Q4 2023 NVIDIA Corp Earnings Call

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Operator: Good afternoon. My name is Emma, and I will be your conference operator today.

At this time I would like to welcome everyone to the Nvidia's fourth quarter earnings call. All lines have been placed on mute to prevent any background noise.

All lines have been placed on mute to prevent any background noise.

After the speakers' remarks, there will be a question and answer session. If you'd like to ask a question during this time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, again press the star one. Thank you.

If you'd like to ask a question. During this time simply press star followed by the number one on your telephone keypad.

If you would like to withdraw your question again press the star one thank you.

Simona Jankowski you may begin your conference.

Simona Jankowski: Thank you. Good afternoon everyone and welcome to Nvidia's Conference call for the fourth quarter of fiscal 2023. With me today from Nvidia are Jensen Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer.

<unk> are Jensen, Huang President and Chief Executive Officer, and Colette, Kress Executive Vice President and Chief Financial Officer.

I'd like to remind you that our call is being webcast live on Nvidia's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2024.

A webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2024.

The contents of today's call is Nvidia's property, it can't be reproduced or transcribed without our prior written consent.

During this call we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially.

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K, and 10-Q and the reports that we may file on form 8-K with the Securities and Exchange Commission. All our statements are made as of today February 22, 2023 based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette.

Colette Kress: Thank you Simona. Q4 revenue was 6.05 billion, up 2% sequentially [inaudible] year on year. Full year revenue was 27 billion, flat from the prior year.

1% year on year full year revenue was 27 billion flat from the prior year.

Starting with data center, revenue was $3.62 billion, was down 6% sequentially and up 11% year on year. Fiscal year revenue was $15 billion and up 41%.

Revenue was $3 62 billion was down 6% sequentially and up 11% year on year fiscal year revenue was $15 billion and up 41%.

Hyperscale customer revenue posted strong sequential growth. Fell short of our expectations as some cloud service providers paused at the end of the year to recalibrate their build plans.

So we generally see tightening that reflects overall macroeconomic uncertainty. We believe this is a timing issue at the end market demand for GPUs and AI infrastructure is strong.

Networking grew but a bit less than our expected on softer demand for general purpose CPU infrastructure.

The total data center's sequential revenue decline was driven by lower sales in China, which was largely in line with our expectations, reflecting COVID-19 and other domestic issues.

With cloud adoption continuing to grow, we are serving and expanding list of fast growing cloud service providers, including Oracle and GPU specialized CSPs.

Revenue growth from CSP customers last year significantly outpaced that of data center as a whole as more enterprise customers move to a cloud first approach.

On a trailing four quarter basis, CSP customers drove about 40% of our data center revenue.

Customers drove about 40% of our data center revenue.

Adoption of our new flagship H 100 datacenter GPU is strong. In just the second quarter of its ramp, H 100 revenue was already much higher than that of A 100, which declined sequentially. This is a testament of the exceptional performance on the H 100 which is as much as nine X faster than the A 100 for training and up 30X faster [inaudible] of transformer base large language models. The transformer engine of H 100 arrived just in time to serve the development and scale up of inference of large language models.

Just the second quarter of its ramp page 100 revenue was already much higher than that of a 100, which declined sequentially.

As a testament of the exceptional performance on the <unk> hundred which is as much as nine X faster than the E 100 for training and up 30.

Foster.

<unk> of transformer base large language models.

The transformer engine of H 100 arrived just in time to serve the development and scale up of inference of large language models.

AI adoption is at an inflection point. Open AI, Chat GPT has captured interest worldwide, allowing people to experience AI firsthand, showing what's possible with generative AI. These new type of neural network models can improve productivity and a wide range of tasks, whether generating texts like marketing copy, summarizing documents, creating images for ads or video games, for answering customer questions, generative  AI applications will help almost every industry do more faster.

<unk> an inflection point.

Open AI chat GBT is captured interest worldwide, allowing people to experience firsthand showing what's possible with generative AI.

These new type of neural network models can improve productivity and a wide range of tough weather generating texts like marketing copy summarizing documents.

Creating images for ads or video games for answering customer questions.

AI applications will help almost every industry do more faster.

Generative large language models with over 100 billion parameters are the most advanced neural networks in today's world and videos expertise spans across the supercomputers, algorithms, data processing, and training method that can bring these capabilities to enterprise. We look forward to helping customers with generative AI opportunities.

World and videos expertise spans across the supercomputers algorithms data processing and training method that can bring these capabilities to enterprise.

We look forward to helping customers with generative AI opportunities.

In addition to working with every major Hyperscale cloud provider, we are engaged with many consumer internet companies, enterprises, and startups. The opportunity is significant and driving strong growth in the data center that will accelerate through the year.

We will accelerate through the year.

During the quarter, we made notable announcements in the financial services sector, one of our largest industry view goals. We announced a partnership with Deutsche Bank to accelerate the use of AI and machine learning in financial services. Together, we are developing a range of applications, including virtual customer service agents, speech AI, fraud detection, and bank process automation leveraging Nvidia's full computing stock both on premise and in the cloud, including Nvidia's AI Enterprise software.

We announced a partnership with Deutsche Bank to accelerate the use of AI and machine learning in financial services. Together, we are developing a range of applications, including virtual customer service agents speech, AI fraud detection and bank process automation levering.

Leveraging and videos full computing stock both on premise and in the cloud, including Nvidia AI Enterprise software.

We also announced that Nvidia captured leading results for AI inference in a key financial services industry benchmark for applications such as asset price discovery.

A key financial services industry benchmark for applications, such as asset price discovery.

In networking, we see growing demand for our latest generation Infiniband and HPC optimized Ethernet platforms fueled by AI.

We we see growing demand for our latest generation infiniband.

PC optimized Ethernet platforms fueled by AI.

Generative AI Foundation model sizes continue to grow at exponential rates, driving the need for high performance networking to scale out multi-node accelerated workloads.

Delivering unmatched performance latency and in network computing capabilities, Infiniband is the clear choice for power efficient cloud scale generative AI.

For smaller scale of appointments, Nvidia's bringing its full accelerated stock expertise and integrating it with the world's most advanced high performance Ethernet fabrics.

In the quarter, Infiniband lowered our growth as our [inaudible] 40 Gigabit per second platform is off to a great start, driven by demand across cloud, enterprise, and supercomputing customers.

Gigabit per second platform is off to a great start driven by demand across cloud enterprise and supercomputing customers.

In Ethernet, our 40 gigabit per second spectrum four networking platform is gaining momentum as customers transition to higher speeds, next generation adapters, and switches.

First generation adapters and switches.

We remain focused on expanding our software and services, released version 3.0 of Nvidia AI enterprise with support for more than 50 Nvidia AI frameworks and pre-trained model and new workflows for contact center intelligent virtual assistant, audio transcription, and cyber security.

And cyber security.

Upcoming offerings include our Nemo and Bio Nemo large language model services, which are currently in early access with customers.

Now I'll turn to Jensen to talk a bit more about our software and cloud.

Two Johnson to talk a bit more about our software and cloud.

Jensen Huang: Thanks Colette. The cumulation of technology breakthroughs has brought AI to an inflection point. Generative AI's versatility and capability has triggered a sense of urgency at enterprises around the world to develop and deploy AI strategies. Yet the AI supercomputer infrastructure model algorithms data processing and training techniques remain an insurmountable obstacle for most.

Cumulation of technology breakthroughs has brought AI to an inflection point.

Generative ai's versatility and capability.

It has triggered a sense of urgency at enterprises around the world to develop and deploy AI strategies.

Yet the AI supercomputer infrastructure model algorithms data processing and training techniques remain an insurmountable obstacle for most.

AI supercomputer infrastructure model algorithms data processing and training techniques remain an insurmountable obstacle for most.

Today, I want to share with you the next level of our business model to help put AI within reach of every enterprise customer.

I want to share with you the next level of our business model.

To help put AI within reach of every enterprise customer.

We are partnering with major cloud service providers to offer Nvidia AI cloud services, offered directly by Nvidia, and through our network of go to market partners, and hosted within the world's largest clouds.

Offer Nvidia AI cloud services.

Offered directly by Nvidia.

And through our network of go to market partners.

And hosted within.

The worlds largest clouds.

Nvidia AI at the surface, offers enterprises easy access to the world's most advanced AI platform while remaining close to the storage, networking, security, and cloud services offered by the worlds most advanced clouds. Customers can engage Nvidia AI cloud services at the AI supercomputer, acceleration library software, or pre-trained AI models layers.

While remaining close to the storage networking security and cloud services offered by the worlds most advanced clouds.

Customers.

Can engage Nvidia AI cloud services.

At the AI supercomputer.

Acceleration library software.

Pre trained AI models layers.

Nvidia EGS is an AI supercomputer and the blueprint of AI factories being built around the world. AI supercomputers are hard and time consuming to build. Today, we are announcing the Nvidia EGS cloud, the fastest and easiest way to have your own EGS AI supercomputer, just open your browser. Nvidia EGS cloud is already available through Oracle cloud infrastructure, and Microsoft Azure, Google GTP, and others underway.

AI supercomputers are hard and time consuming to build.

Today, we are announcing the Nvidia <unk> cloud.

Fast and easiest way to have your own <unk> AI supercomputer.

Just open your browser.

And video <unk> cloud is already available through Oracle cloud infrastructure.

Microsoft Azure.

Google GTP.

And others underway.

At the AI platform software layer, customers can access Nvidia AI enterprise for training and deploying large language models or other AI workloads. And at the pre-trained generative AI model layer, we will be offering Nemo and Bio Nemo customizable AI models to enterprise customers who want to build proprietary generative AI models and services for their businesses.

Customers can access Nvidia AI enterprise.

Our training and deploying large language models or other AI workloads.

And at the pre trained generative AI model layer.

we will be offering Nemo and Bio Nemo customizable AI models to enterprise customers who want to build proprietary generative AI models and services for their businesses.

And bio Nemo customizable AI models.

Two enterprise customers, who want to build proprietary generative AI models and services for their businesses.

With our new business model, customers can engage Nvidia's full scale of AI computing across their private to any public cloud. We will share more details about Nvidia AI cloud services at our upcoming GTC, so be sure to tune in. Now, let me turn it back to Colette on gaming.

With our new business model customer.

Customers can engage and videos full scale of AI computing across their private.

Any public cloud.

We will share more details about Nvidia AI cloud services at our upcoming GTC, so be sure to tune in.

Now, let me turn it back to Colette on gaming.

Colette Kress: Thanks Jensen. Gaming revenue of $1.83 billion was up 16% sequentially and down 46% from a year ago. Fiscal year revenue of 9.07 billion is down 27%.

Gaming revenue of $1 83 billion was up 16% sequentially and down 46% from a year ago fiscal year revenue of 907 billion is down 27%.

Sequential growth was driven by the strong reception of our 40 series G Force [inaudible] GPUs based on the Ada Lovelace architecture.

The year on year decline reflects the impact of channel inventory correction, which is largely behind us.

And demand in the seasonally strong fourth quarter was solid in most regions. while China was somewhat impacted by disruption related to COVID, we are encouraged by the early signs of recovery in that market.

Gamers are responding enthusiastically to the new RTI 40-90, 40-80, 40-70 Ti desktop GPUs, with many retail and online outlets quickly selling out of stock.

<unk> thousand 90, 40, 80, 40, 70 Ti desktop Gpus.

With many retail and online outlets quickly selling out of stock.

The flagship RTX 40-90 has quickly shot up in popularity on stream claimed the top spot for the Ada architecture, reflecting gamers' desire for high performance graphics.

As quickly shot up in popularity on steam claimed the top spot for the Ada architecture, reflecting gamers desire for high performance graphics.

Earlier this month, the first wave of gaming laptops based on the Ada architecture reached retail shelves delivering Nvidia's largest ever generational leap in performance and power efficiency.

For the first time, we're bringing enthusiasts cloud GPU performance to laptops as slim as 14 inches, a fast growing segment previously limited to basic tasks and apps.

In another [inaudible], we are bringing the 90 cloud GPU, our most performance models to laptops, thanks to the power efficiency of our fifth generation Max Q technology.

All in RTX 40 series GPUs will power over 170 gaming and creator laptops, setting up for a great [inaudible].

<unk>.

There are now over 400 games and applications supporting Nvidia's RTX technology for real time, ray tracing and AI powered graphics. The Ada architecture features [inaudible] for third generation AI powered graphics can massively boost performance for the most advanced scanned [inaudible] pump 2077 recently added CLS three enabling a three to four extra boost and frame rate performance at 4K resolution.

advanced scanned [inaudible] pump 2077 recently added CLS three enabling a three to four extra boost and frame rate performance at 4K resolution.

Three enabling a three to four extra boost and frame rate performance at four K resolution.

Our GeForce Now cloud gaming service continues to expand in multiple dimensions, users, titles, and performance. It now has more than 25 million members in over 100 countries. Last month, it enabled RTX 40-80 graphics horsepower in the new high performance ultimate membership tiers. Ultimate members can stream at up to 240 frames per second from a cloud with full ray tracing and CLS SIII. And just yesterday, we made an important announcement with Microsoft. We agreed to a 10-year partnership to bring to GeForce Now Microsoft lineup of Xbox PC games, which include blockbusters like Minecraft, Halo, and Flight Simulator. And upon the close of Microsoft Activision acquisition, it will add titles like Call of Duty and Overwatch.

Now has more than 25 million members and over 100 countries.

Last month enabled RPX 40, 80 graphics horsepower and the new high performance Ultimate membership tiers.

Members can stream at up to 240 frames per second from a cloud with full ray tracing and CLSA III. And just yesterday, we made an important announcement with Microsoft We agreed to a 10 year partnership to bring to G Force now Microsoft lineup of Xbox PC games, which include blockbusters like Minecraft Halo and flight simulator. Upon the close of Microsoft Activision acquisition, it will add titles like call of duty and Overwatch.

And just yesterday, we made an important announcement with Microsoft We agreed to a 10 year partnership to bring to G Force now Microsoft lineup of Xbox PC games, which include blockbusters like Minecraft Halo and flight simulator. Upon the close of Microsoft Activision acquisition, it will add titles like call of duty and Overwatch.

Upon the close of Microsoft Activision acquisition, it will add titles like call of duty and Overwatch.

Moving to pro visualization, revenue of $226 million was up 13% sequentially and down 65% from a year ago. Fiscal year revenue of $1.54 billion was down 27%.

Revenue of $226 million was up 13% sequentially and down 65% from a year ago.

Fiscal year revenue of $1 $5 4 billion was down 27%.

Sequential growth was driven by desktop workstation with strength in the automotive and manufacturing industrial verticals.

The year on year decline reflects the impact of the channel inventory correction, which we expect to end in the first half of the year.

Interest in Nvidia's Omniverse continues to build with almost 300,000 downloads so far, 185 connectors to third party design applications. The latest release of Omniverse has a number of features and enhancements, including support for 4K real time path tracing Omniverse's search for AI powered search through large untapped 3D databases, and omni one cloud containers for AWS.

The latest release of <unk>. Number of features and enhancements, including support for four K real time path tracing. On University search for AI powered search through large untapped <unk> databases, and omni one cloud containers for AWS.

Number of features and enhancements, including support for four K real time path tracing.

On University search for AI powered search through large untapped <unk> databases, and omni one cloud containers for AWS.

Let's move to automotive. Revenue was a record $294 million, up 17% and up 135% from year ago.

Revenue was a record $294 million up 17%.

And up 135% from year ago.

Sequential growth was driven primarily by AI automotive solutions.

New program ramps at both electric vehicle and traditional OEM customers helped drive this growth.

Fiscal year revenue of $903 million was up 60%.

At CES, we announced a strategic partnership with Fox Com to develop automated an autonomous vehicle platforms. This partnership will provide scale for volume manufacturing to meet growing demand for the Nvidia drive platform.

Fox Com will use Nvidia drive Hyperion compute and sensor architecture for its electric vehicles. Fox Com will be a tier one manufacturer producing electronic control units based on the Nvidia drive [inaudible].

<unk> will be a tier one manufacturer producing electronic control units based on the Nvidia drive Oren.

Global. Yes.

Yes.

We also reached an important milestone this quarter. The Nvidia drive operating system received safety certification from Q2, one of the most experienced and rigorous assessment bodies in the automotive industry. With industry-leading performance and functional safety, our platform meets our higher standards required for autonomous transportation.

Nvidia drive operating system received safety certification from two to one of the most experienced and rigorous assessment bodies in the automotive industry.

With industry, leading performance and functional safety our platform meets our higher standards required for autonomous transportation.

Moving to the rest of the P&L, GAAP gross margin was 63.3% and non-GAAP gross margin was 66.1%. Fiscal year GAAP gross margin was 56.9 and non-GAAP gross margin was 59.2.

Gross margin was 63, 3% and non-GAAP gross margin was 66, 1% fiscal year GAAP gross margin was $56 nine.

non-GAAP gross margin was $59 two.

Year on year Q4 GAAP operating expenses were up 21% and non-GAAP operating expenses were up 23%, primarily due to the higher compensation and data center infrastructure expenses.

Sequentially, GAAP operating expenses were flat and non-GAAP operating expenses were down 1%. We plan to keep them relatively flat at this level over the coming quarters.

Plan to keep them relatively flat at this levels over the coming quarters.

Full year GAAP operating expenses were up 15% and non-GAAP operating expenses were up 31%.

We returned $1.15 billion to shareholders in the form of share repurchases and cash dividends. At the end of Q4, we had approximately $7 billion remaining under our share repurchase authorization through December 2023.

At the end of Q4, we had approximately $7 billion remaining under our share repurchase authorization through December 2023.

Let me look to the outlook for the first quarter of fiscal '24.

We expect sequential growth to be driven by each of our four major market platforms, led by strong growth in datacenter and gaming. Revenue is expected to be $6.5 billion plus or minus 2%.

Led by strong growth in datacenter and gaming.

Revenue is expected to be $6 5 billion plus or minus 2%.

GAAP and non-GAAP gross margins are expected to be 64.1% and 66.5%, respectively, plus or minus 50 basis points.

GAAP operating expenses are expected to be approximately 2.53 billion. Non-GAAP operating expenses are expected to be approximately 1.78 billion.

Non-GAAP operating expenses are expected to be approximately 1.78 billion.

GAAP and non-GAAP other income and expenses are expected to be an income of approximately $60 million, excluding gains and losses of non affiliated investments.

GAAP and non-GAAP tax rates are expected to be 13% plus or minus 1%, excluding any discrete items.

Capital expenditures are expected to be approximately $350 million to $400 million for the first quarter, and in the range of $1.1 to $1.3 billion for the full fiscal year 2024.

And in the range of $1, one to $1 3 billion for the full fiscal year 2024.

Further financial details are included in the CFO commentary and other information available on our IR website.

In closing, let me highlight upcoming events for the financial community. We will be attending the Morgan Stanley Technology Conference on March 6th in San Francisco and the Cowen Healthcare Conference on March 7th in Boston. We will also host GTC virtually with Jetsons keynote kicking off on March 21st. Our earnings call to discuss the results of our first quarter of fiscal year '24 is scheduled for Wednesday May 24th.

On March 21.

Our earnings call to discuss the results of our first quarter of fiscal year 'twenty. Four is scheduled for Wednesday may 24.

Now we will open up the call for questions. Operator, would you please poll for questions.

Operator: At this time, I would like to remind everyone in order to ask a question, press star then the number one on your telephone keypad. As a reminder, please limit yourself to one question. We'll pause for just a moment to compile the Q&A roster.

As a reminder, please limit yourself to one question.

Pause for just a moment to compile the Q&A roster.

Your first question comes from the line of Aaron Rakers with Wells Fargo. Your line is now open.

Aaron Christopher Rakers: Yeah, thanks for taking the question. Clearly on this call, a key focal point is going to be the monetization of fact of your software and cloud strategy. I think as we look at it, I think straight up the enterprise AI software suite is priced at around $6000 per CPU socket. I think you've got pricing metrics a little bit higher for the cloud consumption model. I'm just curious Colette how do we start to think about that monetization contribution to the company's business model over the next couple of quarters relative to I think in the past you've talked to like a couple of hundred million dollars or so? Just curious if you could unpack that a little bit.

On this call key focal point is going to be the monetization of fact of your software and cloud strategy I think so.

We look at it I think straight up the enterprise AI software suite <unk> priced at around $6000 per CPU socket.

I think you've got pricing metrics, a little bit higher for the cloud consumption model I'm. Just curious how do we start to think about that monetization contribution to the company's business model over the next couple of quarters relative to I think in the past you've talked to like a couple of hundred million dollars or so.

Just curious if you could unpack that a little bit.

Colette Kress: So I'll start and turn it over to Jensen to talk more because I believe this will be a great topic of discussion also with our GTC.

Great topic of discussion also with our GTC.

Our plans in terms of our software, we continue to see growth. Even in our Q4 results, we're making quite good progress in both working with our partners, onboarding more partners and increasing our software. You are correct, we've talked about our software revenues being in the one hundreds of millions, and we're getting even stronger each day as Q4 was probably a record level in terms of our software levels. But there's more to unpack in terms of there and I'm going to turn it to Jensen.

Even in our Q4 results, we're making quite good progress in both working with our partners Onboarding more partners and increasing our software <unk>. You are correct, we've talked about our software revenues being in the one hundreds of millions. And we're getting even stronger each day as Q4 was probably a record level in terms of our sulfur levels. But theres more to unpack in terms of there and I'm going to turn it to Johnson.

You are correct, we've talked about our software revenues being in the one hundreds of millions.

And we're getting even stronger each day as Q4 was probably a record level in terms of our sulfur levels.

But theres more to unpack in terms of there and I'm going to turn it to Johnson.

Jensen Huang: First of all, taking a step back, you know Nvidia AI is essentially the operating system of AI systems today. It starts from data processing to learning, training, to validation, to inference. And so this body of software is completely accelerated. It runs in every cloud, it runs on Prem, and it supports every framework, every model that we know of, and it's accelerated everywhere. By using Nvidia AI your entire machine learning operations is more efficient and it is more cost effective. You save money by using accelerated software.

AI systems today. From data processing. Two <unk>. <unk> training. Two validation to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

From data processing. Two <unk>. <unk> training. Two validation to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

Two <unk>. <unk> training. Two validation to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

<unk> training. Two validation to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

Two validation to inference. And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

And so this body of software is completely accelerated. It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

It runs in every cloud. It runs on Prem. It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

It runs on Prem. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software. And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

And it supports. Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

Every framework. Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

Every model that we know of. And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

And it's accelerated everywhere. By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

By using Nvidia AI your. And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

And tire machine learning operations is more efficient. And it is more cost effective. You save money by using accelerated software.

And it is more cost effective. You save money by using accelerated software.

You save money by using accelerated software.

Our announcement today of putting Nvidia's infrastructure and have it be hosted from within the world's leading cloud service providers accelerates the enterprise's ability to utilize Nvidia AI enterprise. It accelerates people's adoption of this machine learning pipeline, which is not for the faint of heart. It is a very extensive body of software, it is not deployed in enterprises broadly, but we believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI in enterprises. So we're excited about this new extended part of our business model, we really believe that it will accelerate the adoption of the software.

Announcement today are putting envious infrastructure and have it be hosted from within the worlds. Leading cloud service providers. A solid rates. The enterprise's ability to. To utilize Nvidia AI enterprise. It accelerates. Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

Leading cloud service providers.

A solid rates. The enterprise's ability to. To utilize Nvidia AI enterprise. It accelerates. Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

The enterprise's ability to. To utilize Nvidia AI enterprise. It accelerates. Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

To utilize Nvidia AI enterprise. It accelerates. Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

It accelerates. Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

Adoption of this machine learning pipeline, which is not for the faint apart. It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

It is a very extensive body of software. Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

Is not deployed in enterprises broadly. But. We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

But.

We believe that by hosting everything in the cloud from the infrastructure through the operating system software all the way through pre trained models, we can accelerate the adoption of generative AI and enterprises. We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

We're excited about this. This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

This new extended part of our business model, we really believe that it will accelerate the adoption of the software.

Operator: Your next question comes from the line of Vivek Arya with Bank of America. Your line is now open.

Vivek Arya: Thank you. Just wanted to clarify Colette if you meant datacenter could grow on a year on year basis also in Q1. And then Jensen, my main question kind of related to two small related ones. The computing intensity for generative AI, if it is very high then it limits the market size to just a handful of hyperscalers and on the other extreme, if the market gets very large then doesn't it attract more competition for Nvidia from cloud basics or other accelerator options that are out there in the market?

Computing and density for generator of AI. If it has very high does it limit the market size to just a handful of hyperscale. And on the other extreme if the market gets very large then doesn't attract more competition photo and video from cloud basics or other. Elevator options that are out there in the market.

And on the other extreme if the market gets very large then doesn't attract more competition photo and video from cloud basics or other. Elevator options that are out there in the market.

Elevator options that are out there in the market.

Colette Kress: Vivek, thanks for the question. First talking about our data center guidance that we provided for Q1, we do expect a sequential growth in terms of our data center, strong sequential growth, and we are also expecting a growth year over year for our data center. We expect a great year with our year over year in data center probably accelerating past Q1. 

Our guidance that we provided for Q1, we do expect a sequential.

Growth in terms of our data center strong sequential growth and we are also expecting a growth year over year. For our dental Harley Davidson. We expect a great year. With our year over year. In data center, probably accelerating past Q1. Okay.

For our dental Harley Davidson. We expect a great year. With our year over year. In data center, probably accelerating past Q1. Okay.

We expect a great year. With our year over year. In data center, probably accelerating past Q1. Okay.

With our year over year. In data center, probably accelerating past Q1. Okay.

In data center, probably accelerating past Q1.

Okay.

Jensen Huang: Arch language models are called large because they are quite large. However, remember that we've accelerated and advanced AI processing by a million X over the last decade. Moore's law in its best days would have delivered 100X in a decade by coming up with new processors, new systems, new interconnects, new frameworks with algorithms, and working with data scientists, AI researchers on new models, across that entire span we've made large language module processing a million times faster, a million times faster. What would have taken a couple of months in the beginning, now happens in about 10 days. And of course, you still need a large infrastructure, and given the large infrastructure, we're introducing hopper, which is a transformer engine, it's new envy linked switches, and it's new Infiniband 400 Gigabits per second data rates, we are able to take another leap in the processing of large language models. And so I think by putting Nvidia's DGX supercomputers into the cloud with Nvidia DGX cloud, we're going to democratize the access of this infrastructure with accelerated training capabilities really make this technology and this capability quite accessible. So that's one one thought.

Arch language models are called large because they are quite large.

However.

Remember that we've accelerated and advanced.

AI processing by a million X over the last decade.

Moore's law and its best days would have delivered 100 X in a decade.

By coming up with new processors, new systems, New Interconnects.

New.

Frameworks algorithms.

And working with.

Data scientists AI researchers on new models. Across that entire spanned we've made large language module processing. Million times faster. One 1 million times faster. It would have taken. A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Across that entire spanned we've made large language module processing. Million times faster. One 1 million times faster. It would have taken. A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Million times faster. One 1 million times faster. It would have taken. A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

One 1 million times faster. It would have taken. A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

It would have taken. A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

A couple of months in the beginning. Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Now. It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

It happens in about 10 days. And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

And. Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Of course, you still need a large infrastructure. And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

And <unk>. Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Given the large infrastructure, we're introducing hopper, which whether it's transformer engine. It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

It's new envy linked switches. And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

And it's new Infiniband 400, Gigabits per second. And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

And data rates. We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

We are able to. Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Take another leap. The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

The processing of large language models. And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

And so I think by putting in videos D. G X. Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

Supercomputers into the cloud with Nvidia digits cloud, we're going to democratize. The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

The access of this infrastructure and. With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

With accelerated training capabilities really make this technology and this capability quite accessible. So thats one one thought.

So thats one one thought.

The second is the number of large language models or foundation models that have to be developed is quite large. Different countries with different cultures and its body of knowledge are different, different fields, different domains whether it's imaging or its biology, or it's physics, each one of them need their own domain foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

Different countries with different cultures and its body of knowledge are different. Different fields different domains. There is imaging. Or its biology. It's physics. Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

Different fields different domains. There is imaging. Or its biology. It's physics. Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

There is imaging. Or its biology. It's physics. Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

Or its biology. It's physics. Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

It's physics. Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

Each one of them meet their own domain. Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

Foundation models. With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

With large language models of course, we now have a prior that could be used to accelerate the development of all of these other fields, which is really quite exciting.

The other thing to remember is that the number of companies in the world have their own proprietary data. The most valuable data in the world are proprietary and they belong to the company, it's inside their company, it will never leave the company, and that body of data will also be harnessed to train new AI models for the very first time. And so our strategy and our goal is to put the DGX infrastructure in the cloud so that we can make this capability available to every enterprise, every company in the world, who would like to create proprietary models.

<unk> proprietary. And they belong to the company it's inside their company it will never lead the company and that body of data. Also be. Harnessed to train new AI models for the very first time. And so we are. Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

And they belong to the company it's inside their company it will never lead the company and that body of data. Also be. Harnessed to train new AI models for the very first time. And so we are. Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

Also be. Harnessed to train new AI models for the very first time. And so we are. Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

Harnessed to train new AI models for the very first time. And so we are. Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

And so we are. Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

Our strategy and our goal is to put the <unk> infrastructure in the cloud. So that we can make this capability available to every enterprise every company in the world, who would like to create proprietary data and so proprietary models.

The second thing about competition, we've had competition for a long time. Our approach, our computing architecture as you know is quite different on several dimensions. Number one, it is universal, meaning you could use it for training, you can use it for inference, you could use it for models of all different types. It supports every framework, it supports every cloud, it's everywhere. It's cloud to private cloud, cloud to on Prem, it's all the way out to the edge. It could be an autonomous system. This one architecture allows developers to develop their AI models and deploy it everywhere.

Our our. Our approach our computing architecture as you know is quite different on several. Dimensions number one. It is universal meaning you could use it for training you can use it for inference you could use it for models of all different types. It supports every framework. It supports every cloud it's everywhere. <unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

Our approach our computing architecture as you know is quite different on several. Dimensions number one. It is universal meaning you could use it for training you can use it for inference you could use it for models of all different types. It supports every framework. It supports every cloud it's everywhere. <unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

Dimensions number one. It is universal meaning you could use it for training you can use it for inference you could use it for models of all different types. It supports every framework. It supports every cloud it's everywhere. <unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

It is universal meaning you could use it for training you can use it for inference you could use it for models of all different types. It supports every framework. It supports every cloud it's everywhere. <unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

It supports every cloud it's everywhere. <unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

<unk> cloud to private cloud cloud to on Prem, It's all the way out to the edge. It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

It could be an autonomous system. This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

This one architecture. Allows developers to develop there. Models and deploy it everywhere the <unk>.

Allows developers to develop there. Models and deploy it everywhere the <unk>.

Models and deploy it everywhere the <unk>.

The second very large idea is that no AI in itself is an application. There is a pre processing part of it and a post processing part of it to turn it into an application or a service. Most people don't talk about the pre and post processing, because they're maybe not as sexy and not as interesting. However, it turns out that pre processing and post processing oftentime consumes half or two thirds of the overall workload. And so by accelerating the entire end to end pipeline, from pre processing, from data ingestion and data processing all the way to the pre processing, all the way to post processing, we're able to accelerate the entire pipeline versus just accelerating top of the pipeline. The limit to speed up even if your instantly passed if you only accelerate half of the workload is twice as fast whereas if you accelerated the entire workload you could accelerate the workload, maybe 10, 20, 50 times faster, which is the reason why when you hear about Nvidia accelerating applications you routinely hear 10X, 20X, 50X speedup and the reason for that is because we accelerate things end to end, not just the deep learning part of it but using [inaudible] to accelerate everything from [inaudible].

No AI in itself is an application there is a pre processing part of it in a post processing part of it to turn it into a application or service. Most people. Don't talk about the pre and post processing, because maybe not as sexy and not as interesting. However, it turns out the pre processing and post processing Oftentime consumes. Half or two thirds of the overall workload.

Most people. Don't talk about the pre and post processing, because maybe not as sexy and not as interesting. However, it turns out the pre processing and post processing Oftentime consumes. Half or two thirds of the overall workload.

Don't talk about the pre and post processing, because maybe not as sexy and not as interesting. However, it turns out the pre processing and post processing Oftentime consumes. Half or two thirds of the overall workload.

Half or two thirds of the overall workload.

And so by accelerating the entire end to end pipeline from pre processing from data ingestion and data processing all the way to the pre processing all the way to post processing, we're able to accelerate the entire pipeline versus just accelerating top of the pipeline. The limit. Two. Speed up even if your instantly passed if you only accelerate half of the workload is twice as fast. As if you accelerated the entire workload you could accelerate the workload, maybe 10 2050 times faster, which is the reason why when you hear about Nvidia accelerating applications. Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

The limit. Two. Speed up even if your instantly passed if you only accelerate half of the workload is twice as fast. As if you accelerated the entire workload you could accelerate the workload, maybe 10 2050 times faster, which is the reason why when you hear about Nvidia accelerating applications. Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

Two. Speed up even if your instantly passed if you only accelerate half of the workload is twice as fast. As if you accelerated the entire workload you could accelerate the workload, maybe 10 2050 times faster, which is the reason why when you hear about Nvidia accelerating applications. Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

Speed up even if your instantly passed if you only accelerate half of the workload is twice as fast. As if you accelerated the entire workload you could accelerate the workload, maybe 10 2050 times faster, which is the reason why when you hear about Nvidia accelerating applications. Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

As if you accelerated the entire workload you could accelerate the workload, maybe 10 2050 times faster, which is the reason why when you hear about Nvidia accelerating applications. Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

Routinely here. <unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

<unk> 'twenty X 50, speedup and the reason for that is because we accelerate things end to end not just the deep learning part of it but using cuda to accelerate everything from <unk>.

And so I think the universality of our accelerated computing platform, the fact that we're in every cloud, the fact there we're from cloud to edge, it makes our architecture really quite accessible and very differentiated in this way and most importantly to all the service providers because the utilization is so high, because you can use it to accelerate the end to end workload and get such good throughput, our architecture is the lowest operating cost, the comparison is not even close. So those are the two answers.

Computing accelerated computing platform. Fact that we're in every cloud. The fact, there were from cloud to edge. It makes our architecture really quite accessible and very differentiated in this way and most importantly to all the service providers. Because of the utilization. Utilization is so high because you can use it to accelerate the end to end workload and get such good throughput. Our architecture is the lowest operating cost. It's not. The comparison is not even close. Hi. Those are the two answers.

Fact that we're in every cloud. The fact, there were from cloud to edge. It makes our architecture really quite accessible and very differentiated in this way and most importantly to all the service providers. Because of the utilization. Utilization is so high because you can use it to accelerate the end to end workload and get such good throughput. Our architecture is the lowest operating cost. It's not. The comparison is not even close. Hi. Those are the two answers.

Because of the utilization. Utilization is so high because you can use it to accelerate the end to end workload and get such good throughput. Our architecture is the lowest operating cost. It's not. The comparison is not even close. Hi. Those are the two answers.

Utilization is so high because you can use it to accelerate the end to end workload and get such good throughput. Our architecture is the lowest operating cost. It's not. The comparison is not even close. Hi. Those are the two answers.

It's not. The comparison is not even close. Hi. Those are the two answers.

The comparison is not even close. Hi. Those are the two answers.

Hi. Those are the two answers.

Those are the two answers.

Operator: Your next question comes from the line of CJ Muse with Evercore. Your line is now open.

Your line is now open.

CJ Muse: Yeah, good afternoon. Thank you for taking the question. I guess Jensen you talked about Chat GPT as an inflection point kind of like the iPhone so curious part A, how have your conversations evolved post Chat GPT with hyperscale and large scale enterprises? And then secondly, as you think about hopper with the [inaudible] in Greece with high bandwidth memory, how has kind of your outlook for growth for those two product cycles evolved in the last few months? Thanks so much.

I guess you had some you talked about <unk> as an inflection point kind of what the.

So curious.

How have your conversations evolved.

Post Chuck GPT with Hyperscale and large scale enterprises, and then secondly, as you think about hopper with the trades.

In Greece with high bandwidth memory.

<unk> been kind of your outlook for growth for those two product cycles evolved.

The last few months thanks, so much.

Jensen Huang: Chat GPT is a wonderful piece of work and the team did a great job, Open AI did a great job with it. They stuck with it and the accumulation of all of the the breakthroughs led to a service with the modeling side that surprised everybody with its versatility and its capability.

Chad GPT.

As a wonderful piece of work and the team did a great job openings did a great job with it.

Stuck with it and.

The accumulation of all of the the breakthroughs led to.

A.

Our service with the modeling side that surprised everybody with its versatility and its capability.

What people people were surprised by, and this is an art and close within the industry is well understood, but the surprising capability of a single AI model that can perform tasks and skills that it was never trained to do. And for this language model to not just speak English or can translate of course, but not just speak human language, it can be prompted in human language, but output Python, output [inaudible], a language that very few people even remember, output Python for Blender, a 3D program. So it's a program that writes a program for another program.

And this is this is an art and.

Close within the industry is well understood, but the surprising capability.

Single AI models that can perform tasks and skills that it was never trained to do.

And for this language model to not just speak English or <unk>.

Can translate of course, but not just speak human language. It can be prompted in human language, but output Python output.

<unk> a language that very few people even remember output.

Python for Blender, a three D program. So it's a program that writes a program for another program.

The world now realizes that maybe human language is a perfectly good computer programming language and we've democratize computer programming for everyone, almost anyone who could explain in human language a particular task to be performed by this new computer. When I say new era of computing, this new computing platform, this new computer could take whatever your prompt is, whatever your human explained request is and translate it to a sequence of instructions that he processes directly or it waits for you to decide whether you want to process it or not. And so this this type of computer is utterly revolutionary in its application because it's democratize programming to so many people really has excited enterprises all over the world.

The World now realizes that maybe human language is a perfectly good computer programming language and we democratize computer programming for everyone almost anyone who could <unk>.

Explain in human language, a particular test to be performed. New computer.

New computer.

When I say new era of computing this new computing platform. This new computer could take whatever year prompted wherever your human explained requested and translated to a sequence of instructions that he processes directly.

Wait for you to decide whether you want to process it or not.

And so this this type of computer is.

utterly revolutionary in its application because it's democratize programming to so many people really has excited enterprises all over the world. Every single CSP, every single internet service provider, and frankly every single software company because of what I just explained that this is a AI model that can write a program for any program. Because of that reason, everybody who developed software is either alerted or shocked into alert, or actively working on something that is like Chat GPT to be integrated into their application or integrated into their service and so this is as you can imagine utterly worldwide. The activity around around the AI infrastructures that we build hopper and the activity around inferencing, using hopper and ampere to influence large language models has just gone through the roof in the last 60 days. So there is no question that whatever our views are of this year as we enter the year has been fairly dramatically changed as a result of the last 60-90 days.

utterly revolutionary in its application because it's democratize programming to so many people really has excited enterprises all over the world.

Everything every single CSP every single Internet service provider and frankly every single software company because of what I. Just explained that this is a AI model that can write a program for any program.

Every single CSP, every single internet service provider, and frankly every single software company because of what I just explained that this is a AI model that can write a program for any program. Because of that reason, everybody who developed software is either alerted or shocked into alert, or actively working on something that is like Chat GPT to be integrated into their application or integrated into their service and so this is as you can imagine utterly worldwide. The activity around around the AI infrastructures that we build hopper and the activity around inferencing, using hopper and ampere to influence large language models has just gone through the roof in the last 60 days. So there is no question that whatever our views are of this year as we enter the year has been fairly dramatically changed as a result of the last 60-90 days.

Because of that reason.

Everybody who developed software.

Is either alerted or shocked into alert.

Actively working.

On something that has live chat GTT to be integrated into their application or integrated into their service and so this is as you can imagine utterly worldwide.

The activity around around the AI.

AI infrastructures that we build hopper.

And the activity around inferencing, using hopper and peer to influence large language models has just gone through the roof in the last 60 days.

So there is no question that that whatever our views are of this year as. As we enter the year has been. Fairly dramatically changed as a result of the last 60 90 days.

As we enter the year has been.

Fairly dramatically changed as a result of the last 60 90 days.

Operator: Your next question comes from the line of Matt Ramsey with Cowen & Company. Your line is now open.

Your line is now open.

Matt Ramsay: Thank you very much. Good afternoon. Jensen, I wanted to ask a couple of questions on the DGX cloud and I guess, we're all talking about the drivers of the services and the compute that you're going to host on top of these services with a different hyperscalers. But I think we've been kind of watching and wondering when your data center business might transition to more of a systems level business, meaning pairing MD link and Infiniband with your hopper product, with a great product and selling things more on a systems level. I am wondering if you could step back and over the next two or three years, how do you think the mix of business in your data Center segment evolves from maybe selling cards to systems and software and what can that mean for the margins of that business over time? Thank you.

Jensen I wanted to ask a cup.

A question on the <unk> cloud and I guess, we're all talking about the drivers.

Our services and the compute that youre going to hold.

Post on top of these services with a different hyperscale or spot.

I think we've been kind. Kind of watching and wondering when your data center business might transition to more of a systems level business, meaning. Pairing MD link and its been a bandwidth youre hopper product with a great product and selling things. We're on a systems level. I am wondering if you could step back and over the next two or three years. How do you think the mix of business in your data Center segment evolves from for maybe selling cards too. <unk> and software and what can that mean for the margins of that business over time. Thank you.

Kind of watching and wondering when your data center business might transition to more of a systems level business, meaning. Pairing MD link and its been a bandwidth youre hopper product with a great product and selling things. We're on a systems level. I am wondering if you could step back and over the next two or three years. How do you think the mix of business in your data Center segment evolves from for maybe selling cards too. <unk> and software and what can that mean for the margins of that business over time. Thank you.

Pairing MD link and its been a bandwidth youre hopper product with a great product and selling things. We're on a systems level. I am wondering if you could step back and over the next two or three years. How do you think the mix of business in your data Center segment evolves from for maybe selling cards too. <unk> and software and what can that mean for the margins of that business over time. Thank you.

We're on a systems level. I am wondering if you could step back and over the next two or three years. How do you think the mix of business in your data Center segment evolves from for maybe selling cards too. <unk> and software and what can that mean for the margins of that business over time. Thank you.

I am wondering if you could step back and over the next two or three years. How do you think the mix of business in your data Center segment evolves from for maybe selling cards too. <unk> and software and what can that mean for the margins of that business over time. Thank you.

<unk> and software and what can that mean for the margins of that business over time. Thank you.

Jensen Huang: I appreciate the question. First of all, as you know, our data center business  is a GPU business only in the context of a conceptual GPU. Because what we actually sell to the cloud service providers is a fairly large computing panel of eight hoppers or eight amperes that is connected with MD linked switches that are connected with MD link. And so this board represents essentially one GPU. It's eight ships connected together into one GPU with a very high speed chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time, and that is one GPU. So when we think about the GPU, we actually think about an HGX GPU and that's A GPU. We're going to continue to do that.

As you know our data center business as is.

Our GPU business only in the context.

The conceptual GPU.

Because what we actually sell to the cloud service providers is a.

A panel of fairly large computing panel eight.

Hoppers or eight amperes.

That is connected with <unk>. Can be linked switches that are connected with MD link and. And so this this. Board represents essentially one GPU. It's eight ships connected together into one GPU with a very high speed. Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

Can be linked switches that are connected with MD link and. And so this this. Board represents essentially one GPU. It's eight ships connected together into one GPU with a very high speed. Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

And so this this. Board represents essentially one GPU. It's eight ships connected together into one GPU with a very high speed. Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

Board represents essentially one GPU. It's eight ships connected together into one GPU with a very high speed. Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

It's eight ships connected together into one GPU with a very high speed. Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

Chip to chip interconnect. And so we've been working on if you will multi die computers for quite some time.

And so we've been working on if you will multi die computers for quite some time.

And that is one GPU. So when we think about the GP, we actually think about it <unk> GPU and Thats <unk>. We're going to continue to do that and and.

So when we think about the GP, we actually think about it <unk> GPU and Thats <unk>.

We're going to continue to do that and and.

And the thing that the cloud service providers are really excited about is by hosting our infrastructure for Nvidia to offer because we have so many companies that we worked directly with. We're working directly with 10,000 AI startups around the world, with enterprises in every industry, and all of those relationships today would really love to be able to deploy both into the cloud at least or into the cloud and on Prem and oftentimes multi cloud. And so by having Nvidia DGX and Nvidia's infrastructure, our full stack in their cloud we're effectively attracting customers to the CSPs.

10000, AI startups around the world.

With enterprises in every industry. And all of those all of those relationships today.

And all of those all of those relationships today.

Today would really love to be able to deploy both.

Into the cloud at least or into the cloud and on Prem and oftentimes multi cloud and <unk>.

So by having Nvidia <unk> and video infrastructure, our full stack in their cloud where effectively attracting customers to the CSP.

This is a very, very exciting model for them and they welcomed us with open arms, and we are we're going to be the best AI salespeople for the world's clouds. And for the customers, they now have an instantaneous infrastructure that is the most advanced, they have a team of people who are extremely good from the infrastructure to the acceleration software, the Nvidia AI open operating system, all the way up to AI models. Within one entity they have access to expertise across an entire span. And so this is a great model for customers, it's a great model for CSPs, it's a great model for us. It lets us really run like the wind. As much as we we will continue and continue to advance DGX AI supercomputers, it does take time to build AI supercomputers on Prem. It's hard no matter how you look at it, it takes time no matter how you look at it, and so now we have the ability to really pre sketch a lot of that and get customers up and running as fast as possible.

The welcomed us with open arms and. We are we're going to be the best AI salespeople for the world's clouds. And for. The customers. They now have an instantaneous infrastructure that is the most advanced. They have a team of people who are extremely good from the infrastructure to the soft the acceleration software the Nvidia AI open. Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

We are we're going to be the best AI salespeople for the world's clouds. And for. The customers. They now have an instantaneous infrastructure that is the most advanced. They have a team of people who are extremely good from the infrastructure to the soft the acceleration software the Nvidia AI open. Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

And for. The customers. They now have an instantaneous infrastructure that is the most advanced. They have a team of people who are extremely good from the infrastructure to the soft the acceleration software the Nvidia AI open. Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

The customers. They now have an instantaneous infrastructure that is the most advanced. They have a team of people who are extremely good from the infrastructure to the soft the acceleration software the Nvidia AI open. Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

They have a team of people who are extremely good from the infrastructure to the soft the acceleration software the Nvidia AI open. Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Operating system, all the way up to AI models. Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Then one entity they have access to expertise across an entire span. And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

And so so this is a great. Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Model for customers is a great model for CSP. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible. Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Model for us. Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Lets us really run like to win as much as we we will continue and continue to advance <unk> AI supercomputers. It does take time to build AI supercomputers on Prem it's hard no matter, how you look at it. It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

It takes time no matter, how you look at it and so now we have the ability to really pre search a lot of that and. Get customers up and running as fast as possible.

Get customers up and running as fast as possible.

Operator: Your next question comes from the line of Timothy Arcuri with UBS. Your line is now open.

Timothy Arcuri with UBS.

Your line is now open thanks a lot.

Timothy Arcuri: Thanks a lot. Jensen, I have a question about what this all does to your Tam. Most of the focus right now is on techs, but obviously there are companies doing a lot of trading on video and music there, working on models there, and it seems like somebody who is training these big models has maybe on the high end at least 10,000 GPUs in the cloud that they've contracted and maybe tens of thousands of more to influence a widely deployed model. So it seems like the incremental Tam is easily in the several hundred thousands of GPUs and easily in the tens of billions of dollars, but I'm kind of wondering what this does to the Tam numbers you gave last year? I think you said 300 billion hardware Tam and $300 billion software Tam, so how do you kind of think about what the new Tam would be? Thanks.

Most of the focus right now is on tax, but obviously there are companies doing a lot of trading on video and.

Music there working on models, there and it seems like somebody Who's training. These big models has maybe on the high end at least 10000 Gpus in the cloud that they've contracted and maybe tens of thousands of more to <unk>.

<unk> widely deployed model. So it seems like the incremental Tam is easily in the several hundred thousands of Gpus and easily in the tens of billions of dollars, but I'm kind of wondering what this does to the Tam numbers. You gave last year. I think you said 300 billion hardware Tam in $300 billion software Tam. So how do you kind of think about what the new Tam would.

Jensen Huang: I think those numbers are really good anchors still. The difference is because of the--if you will--incredible capabilities and versatility of generative AI and all of the converging breakthroughs that happened towards the middle and the end of last year, we're probably going to arrive at that Tam sooner than later. There is no question that this is a very big moment for the computer industry. Every single platform change, every inflection point in the way that people develop computers happened because it was easier to use, easier to program, and more accessible this.

I think those numbers are really good good anchors still.

The difference is because of the. If you will incredible capabilities and versatility of generative AI.

If you will incredible capabilities and versatility of generative AI.

And all of the all of the converging breakthroughs that happened towards the middle and the end of last year.

We're probably.

Going to arrive at that Tam sooner than later.

There is no question that this is a very big moment for the computer industry.

Every single every single.

Platform change.

Every inflection point.

The way that people develop computers happened because it was easier to use easier to program and more accessible this.

This happened with the PC Revolution, this happened with the Internet Revolution, this happened with mobile cloud. Remember mobile cloud because of the iPhone and the App store, 5 million applications and counting emerged, but there weren't 5 million mainframe applications, there weren't $5 million workstation applications, they weren't 5 million PC applications. And because it was so easy to develop and deploy amazing applications part cloud, part on a mobile device, so easy to distribute because of App stores, the same exact thing is no happening to AI.

But there werent, there werent 5 million mainframe applications, there werent $5 million.

Workstation applications that Werent 5 million PC applications.

And because it was so easy to develop and deploy amazing applications part cloud part on a mobile device.

So easy to distribute because of App stores.

In the computing era, one computing platform, Chat GPT reached 150 million people in 60 to 90 days. I mean this is quite an extraordinary thing when people were using it to create all kinds of things. And so I think that what you're seeing now is just a torrent of new companies and new applications that are emerging. There is no question that in every way there's a new computing era. And so I think the Tam that we explained and expressed it really is even more realizable today and sooner than before.

Computing era did one computing platform chat GPT reached 150 million people in 60 to 90 days.

I mean this is quite an extraordinary thing when people were using it to create all kinds of things.

And so I think that what's your what youre seeing now.

Just a torrent of.

New companies and new applications that are emerging there is no question does in every way a.

New computing era, and so I think the.

The Tam that we explained expressed.

It really is even more realizable today in sooner than before.

Operator: Your next question comes from the line of Stacy Rasgon with Bernstein. Your line is now open.

Stacy Aaron Rasgon: Hi guys, thanks for taking my questions. I have a clarification and then a question both for Colette. The clarification, you said H 100 hundred revenues higher than A 100, was that an overall statement or was that at the same point in time like after two quarters of shipments?

A clarification and then a question both for collect.

The clarification, you said <unk> hundred revenues higher than a 100 was that an overall statement or was that at the same point in time like after two quarters of shipments.

And then for my actual question, I wanted to ask about auto specifically, the Mercedes opportunity. Mercedes had an event today and they were talking about software revenues for their MB drive there could be single digit, low low billion euros by mid decade, and mid billion euros by the end of the decade. And I know you guys were supposedly splitting software revenues 50/50, is that kind of the order of magnitude of software revenues from the Mercedes deal that you guys are thinking about over that similar timeframe and is that how we should be modeling that?

I wanted to ask about auto specifically, the Mercedes opportunity that Mercedes had an event today.

And they were talking about software revenues.

They're MB drive there could be no single digit low low low billion euros by mid decade, and mid billion euros by the end of the decade and I know you guys were supposedly splitting software revenues 50, 50 is that kind of the order of magnitude of software revenues from the Mercedes deal that you guys are thinking out over that similar timeframe.

Is that how we should be modeling that.

Colette Kress: Great, thank you for the question. Let me first start with your question you have about H 100 and A 100. We began our initial shipments of H 100 back in Q3. It was great start, many of them began that process many quarters ago and this was a time for us to get production level to them in Q3. So Q4 was an important time for us to see a great ramp of H 100 that we saw. What that means is our H 100 was the focus of many of our [inaudible] within Q4, and they were all wanting to both get up and running in the cloud instances and so we actually saw less of A 100 in Q4 of what we saw in H 100 in a larger amount. We tend to continue to sell both architectures going forward, but just in Q4 it was a strong quarter for H 100.

Great. Thanks. Thank you for the question. Let me first start with your question you had about 800 and a 100 <unk>.

We began our niche.

Shipments of <unk> hundred back in Q3.

<unk>.

A great start many of them began that process many quarters ago and this was a time for us to get production level to them in Q3.

Q4 was an important time for us to see a great brand per day to 100.

For that we saw what that means is our eight 100 was the focus of many of our SaaS peers within Q4, and they were all wanting to get.

It up and running in the cloud instances and so we actually saw less of a 100 in Q4.

What we saw in each 108, a larger amount.

We tend to continue to sell both architectures going forward, but just in Q4. It was a strong quarter for <unk>. Yes.

It was a strong quarter for <unk>. Yes.

Yes.

The additional questions that you had on Mercedes Benz, I'm very pleased with the joint connection that we have with them and we've been working very diligently at both getting ready to come to market. But you're right, they did talk about the software opportunity, they talked about the software opportunity in two phases about what they can drive as well as what they can also direct connect. They extend it out to a position of probably about 10 years looking at the opportunity that they see in front of us. So it aligns with what our thoughts are, with a long term partner of that and sharing that revenue over time.

Very pleased with the joint connection that we have with them and the work we've been.

Working very diligently at both getting ready to come to market, but you're right. They did talk about the software opportunity they talked about the software opportunity in two phases about what they can drive as well as what they can also direct connect.

They extend it out to a position of probably about 10 years looking at the opportunity that they see in front of us. So it aligns with what our thoughts are with a long term.

Partner of that ensuring that revenue over time.

Jensen Huang: One of the things that I'd like to add Stacy, something about the wisdom of what Mercedes is doing, this is the only large luxury brand that has across the board from the entry all the way to the highest end of their luxury cars to install every single one of them with a rich sensor set, every single one of them with an AI supercomputer so that every future car in the Mercedes fleet will contribute to an installed base that could be upgradable and forever renewed for customers going forward. If you could just imagine what it looks like if the entire Mercedes fleet that is on the road today were completely programmable that you can OTA, it would represent tens of millions of Mercedes that would represent revenue generating opportunity. And that's the vision that [inaudible] has and what they are building towards. I think it's going to be extraordinary the large installed base of luxury cars that we'll continue to renew for customers benefits and also for revenue generating benefits.

Luxury brand.

That has across the board from from every from the entry all the way to the highest end of their luxury cars.

To install every single one of them with a rich sensor set.

Every single one of them with an AI supercomputer.

So that so that.

Every every future car in the Mercedes fleet will contribute to an installed base that could be upgradable and forever renewed for customers going forward.

If you could just imagine.

What it looks like if the entire Mercedes fleet that is on the road today, we're completely programmable that you can OTI.

It would represent tens of millions of Mercedeses that would represent revenue generating opportunity.

And that's that's the that's the vision that <unk> has and what they.

Building towards I think it's going to be extraordinary.

The large installed base of luxury cars.

We'll continue to renew with four customers benefits and also for. Revenue generating benefits.

Revenue generating benefits.

Operator: Your next question comes from the line of Mark Lipacis with Jefferies. Your line is now open.

Mark Lipacis: Hi, thanks for taking my question. I think for you Jensen, it seems like every year a new workload comes out and drives demand for your process through your ecosystem cycles. And if I think back, your facial recognition and then recommendation engines, natural language processing, Omniverse and now generative AI engines, can you share with us your view? Is this what we should expect going forward like a brand new workload that drives demand to the next level for your products? And the reason I ask is because I found it interesting your comments in your script where you mentioned that your kind of view about the demand that generative AI is going to drive for your products and services seems to be a lot better than what you thought just over the last 90 days. And to the extent that there's new workloads that you're working on or new applications that can drive next levels of demand, would you care to share with us a little bit of what you think could drive it past what you're seeing today? Thank you.

It seems like every year, a new workload comes out and drives demand for Europe .

Process through your ecosystem cycles.

I think back facial recognition and then recommendation engines natural language processing on diverse and now generative AI engines can you share with US your view are.

Is this what we should expect going forward like a brand new workload that drives demand. The next level. For your products. And the reason I ask is because I found it interesting your comments in your in your script you mentioned that your your kind of view about.

The next level. For your products. And the reason I ask is because I found it interesting your comments in your in your script you mentioned that your your kind of view about.

For your products. And the reason I ask is because I found it interesting your comments in your in your script you mentioned that your your kind of view about.

And the reason I ask is because I found it interesting your comments in your in your script you mentioned that your your kind of view about.

Demand that generated by AI is going to drive for.

For your products and services.

It seems to be a lot better than what you thought just over the last 90 days, so and to the extent that there's new workloads that youre working on or new applications that can drive next levels of demand would you would you care to share with us.

A little bit of what you think could drive past, what we're what youre seeing today. Thank you.

Jensen Huang: Yeah, Mark I really appreciate the question. First of all, I have new applications that you don't know about and new workloads that we've never shared that I would like to share with you at GTC. And so that's my hook to come to GTC, and I think you're going to be very surprised and quite delighted by the applications that we're going to talk about.

First of all I have new applications that you don't know about.

And new workloads that we've never shared that I would like to share with you at GTC.

And so that's my that's my hope to come to GTC, and I think youre going to be very surprised and quite delighted by the applications that we're going to talk about.

Now there is a reason why it is the case that you're constantly hearing about new applications. And the reason for that is number one, Nvidia is a multi domain accelerated computing platform. It is not completely general purpose like a CPU because a CPU is 95%, 98% control functions and only 2% mathematics, which makes it completely flexible. We're not that way. We are an accelerated computing platform that works with the CPU, that off loads the really heavy computing units, things that can be highly, highly paralyzed to offload them, but we're multi domain. We could do particle systems, we could do fluids, we could do neurons, and we can do computer graphics, we could do [inaudible].

It is the case that youre constantly hearing about new applications and the reason for that is number one.

Nvidia is a multi domain.

Accelerated computing platform.

It is not completely general purpose like a CPU because the CPU is.

95%, 98% control functions, and only 2% mathematics, which makes it completely flexible we're not that way. We are an accelerated computing platform that works with the CPU that off loads the really heavy computing units.

Things that can be highly highly paralyzed.

To offload them.

But we're multi domain, we could we could do particle systems, we can do fluids, we could do neurons.

And we can do computer graphics when could you raise.

There are all kinds of different applications that we can accelerate number one. Number two, our installed base is so large. This is the only accelerated computing platform, the only platform, literally the only one that is architecturally compatible across every single cloud from PCs to workstations, gamers to cars, and to on Prem. Every single computer is architecturally compatible which means that a developer who developed something special would seek out our platform because they like the reach, they like the universal reach, they like the acceleration number one, they like the ecosystem of programming tools and the ease of using it and the fact that they have so many people that can reach out to to help them. There are millions of Cuda experts around the world, software all accelerated, tool all accelerated, and then very importantly, they like the reach. They like the fact that they can reach so many users after the develop the software and it is the reason why we just keep attracting new applications. And then finally, this is a very important point. Remember that the rate of CPU computing advance has slowed tremendously, whereas back in the first 30 years of my career at 10 X and performance at about the same power every five years and at 10X every five years  that rate of continued advance has slowed at a time when people still have really, really urging applications that they would like to bring to the world, and they can't afford to do that with the power keep going up. Everybody needs to be sustainable, you can't continue to consume power, by accelerating it we can decrease the amount of power you use for any workload. So all of these multitude of reasons is really driving people to use accelerated computing and we keep discovering new exciting applications.

There are all kinds of different applications that we can accelerate number one. Number two, our installed base is so large. This is the only accelerated computing platform, the only platform, literally the only one that is architecturally compatible across every single cloud from PCs to workstations, gamers to cars, and to on Prem. Every single computer is architecturally compatible which means that a developer who developed something special would seek out our platform because they like the reach, they like the universal reach, they like the acceleration number one, they like the ecosystem of programming tools and the ease of using it and the fact that they have so many people that can reach out to to help them. There are millions of Cuda experts around the world, software all accelerated, tool all accelerated, and then very importantly, they like the reach. They like the fact that they can reach so many users after the develop the software and it is the reason why we just keep attracting new applications.

This is the only accelerated computing platform the only platform literally the only one that is architecturally compatible across every single cloud from Pcs to workstations gamers to cars.

Two on Prem.

Every single computer is architecturally compatible which means that a developer who developed something special.

Seek out our platform because they like the reach they like the universal reach they like the acceleration number one they like the ecosystem of programming tools and the ease of using it and the fact that they have so many people that can reach out to to help them there.

millions of Cuda experts around the world, software all accelerated, tool all accelerated, and then very importantly, they like the reach. They like the fact that they can reach so many users after the develop the software and it is the reason why we just keep attracting new applications. And then finally, this is a very important point. Remember that the rate of CPU computing advance has slowed tremendously, whereas back in the first 30 years of my career at 10 X and performance at about the same power every five years and at 10X every five years 

Software all accelerated tool all accelerated and then very importantly, they like the reach they like the fact that you can see they can reach so many users. After the develop the software and it is the reason why do we just keep attracting new applications and then finally this is a very important point. Remember. The rate of. CPU computing advance has slowed tremendously.

After the develop the software and it is the reason why do we just keep attracting new applications and then finally this is a very important point. Remember. The rate of. CPU computing advance has slowed tremendously.

Remember. The rate of. CPU computing advance has slowed tremendously.

And then finally, this is a very important point. Remember that the rate of CPU computing advance has slowed tremendously, whereas back in the first 30 years of my career at 10 X and performance at about the same power every five years and at 10X every five years that rate of continued advance has slowed at a time when people still have really, really urging applications that they would like to bring to the world, and they can't afford to do that with the power keep going up. Everybody needs to be sustainable, you can't continue to consume power, by accelerating it we can decrease the amount of power you use for any workload. So all of these multitude of reasons is really driving people to use accelerated computing and we keep discovering new exciting applications.

The rate of. CPU computing advance has slowed tremendously.

CPU computing advance has slowed tremendously.

And.

Whereas back in the first 30 years of my career.

At 10 X and performance at about the same power.

Every.

Every five years and <unk> every five years.

that rate of continued advance has slowed at a time when people still have really, really urging applications that they would like to bring to the world, and they can't afford to do that with the power keep going up. Everybody needs to be sustainable, you can't continue to consume power, by accelerating it we can decrease the amount of power you use for any workload. So all of these multitude of reasons is really driving people to use accelerated computing and we keep discovering new exciting applications.

Applications that they would like to bring to the world.

And they can't afford to do that with the power keep going up everybody needs to be sustainable you can't continue to consume power by accelerating it we can decrease the amount of power you use for any workload.

So all of these multitude of reasons is really driving people to use accelerated computing and we keep discovering new exciting applications.

Operator: Your next question comes from the line of Atif Malik with Citi. Your line is now open.

Your line is now open.

Atif Malik: Hi, thank you for taking my question. Colette, I have a question on data center. You saw some weakness on build plan in the January quarter, but you are guiding to year over year acceleration in April and through the year. So if you could just rank order for us the confidence in the acceleration, is that based on your H 100 ramp or generative AI sales coming through or the new AI services model and also if you could talk about what you're seeing on the enterprise vertical.

Question on data Center, you saw some weakness on build plan.

The January quarter, but you are guiding to year over year acceleration in April and through the year. So if you could just rank order for us.

Confidence index duration is that based on each 100 ramp or generative AI.

It is coming through or new.

Hi services model and also if you could talk about what you're seeing on the enterprise vertical.

Colette Kress: Sure. Thanks for the question. When we think about our growth, yes, we're going to grow sequentially in Q1, and we expect year over year growth in Q1 as well, we will likely accelerate there going forward. So what do we see as the drivers of that? Yes, we have multiple product cycles coming to market. We have H 100 in market now. We are continuing with our networking launches as well that sometimes deal with our GPU computing with our network. And then we have grace coming likely in the second half of the year. Additionally, generative AI has marked intercept definitely among our customers, whether they're the CSP, whether those be on prices, whether those be start ups, we expect that to be a part of our revenue growth this year.

We think about our growth, yes, we're going to grow sequentially in Q1, and you expect year over year growth in.

Q1, as well will likely accelerate there going forward. So what do we see as the drivers of that.

Yes, we have multiple product cycles coming to market, we have each 100 market now.

Continuing with our.

No.

Launches as well.

Sometimes deals with our GPU.

<unk> with our network.

And then we have grace coming likely in the second half.

Additionally, general to the AI.

Marked intercept definitely among our customers, whether they're the CSP, whether those be on prices.

Start ups.

We expect that to be a part of.

Our revenue this year.

And then lastly, let's just not forget that given the end of Moore's law there's an error here of focusing on AI, focusing following accelerated computing. So as the economy improves, this is probably very important to the enterprises, and it can be fueled by the existence of cloud first enterprises let's say. I'm gonna turn it to Jensen if there's any additional things he would like to add. No, you did great.

And then lastly, let's just not forget that given the end of Moore's law there's an error here of focusing on AI, focusing following accelerated computing. So as the economy improves, this is probably very important to the enterprises, and it can be fueled by the existence of cloud first enterprises let's say. I'm gonna turn it to Jensen if there's any additional things he would like to add.

There's an error here.

Focusing on AI, focusing following accelerated computing.

So as the economy improves.

It's probably very important to the enterprises. And it can be fueled by the existence of cloud first pretty unimpressive, let's say.

And it can be fueled by the existence of cloud first pretty unimpressive, let's say.

Now turning to Johnson with any. Any additional things. So you would be great. Okay. Yeah.

Any additional things.

So you would be great.

Okay.

Jensen Huang: No, you did great.

Yeah.

Operator: Your last question today comes from the line of Joseph Moore with Morgan Stanley. Your line is now open.

Your line is now open.

Joseph L. Moore: Great, thank you. Jensen, you talked about sort of 1 million times improvement in your ability to train these models over the last decade, can you give us some insight into what that looks like in the next few years and to the extent that some of your customers with these large language models are talking about 100X complexity over that kind of timeframe. I know Hopper is 6X better transformer performance, but what can you do to scale that up and how much of that just reflects it's going to be a much larger hardware expense down the road?

Hopper six X better transformer performance, but what can you do to two.

To scale that up and how much of that just reflects it it's going to be a much larger hardware expense down the road.

Jensen Huang: First of all, I'll start backwards, I believe the number of AI infrastructures are going to grow all over the world. And the reason for that is, AI, the production of intelligence is going to be manufacturing. There was a time when people manufacture just physical goods. In the future, almost every company will manufacture soft goods, it just happens to be in the form of intelligence. Data comes in, that data center does exactly one thing and one thing only, it crank on that data and it produces a new updated model. Where raw material comes in, a building or infrastructure cranks on it, and something refined or improved comes out that is of great value, that's called the factory. And so I expect to see AI factories all over the world. Some of them will be hosted in cloud, some of it will be on Prem, there'll be some that are large, and there are some that will be mega large, and then there'll be some that are smaller. And so I expect that to happen number one.

First of all I'll start backwards I believe the number of AI infrastructures.

We're going to grow all over the world and the reason for that is.

AI the production of intelligence is going to be manufacturing.

There was a time when people manufacturer just physical goods in the future there will be there'll be almost every company will manufacture.

Soft goods just happens to be in the form of intelligence data comes in that data Center does exactly one thing and one thing only it crank on that data and it produces a new updated model.

Where where.

Raw material comes in a building or infrastructure cranks on it and something refined or improved comes out that is of great value. That's called the factory and so I expect to see AI factories, all over the world. Some of them will be hosted and cloud. Some some of it will be on Prem there'll be some that are large and there was some.

That will be Mega large and then there'll be some that are smaller.

And so I expect that to happen number one. Number two, over the course of the next 10 years, I hope through new chips, new interconnects, new systems, new operating systems, new distributed computing algorithms, and new AI algorithms and working with developers coming up with new models, I believe we're going to accelerate AI by another 1 million X. There's a lot of ways for us to do that and that's one of the reasons why Nvidia is not just a chip company because the problem we're trying to solve is just too complex, you have to think across the entire stack all. All the way from the chip, all the way into the data center, across the network through the software and in the mind of one single company, we can think across that entire stack and it's really quite a great playground for computer sciences for that reason, because we can innovate across that entire stack. So my expectation is that you're going to see really gigantic breakthroughs in AI models in the next company--in AI platforms in the coming decade, but simultaneously because of the incredible growth and adoption of this we're going to see these factories everywhere.

And so I expect that to happen number one.

Number two, over the course of the next 10 years, I hope through new chips, new interconnects, new systems, new operating systems, new distributed computing algorithms, and new AI algorithms and working with developers coming up with new models, I believe we're going to accelerate AI by another 1 million X. There's a lot of ways for us to do that and that's one of the reasons why Nvidia is not just a chip company because the problem we're trying to solve is just too complex, you have to think across the entire stack all. All the way from the chip, all the way into the data center, across the network through the software and in the mind of one single company, we can think across that entire stack and it's really quite a great playground for computer sciences for that reason, because we can innovate across that entire stack. So my expectation is that you're going to see really gigantic breakthroughs in AI models in the next company--in AI platforms in the coming decade, but simultaneously because of the incredible growth and adoption of this we're going to see these factories everywhere.

Over the course of the next 10 years.

<unk> through new chips, new Interconnects, new systems, New operating systems, New distributed computing algorithms in new AI algorithms and so working with developers coming up with new models I believe we're going to accelerate AI by another $1 million next.

There's a lot of ways for us to do that and that's one of the reasons why.

And video is not just a chip company because the problem. We're trying to solve is just too complex you have to think across the entire stack all.

All the way from the chip all the way into the data center across the network through the software and in. In the mind of one single company, we can think across that entire stack and it's.

In the mind of one single company, we can think across that entire stack and it's.

Really quite a great playground for computer sciences for that reason, because we can innovate across that entire stack. So my expectation is is that youre going to see really gigantic breakthroughs in AI models in the next company.

So my expectation is is that youre going to see really gigantic breakthroughs in AI models in the next company.

Our platforms in the coming decade, but simultaneously because of the incredible growth and adoption of this we're going to see these factories everywhere.

Okay.

Operator: This concludes our Q&A session, I will now turn the call back over to Jensen Huang for closing remarks.

Jensen Huang: Thank you. The accumulation of breakthroughs from transformers, large language model, and generative AI has elevated the capability and versatility of AI to a remarkable level. A new computing platform has emerged. New companies, new applications, and new solutions to longstanding challenges are being invented at an astounding rate. Enterprises in just about every industry are activating to applied generative AI to reimagine their products and businesses. The level of activity around the AI, which was already high, has accelerated significantly. This is the moment we've been working towards for over a decade, and we are ready. Our hopper AI supercomputer with the new transformer engine and quantum Infiniband fabric is in full production and CSPs are racing to open their hopper cloud services. As we work to meet the strong demand for our GPUs, we look forward to accelerating growth through the year. Don't miss the upcoming GTC. We have much to tell you about new chips systems and software, new cuda applications and customers, new ecosystem partners, and a lot more on Nvidia AI and Omniverse. This will be our best GTC yet. See you there.

A new computing platform has emerged.

New companies.

New applications.

And new solutions to longstanding challenges are being invented at an astounding rate enterprises in just about every industry are activating to applied generative AI to re imagine their products and businesses.

The level of activity around the AI, which was already high has accelerated significantly.

This is the moment, we've been working towards for over a decade. And we are ready. Our hopper AI supercomputer with the new transformer engine and quantum Infiniband fabric is in full production and.

And we are ready. Our hopper AI supercomputer with the new transformer engine and quantum Infiniband fabric is in full production and.

Our hopper AI supercomputer with the new transformer engine and quantum Infiniband fabric is in full production and.

And CSP are racing to open their hopper cloud services.

As we work to meet the strong demand for our Gpus, we look forward to accelerating growth through the year.

Don't Miss the upcoming GTC.

We have much to tell you about new chips systems and software.

<unk>. New cuda applications and customers. New ecosystem partners and a lot more on Nvidia AI and <unk> This will be our best GTC yet.

New cuda applications and customers. New ecosystem partners and a lot more on Nvidia AI and <unk> This will be our best GTC yet.

New ecosystem partners and a lot more on Nvidia AI and <unk> This will be our best GTC yet.

See you there.

Operator: This concludes today's conference, you may now disconnect.

This concludes today's conference. Of disconnect.

Of disconnect.

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