AI agent.

英偉達™（NVIDIA®）人工智能代工廠正在與他們合作，幫助他們建立 Gaia 生成式人工智能代理。

Snowflake is a company that stores the world's digital warehouse in the cloud and serves over 3 billion queries a day for 10,000 enterprise customers.

Snowflake 是一家在雲中存儲全球數字倉庫的公司，每天為 10,000 家企業客戶提供超過 30 億次查詢服務。

Snowflake is working with NVIDIA AI foundry to build co-pilots with NVIDIA NIMO and NIMS.

雪花公司正在與英偉達™（NVIDIA®）人工智能代工廠合作，利用英偉達™（NVIDIA®）NIMO 和 NIMS 構建協同飛行器。

NetApp, nearly half of the files in the world are stored on-prem on NetApp.

全球近一半的文件都存儲在 NetApp 公司內部。

NVIDIA AI foundry is helping them build chatbots and co-pilots like those vector databases and retrievers with NVIDIA NIMO and NIMS.

英偉達™（NVIDIA®）人工智能代工廠正在幫助他們利用英偉達™（NVIDIA®）NIMO 和 NIMS 構建哈拉機器人和輔助機器人，如矢量數據庫和檢索器。

And we have a great partnership with Dell.

我們與戴爾公司建立了良好的合作關係。

Everybody who is building these chatbots and generative AI, when you're ready to run it, you're going to need an AI factory.

每個正在構建哈拉機器人和生成式人工智能的人，當你準備好運行它時，都需要一個人工智能工廠。

And nobody is better at building end-to-end systems of very large scale for the enterprise than Dell.

在為企業構建端到端大規模系統方面，沒有人比戴爾更勝一籌。

And so anybody, any company, every company will need to build AI factories.

是以，任何人、任何公司、每家公司都需要建立人工智能工廠。

And it turns out that Michael is here.

原來邁克爾在這裡

He's happy to take your order.

他很樂意為您點餐。

We need a simulation engine that represents the world digitally for the robot so that the robot has a gym to go learn how to be a robot.

我們需要一個模擬引擎，以數字方式為機器人呈現世界，讓機器人有一個健身房來學習如何成為一個機器人。

We call that virtual world Omniverse.

我們稱這個虛擬世界為 "Omniverse"。

And the computer that runs Omniverse is called OVX.

運行 Omniverse 的計算機叫做 OVX。

And OVX, the computer itself, is hosted in the Azure cloud.

而 OVX（計算機本身）則託管在 Azure 雲中。

The future of heavy industries starts as a digital twin.

重工業的未來始於數字孿生。

The AI agents helping robots, workers and infrastructure navigate unpredictable events in complex industrial spaces will be built and evaluated first in sophisticated digital twins.

幫助機器人、工人和基礎設施在複雜的工業空間中應對不可預測事件的人工智能代理將首先在複雜的數字雙胞胎中構建和評估。

Once you connect everything together, it's insane how much productivity you can get.

一旦把所有東西連接在一起，你就能獲得驚人的生產力。

And it's just really, really wonderful.

它真的非常非常棒。

All of a sudden, everybody's operating on the same ground truth.

突然之間，每個人都在根據相同的基本事實開展工作。

You don't have to exchange data and convert data, make mistakes.

您不必交換數據和轉換數據，也不必犯錯誤。

Everybody is working on the same ground truth.

每個人都在根據相同的基本事實開展工作。

From the design department to the art department, the architecture department, all the way to the engineering and even the marketing department.

從設計部門到藝術部門、建築部門，一直到工程部門，甚至營銷部門。

Today, we're announcing that Omniverse cloud streams to the Vision Pro.

今天，我們宣佈 Omniverse 雲流媒體將應用於 Vision Pro。

And it is very, very strange that you walk around virtual doors when I was getting out of that car.

非常非常奇怪的是，當我從車裡出來時，你竟然繞著虛擬門走了一圈。

And everybody does it.

每個人都會這麼做。

It is really, really quite amazing.

這真的非常非常神奇。

Vision Pro, connected to Omniverse, portals you into Omniverse.

Vision Pro 與 Omniverse 相連，可將您帶入 Omniverse。

And because all of these CAD tools and all these different design tools are now integrated and connected to Omniverse, you can have this type of workflow.

現在，所有這些 CAD 工具和不同的設計工具都已集成並連接到 Omniverse，是以可以實現這種工作流程。

Really incredible.

真是不可思議。

This is NVIDIA Project Grid.

這就是英偉達網格項目。

A general purpose foundation model for humanoid robot learning.

仿人機器人學習的通用基礎模型。

The group model takes multimodal instructions and past interactions as input and produces the next action for the robot to execute.

群模型將多模態指令和過去的互動作為輸入，並生成機器人要執行的下一個動作。

We developed Isaac Lab, a robot learning application to train Grid on Omniverse Isaac Sim.

我們開發了 Isaac Lab 機器人學習應用程序，用於在 Omniverse Isaac Sim 上訓練網格。

And we scale out with Osmo, a new compute orchestration service that coordinates workflows across DGX systems for training and OVX systems for simulation.

我們還利用 Osmo 進行擴展，這是一種全新的計算協調服務，可協調用於培訓的 DGX 系統和用於仿真的 OVX 系統之間的工作流程。

The group model will enable a robot to learn from a handful of human demonstrations so it can help with everyday tasks.

該群體模型將使機器人能夠從少量人類演示中學習，從而幫助完成日常任務。

And emulate human movement just by observing us.

並通過觀察我們來模仿人類的動作。

All this incredible intelligence is powered by the new Jetson Thor robotics chips designed for Groot, built for the future.

所有這些令人難以置信的智能都由專為格魯特設計、面向未來的新型 Jetson Thor 機器人芯片提供動力。

With Isaac Lab, Osmo and Groot, we're providing the building blocks for the next generation of AI powered robotics.

通過 Isaac Lab、Osmo 和 Groot，我們為下一代人工智能機器人提供了基石。

About the same size.

大小差不多

The soul of NVIDIA.

英偉達的靈魂

The intersection of computer graphics, physics, artificial intelligence, it all came to bear at this moment.

計算機圖形學、物理學、人工智能的交匯點，在這一刻全部顯現出來。

The name of that project, General Robotics 003.

該項目名稱為通用機器人 003。

I know, super good.

我知道，超級棒。

Super good.

超級棒

Super good.

超級棒

Well, I think we have some special guests.

好吧，我想我們有一些特殊的客人。

Do we?

有嗎？

Hey guys.

你們好

So I understand you guys are powered by Jetson.

據我所知，你們是由杰特森驅動的。

They're powered by Jetsons.

它們的動力來自 Jetsons。

Little Jetson robotics computers inside.

內有小杰特森機器人電腦。

They learned to walk in Isaac Sim.

他們學會了在 Isaac Sim 中行走。

Ladies and gentlemen, this is Orange.

女士們，先生們，這裡是奧蘭治。

And this is the famous Green.

這就是著名的綠色。

They are the BDX robots of Disney.

它們就是迪士尼的 BDX 機器人。

Amazing Disney research.

令人驚歎的迪士尼研究

Come on you guys, let's wrap up.

來吧，夥計們，我們收工了。

Let's go.

我們走吧

Five things.

五件事

Where are you going?

你要去哪兒？

I sit right here.

我就坐在這裡。

Don't be afraid.

別害怕

Come here, Green.

過來，格林

Hurry up.

快點

What are you saying?

你在說什麼？

No, it's not time to eat.

不，還沒到吃飯時間。

It's not time to eat.

還沒到吃飯時間

I'll give you a snack in a moment.

我馬上給你點心。

Let me finish up real quick.

讓我快點說完。

Come on, Green.

來吧，格林

Hurry up.

快點

Stop wasting time.

別浪費時間了

This is what we announced to you today.

這就是我們今天向大家宣佈的消息。

This is Blackwell.

我是布萊克威爾

This is the platform.

這就是平臺。

Amazing processors.

令人驚歎的處理器

NVLink switches.

NVLink 交換機

Networking systems.

網絡系統。

And the system design is a miracle.

系統設計堪稱奇蹟。

This is Blackwell.

我是布萊克威爾

And this to me is what a GPU looks like in my mind.

這就是我心目中 GPU 的樣子。