Q2 2024 NVIDIA Corp Earnings Call

August 23, 2023

Being recorded all lines have been placed on mute to prevent any background noise. After the speakers' remarks will be a question and answer session. If you'd like to ask a question. During this time simply press the star key followed by the number one on your telephone keypad, if you'd like to withdraw your question Press Star one once again. Thank you Simona Jankowski you may begin your conference.

Thank you and good afternoon, everyone and welcome to Nvidia Conference call for the second quarter of fiscal 2024 with me today from Nvidia 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.

<unk> Investor Relations website, the webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2024. The contents of today's call is invidious property it can 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 August 23, 2023 based on information currently available to us except as required by law, we assume no obligation to update any such statements.

Good afternoon my.

My name is David and I'll be your conference operator today at this time I'd like to welcome everyone to Nvidia second quarter earnings call. Today's conference is being recorded 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 the star key followed by the number one on your telephone.

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 and with that let me turn the call over to collect.

You bet, if you'd like to withdraw your question Press Star one once again.

Thanks Simona.

We had an exceptional quarter.

Thank you Simona Jankowski you may begin your conference.

Our record Q2 revenue of $13 five 1 billion was up 88% sequentially and up 101% year on year and above our outlook of 11 billion.

Thank you and good afternoon, everyone and welcome to and video conference call for the second quarter of fiscal 2024 with me today from Nvidia 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 and videos.

Let me first start with data center.

Record revenue of 10.32 billion was up 141% sequentially and up 171% year on year.

Sure Relations website, the webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 'twenty 'twenty four.

Data center compute revenue nearly tripled year on year.

Driven primarily by accelerating demand for cloud from cloud service providers and large consumer internet companies for our HD X platform the engine of generative AI and large language models.

The contents of today's call is NVIDIA's property, it can be reproduced or transcribed without our prior written consent.

Major companies, including AWS, Google Cloud meta, Microsoft Azure, and Oracle cloud as well as growing number of GPU cloud providers are deploying in volume HTS systems based on our Hopper and ampere architecture tensor core GPU.

Q and the reports that we may file on form 8-K, with the Securities and Exchange Commission.

Networking revenue almost doubled year on year, driven by our end to end Infiniband networking platform the gold standard for Aegon.

All our statements are made as of today August 23rd 2023 based on information currently available to us except as required by law, we assume no obligation to update any such statements.

There is tremendous demand for Nvidia accelerated computing and AI platforms.

Our supply partners have been exceptional and ramping capacity to support our needs.

Our data center supply chain, including HCS with 35000 parts in highly complex networking has been built up over the past decade.

Thanks Simona.

We had an exceptional quarter.

Record Q2 revenue of $13 five 1 billion was up 88% sequentially and up 101% year on year and above our outlook of 11 billion.

We have also developed and qualified additional capacity and suppliers for key steps in the manufacturing process such as Carlos packaging.

Let me first start with data center.

We expect supply to increase each quarter through next year.

Record revenue of 10.32 billion was up 141% sequentially and up 171% year on year.

By geography.

Data center growth was strongest in the U S as customers direct for capital investments to AI and accelerated computing.

Data center compute revenue nearly tripled year on year.

China demand was within the historical range of 20% to 25% of our data center revenue, including compute and networking solutions.

Driven primarily by accelerating demand for cloud from cloud service providers and large consumer internet companies for our HD X platform.

Our agenda today are in Los angles model.

At this time, let me take a moment to address recent reports on the potential for increased regulations on our exports to China.

We believe the current regulation is achieving the intended results.

Given the strength of demand for our products worldwide, we do not anticipate that additional export restrictions on our data center Gpus, if adopted would have immediate material impact to our financial results.

Networking revenue almost doubled year on year, driven by our end to end Infiniband networking platform the gold standard for Aegon.

There is tremendous demand for Nvidia accelerated computing and AI platforms are.

However over the long term restrictions prohibiting the sale of our datacenter Gpus to China. If implemented will result in a permanent loss of an opportunity for the U S industry to compete and me and one of the worlds largest market.

Our supplier partners have been exceptional and ramping capacity to support them.

Our data center supply chain, including HCS with 35000 parts in highly complex network has been built up over the past decade.

Our cloud service providers drove exceptional strong demand for <unk> systems in the quarter.

They undertake a generational transition to upgrade their data center infrastructure for the new era of accelerated computing and AI.

We have also developed and qualified additional capacity and suppliers for key staff in the manufacturing process such as cobalt packaging.

The Nvidia <unk> popcorn is come.

We expect supply to increase each quarter through next year.

Culminating of nearly two decades of full stack innovation across silicon systems interconnect networking software and algorithm.

By geography.

The data center growth was strongest in the U S as customers direct capital investments to AI and accelerated computing.

Instances powered by the Nvidia H 100 tensor core Gpus are now generally available in AWS, Microsoft Azure and several GPU cloud providers with others on the way shortly.

China demand was within the historical range of 20% to 25% of our data center revenue, including compute and networking solutions.

Consumer Internet companies also drove the very strong demand there are investments in data center infrastructure purpose built for AI are already generating significant returns for.

At this time, let me take a moment to address recent reports on the potential for increased regulation on our exports to China.

For example, Nutter recently highlighted that since launching real and AI recommendations have driven a more than 24% increase in time spent on Instagram.

We believe the current regulation is achieving the intended results.

Given the strength of demand for our products worldwide, we do not anticipate that additional export restrictions on our data center Gpus, if adopted would have an immediate material impact to our financial results.

Enterprises are also racing to deploy generative AI driving strong consumption of Nvidia powered instances in the cloud as well as demand for on premise infrastructure.

Whether we serve customers in the cloud or on Prem through partners or direct.

Applications can run seamlessly.

Nvidia AI enterprise software with access to our acceleration libraries retained models and API.

Our cloud service providers drove exceptional strong demand for <unk> systems in the quarter as they undertake a generational transition to upgrade their datacenter infrastructure for the new era of accelerated computing and AI.

We announced a partnership with snowflake to provide enterprises with accelerated path to create customized generative AI applications using their own proprietary data.

All securely within the Snowflake data cloud.

The Nvidia HD X platform.

With the Nvidia Nemo platform for developing large language models enterprises will be able to make custom.

Hum, culminating of nearly two decades of full stack innovation across silicon systems interconnect networking software and algorithm.

For advanced AI services, including Chatbot.

Search and summarization right from Nov.

Instances powered by the Nvidia H 100 tensor core Gpus are now generally available in AWS, Microsoft Azure and several GPU cloud providers with others on the way shortly.

Slide eight on the cloud.

Virtually every industry can benefit from general today are for example, AI co pilot such as those just announced by Microsoft and boost the productivity of over 1 billion office workers and tens of millions of software engineers.

Consumer Internet companies also drove the very strong demand there are investments in data center infrastructure purpose built for AI are already generating significant returns.

He is a professional and legal services sales customer support and education will be available to leverage AI systems training in their field.

For example, <unk> recently highlighted that since launching real and AI recommendations has driven a more than 24% increase in time spent on Instagram.

AI co pilot and assistance are set to create new multi hundred billion dollar market opportunities for our customers.

Enterprises are also raising to deploy generative AI driving strong consumption of Nvidia powered instances in the cloud as well as demand for on premise infrastructure.

We are seeing some of the earliest applications, a generous with AI and marketing media and entertainment.

Whether we serve customers in the cloud or on Prem through partners or draw for applications can run seamlessly.

<unk>, the world's largest marketing and communication services organization is developing a content engine using nvidia omni versus to enable artists and designers to integrate generative AI into three D content creation.

Video AI enterprise software with access to our acceleration libraries retained models and API.

WTP designers can create images from text prompts while responsibly trained generative AI tools and content from Nvidia partners, such as Adobe and Getty images, using Nvidia Picasso, a foundry for custom generative AI models for visual design.

We announced a partnership with snowflake to provide enterprises with accelerated path to create customized generous with AI application using their own proprietary data all securely within the snowflake data cloud.

With the Nvidia Nemo platform for developing large language models enterprises will be able to make custom.

Visual content provider Shutterstock is also using Nvidia Picasso to build tools and services that enables users to create <unk> seen background with the help of generative behind.

For advanced AI services, including chat bot.

Search and so much innovation right from the Snowflake data on cloud.

We partnered with service now and Accenture to launch the AI Lighthouse program.

Virtually every industry can benefit from <unk> today are for example.

Fast tracking the development of enterprise AI capabilities.

AI co pilot such as those just announced by Microsoft and boost the productivity of over 1 billion office workers and tens of millions of software engineers.

Our lighthouse unites the service now enterprise automation platform and engine with Nvidia accelerated computing and with Accenture consulting and deployment services.

So he is a professional and legal services sales customer support and education will be available to leverage AI system trained in their field.

We are collaborating also with her hugging face to simplify the creation of new and custom AI models for enterprises I can face will offer a new service for enterprises to train and to advanced AI model powered by Nvidia the jackpot.

AI co pilot and the systems are set to create new multi hundred billion dollar market opportunities.

Our customers.

We are seeing some of the earliest applications a generous with AI and marketing media and entertainment W. P. P. The worlds largest marketing and communication services organization is developing a content engine using nvidia on members to enable artists and designers.

And just yesterday, Vmware and Nvidia announced a major new enterprise offering coding Vmware private AI Foundation with Nvidia.

A fully integrated platform, featuring AI software and accelerated computing.

<unk> with multi cloud software for enterprises running Vmware.

Integrated generative AI into three D content creation.

Vmware has hundreds of thousands of enterprise customers will have access to the infrastructure AI and cloud management software needed to customize models and run generative AI applications, such as intelligence Chatbot assistance search and so much inflation.

WTP designers can create images from tax problems, while responsibly trained generative AI tools and content from Nvidia partners, such as Adobe and Getty images, using Nvidia Picasso, a foundry for custom generative AI models for visual design.

We also announced new Nvidia AI enterprise ready server.

Visual content provider Shutterstock is also using nvidia for hospitals to build tools and services that enable users to create three D seen background with a help of generative behind.

Featuring the new Nvidia, Oh, 40 F GPU build for the industry standard datacenter server ecosystem and Lucille III GPU data center infrastructure processor.

We partnered with service now and Accenture to launch the AI Lighthouse program fast tracking the development of enterprise AI capabilities.

<unk> <unk> 40 S is not limited by cobalt supply and is shipping to the world leading server system makers.

Lighthouse unite with service now enterprise automation platform and engine with Nvidia accelerated computing and with Accenture consulting and deployment services.

Oh 40 F is a universal datacenter processor designed for high volume data center standing out to accelerate the most compute intensive applications, including AI training and inferencing.

We are collaborating also and with us having face to simplify the creation of new and custom AI models for advertisers I can face will offer a new service for enterprises to train and to advanced AI models.

Designing visualization video processing and Nvidia on members industrial digitalization.

Nvidia AI enterprise ready servers are fully optimized for Vmware Cloud Foundation and private AI Foundation.

Our by Nvidia the jackpot.

And just yesterday, Vmware and Nvidia announced a major new enterprise offering coding Vmware private AI Foundation within video.

Nearly 100 configurations of Nvidia AI enterprise ready serves will soon be available from the worlds, leading enterprise computing companies, including Dell HP and Lenovo.

A fully integrated platform, featuring AI software and accelerated computing.

The G. H 200, Grace Hopper Super Chip, which combines our arm based CPU with Hopper GPU entered full production and will be available this quarter and OEM servers. There's also shipping to multiple supercomputing customers, including Los Alamos.

With multi cloud software for enterprises running Vmware.

Additional lab and the Swiss National shooting Center.

And Nvidia and Softbank are collaborating on a platform based on th 200, regenerative AI and <unk> 60 applications.

We also announced new Nvidia AI enterprise ready server.

Featuring the new Nvidia, Oh, 40 S. GPU build for the industry standard datacenter server ecosystem and Lucille three D C New data center infrastructure processor.

The second generation version of our race upper Super ship with the latest HBM.

Memory will be available in Q2 of calendar 'twenty 'twenty four.

40 S is not limited by cobalt supply and is shipping to the worlds leading server system makers.

We announced the Gtx G. H 200, a new class of large memory AI supercomputer for giant AI language model recommendation systems and data analytics. This is the first use of the new Nvidia Nvme switch system, enabling all of it too.

Oh 40 F is a universal datacenter processor designed for high volume data center standing out to accelerate the most compute intensive applications, including AI training and infancy.

The design and visualization video processing and Nvidia omnivorous industrial digitalization.

<unk> hundred 56 upper.

Hopper Super Chip.

Work together as one a huge jump compared to our prior generation connecting just HGTV is underway.

Nvidia AI enterprise ready servers are fully optimized for Vmware Cloud Foundation and private AI Foundation.

<unk> G. H 200 systems are expected to be available by the end of the year, Google cloud matter and Microsoft among the first to gain access.

Nearly 100 configurations of Nvidia AI enterprise ready serves will soon be available from the worlds, leading enterprise IP computing companies, including Dell HP and Lenovo.

Strong networking growth was driven primarily by Infiniband infrastructure to connect HD X GPU systems. Thanks.

Thanks to its end to end optimization and in network computing capabilities.

<unk> delivers more than double the performance of traditional Ethernet for AI.

For billions of dollars AI infrastructures the value from the increased throughput of Infiniband is worth hundreds of melanoma and paid for the network. In addition, only infiniband can scale to hundreds of thousands of Gpus.

National Labs, and the Swiss National shooting Center.

And Nvidia and Softbank are collaborating on a platform based on G. H 200 for generous in AI and <unk> 60 applications.

It is the network of choice for leading AI practitioners.

The second generation version of our race upper systems.

For Ethernet based cloud data centers that seek to optimize their AI performance, we announced Nvidia spectrum.

With the latest HBM.

<unk> memory will be available in Q2 of calendar 'twenty 'twenty four.

And accelerated networking platform designed to optimize Ethernet for AI workloads.

We announced the Gtx G. H 200, new class large memory AI supercomputer for giant AI language model recommendation systems and data analytics. This is the first use of the new Nvidia Nvme switch system, enabling all of its two.

Spectrum ex couples the spectrum for Ethernet switch, where the bluefield three GPU, achieving one five X better overall, AI performance and power efficiency versus traditional Ethernet.

Yes.

We will feel III GPU is a major success.

Third 56 upper.

Super Super Chip.

It is in qualification with major Oems and ramping across multiple CSB and consumer demand.

Work together as one a huge jump compared to our prior generation connecting just HGTV is already underway.

Now moving to gaming.

<unk> G. H 200 systems are expected to be available by the end of the year, Google cloud and Microsoft among the first to gain access.

Gaming revenue of $2 49 billion was up 11% sequentially and 22% year on year.

Growth was fueled by <unk> 40 series Gpus for laptops and desktops.

Strong networking growth was driven primarily by Infiniband infrastructure to connect HTS GPU system. Thanks.

And customer demand was solid and consistent with seasonality we.

Believe global end demand has returned to growth after last year's slowdown.

Thanks to its end to end optimization and thin network computing capabilities.

We have a large upgrade opportunity ahead of us just 47% of our installed base of upgraded to our tax and about 20% of a GPU within RTI.

And about the weather was more than double the performance of traditional Ethernet for AI.

For billions of dollars AI infrastructures the value from the increased throughput of Infiniband is worth hundreds of melanoma and pays for the network. In addition, only infiniband can scale to hundreds of thousands of Gpus.

<unk> 60 for higher performance.

Laptop Gpus posted strong growth in the key back to school season led by <unk> 46 to eight Gpus Nvidia.

It is the network of choice for leading AI practitioners.

And videos.

<unk> powered laptops have gained in popularity and their shipments are now outpacing desktop gpus from several regions around the world.

For Ethernet based cloud data centers that seek to optimize their AI performance, we announced Nvidia spectrum.

This is likely to shift reality of our overall gaming revenue that we have.

And accelerated networking platform designed to optimize Ethernet for AI workloads.

Q2, and Q3, the stronger quarters of the year, reflecting the back to school and holiday build schedules for laptops.

Spectrum ex couples the spectrum for Ethernet switch, where the bluefield three CPU, achieving one five X better overall, AI performance and power efficiency versus traditional.

In desktop we launched the <unk> R. T X 40, 60, and the G Force Archie exports 60 T. IGT is bringing the Ada Lovelace architecture down to price points as low as $299.

Yes.

Lucy L. Three GPU is a major success.

The ecosystem of RPX and D. O S. S games, continuing to expand 35, new games added to deal as a support including blockbusters, such as Diablo four and Baldur's gate three.

It is in qualification with major OEM and ramping across multiple CSB and consumer.

Now moving to gaming.

Gaming revenue of $2 49 billion was up 11% sequentially and 22% year on year.

There's now over 330, <unk> accelerated games and apps.

Growth was fueled by <unk>, our T X 40 series Gpus for laptops and desktops.

We are bringing generative AI to gain.

Computex, we announced Nvidia Avatar cloud engine.

And customer demand was solid and consistent with seasonality.

For games Accustom, AI model foundry service.

Believe global end demand has returned to growth after last year's slowdown.

And our first can use system to bring intelligence to non player characters.

We have a large upgrade opportunity ahead of us just 47% of our installed base of upgraded to our tax and about 20% of a GPU with RTI.

It had harnesses a number of Nvidia auditing version, AI technologies, including Nemo fever, and audio twofold.

30, 60 or higher performance.

Now moving to professional visualization.

[noise] laptop Gpus posted strong growth in the.

Revenue of $375 million was up 28% sequentially and down 24% year on year.

Key back to school season led by Artie adds 40, 60 Gpus Nvidia.

Nvidia <unk>.

The Ada architecture ramp drove strong growth in Q2 rolling out initially been laptop workstations with a refresh of desktop workstations coming in Q3.

<unk> powered laptops are gained in popularity and their shipments are now outpacing desktop gpus from several regions around the world.

This is likely to shift reality of our overall gaming revenue in bed with Q2 and Q3.

These will include powerful new RPX systems with up to four Nvidia RPX 6000, Gpus, providing more than 5800 teraflops of AI performance and 192 gigabytes of GPU memory.

The stronger quarters of the year, reflecting the back to school and holiday build schedules for laptops.

In desktop we launched the <unk> R. T X 40, 60, and the G Force Archie exporting 60, Ti Gpus, bringing the Ada Lovelace architecture down to price points as low as $299.

They can be configured with Nvidia AI enterprise or from video omnibus.

We also announced three new desktop workstation Gpus based on the eighth generation.

The ecosystem of Rts and DNS EFS gains continuing to expand 35, new games added to deal as a support including blockbusters, such as the outlook for and Baldur's Gate three.

The Nvidia RPX 5040, 504000, offering up to two X the RT core throughput and up to two X faster air training performance compared to the previous generation.

There is now over 330, <unk> accelerated games and apps.

In addition to the traditional workloads such as through the design and content creation, new workloads and generous shipping.

We are bringing generative AI to gain.

Computex, we announced Nvidia Avatar cloud engine.

Large language model in development and data science are expanding the opportunities and pro visualization for our T X technology.

For games Accustom, AI model foundry service.

Workers can use system to bring intelligence to non player characters.

One of the key themes and Johnson's keynote at SIGGRAPH earlier. This month was the conversion of graphics and Eli This is where Nvidia on river is position on <unk>.

It harnesses a number of Nvidia omni version, AI technologies, including Nemo, Viva and audio T cells.

<unk> is open USD native platform open USD is a universal interchange that is quickly becoming the standard for the three DS world much like HTML is the universal language for the <unk>.

Now moving to professional visualization.

Revenue of $375 million was up 28% sequentially and down 24% year on year.

Ada architecture ramp drove strong growth in Q2 rolling out initially in laptop workstations with a refresh of desktop workstations coming in Q3.

Together Adobe Apple Autodesk Pixar and in video form the alliance for open doors to our mission is to accelerate open USD development and adoption.

This will include powerful new RPX systems with up to four Nvidia RPX 6000, GP is providing more than 5800 teraflops of AI performance and 192 gigabytes of GPU memory.

Announced new and upcoming omnivores.

Including run Ust and chat USD to bring generative AI to open doors.

Moving to automotive.

They can be configured with <unk>.

Revenue was 253 million down, 15% sequentially and up 15% year on year.

Lydia AI enterprise or in video <unk>.

We also announced three new desktop workstation Gpus based on the Ada generation.

<unk> year on year growth was driven by the ramp of self driving platform based on the prescribed oren associated with a number of new energy vehicles, because the sequential decline reflects lower overall automotive demand, particularly in China. We.

Nvidia RPX 5040, 504000, offering up to two X the RG core throughput and up to two X faster air training performance compared to the previous generation.

We announced a partnership with Mediatek to bring drivers and passengers new experiences inside the car.

In addition to the traditional workloads such as through the design and content creation, new workloads and generous Shanghai large language model in development and data science are expanding the opportunity and pro visualization for our T X technology.

Mediatek will develop automotive slc's and integrate a new product line of Nvidia GPU shipments.

The partnership covers a wide range of vehicle segments from luxury to entry level.

One of the key themes and Johnson's keynote at SIGGRAPH earlier. This month was the conversion of graphics and Eli This is where Nvidia omni averts his position on <unk>.

Moving to the rest of the P&L.

GAAP gross margins expanded to 71% and non-GAAP gross margin to 71, 2% driven by higher data center sales our datacenter products include <unk>.

<unk> is open USD native platform open USD is a universal interchange that is quickly becoming the standard for this reason world much like HTML is the universal language for the <unk>.

Significant amount of software and complexity.

Which is also helping to drive our gross margin.

Sequential GAAP operating expenses were up 6% and non-GAAP operating expenses were up 5%, primarily reflecting increased compensation and benefits.

Together Adobe Apple Autodesk Pixar and in video form the alliance for open doors to our mission is to accelerate open USD development and adoption, we announced new and upcoming omnivores.

We returned approximately $3 4 billion to shareholders in the form of share repurchases and cash dividends.

Including run USD on chat USD to bring generative AI to open doors.

Our board of Directors has just approved an additional $25 billion in stock repurchases to add to our remaining $4 billion of authorization as of the end of Q2.

Moving to automotive.

Revenue was 253 million down, 15% sequentially and up 15% year on year.

Let me turn to the outlook for the third quarter of fiscal 2020 for demand.

Solid year on year growth was driven by the ramp of self driving platforms based on religion, right Orin Soc with a number of new energy vehicle makers.

Demand for our data center platform for AI is tremendous and broad based across industries and customers.

The decline reflects lower overall automotive demand, particularly in China.

Our demand visibility extend into next year.

We announced a partnership with Mediatek to bring drivers and passengers new experiences inside the car.

Our supply over the next several quarters, we will continue to ramp as we lower cycle times and work with our supply partners to add capacity. Additionally.

Mediatek will develop automotive slc's and integrated a new product line of Nvidia GPU chip the.

The partnership covers a wide range of vehicle segments from luxury to entry level.

Additionally, the new our 40 S. GPU will help address the growing demand for many types of workloads from cloud.

Moving to the rest of the P&L.

GAAP gross margins expanded to 71% and non-GAAP gross margin to 71, 2% driven by higher data center sales our datacenter products include <unk>.

Right.

For Q through for Q3.

Total revenue is expected to be 16 billion plus or minus 2%, we expect sequential growth to be driven largely by data center with gaming and prove is also contributing.

Significant amount of software and complexity.

Which is also helping to drive our gross margin.

GAAP and non-GAAP gross margins are expected to be 71, 5% and 72.

Sequential GAAP operating expenses were up 6% and non-GAAP operating expenses were up 5%, primarily reflecting increased compensation and benefits.

5%, respectively, plus or minus 50 basis points.

The non-GAAP operating expenses.

Are expected to be approximately $2 95 billion and 2 billion respectively.

We returned approximately $3 4 billion to shareholders in the form of share repurchases and cash dividends.

GAAP and non-GAAP other income and expenses are expected to be from income of approximately 100000.

Our board of Directors has just approved an additional 25 billion in stock repurchases to add to our remaining 4 billion of authorization as of the end of Q2.

Excluding gains and losses for chronically.

Both of them.

GAAP and non-GAAP tax rates are expected to be.

<unk> thousand 14, 5% plus or minus 1%, excluding any discrete items.

Let me turn to the outlook for the third quarter of fiscal 2024.

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

Demand for our data center platform for AI is tremendous and broad based across industries and customers are.

In closing, let me highlight some upcoming events for the financial community. We will attend the Jefferies Tech Summit on August 30 in Chicago, The Goldman Sachs Conference on September 1st time Francisco <unk>.

Our demand visibility extend into next year.

Our supply over the next several quarters, we will continue to ramp as we lower cycle times and work with our supply partners to add capacity. Additionally.

Evercore semiconductor conference on September six as well as the city Tech Conference on September seven.

Additionally, the new hour 40 S. GPU will help address the growing demand for many types of Brooklyn from cloud.

In New York, and the Bofa Virtual AI conference on September 11th.

Our earnings call to discuss the results of our third quarter of fiscal 2024 is scheduled for Tuesday November 1st.

Hi.

For Q through for Q3 total.

Total revenue is expected to be 16 billion plus or minus 2%, we expect sequential growth to be driven largely by data center with gaming and prove is also contributing.

Operator, we will now open the call for questions could you. Please poll for questions. Thank you.

Thank you 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. We ask that you. Please limit yourself to one question, we'll pause for just a moment to compile the Q&A roster.

GAAP and non-GAAP gross margins are expected to be 71, 5% and 72.

5%, respectively, plus or minus 50 basis points.

We'll take our first question from Matt Ramsay with TD Cowen. Your line is now open.

GAAP and non-GAAP operating expenses.

Are expected to be approximately $2 95 billion and 2 billion respectively.

Yes. Thank you very much good afternoon.

Obviously, a remarkable result.

GAAP and non-GAAP other income and expenses are expected to be from income of approximately $100000.

Jensen I wanted to ask a question of you regarding the.

Excluding gains and losses for chronically.

Really quickly emerging application of large model in France, So I think it's pretty well understood by.

Both of them.

GAAP and non-GAAP tax rates are expected to be.

<unk> thousand 14, 5% plus or minus 1%, excluding any discrete items.

The majority of investors that you guys have.

Very much locked down share of the training market.

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

A lot of the smaller market smaller smaller model inference workloads.

Ben Don on Asics, or Cpus in the past and with many of these GPT theyre really large models.

This new workload, that's accelerating Super Duper quickly on large model in Princeton, I think Grace Hopper Super chip products, and others are pretty well aligned for that but could you maybe talk to us about how youre seeing the inference market.

Evercore semiconductor conference on September six as well as the city Tech Conference on September seven.

Segment between small model in France, and large model in France, and how your product portfolio is positioned for that.

In New York, and the Bofa Virtual AI conference on September 11th.

Our earnings call to discuss the results of our third quarter of fiscal 2024. It is scheduled for Tuesday November 1st.

Yes, Thanks, a lot so let's take a quick step back.

These large language models are fairly.

Operator, we will now open the call for questions could you. Please poll for questions. Thank you.

Pretty phenomenal.

It it does several things of course, it has the ability to understand unstructured language, but at its core what it has learned is the structure of human language.

And it Hasnt coded.

Within it compressed within it a large amount of human knowledge that has learned by.

We'll take our first question from Matt Ramsay with TD Cowen. Your line is now open.

The corpus is that it studied.

What happens is you create these large language models and you create as large as you can.

Yes. Thank you very much good afternoon.

Obviously, a remarkable result.

And then you derive from it smaller versions of the model essentially teacher student models.

Jensen I wanted to ask a question of you regarding the.

It's a process called distillation.

Really quickly emerging application of large model in France.

And so when you see these smaller smaller models, it's very likely.

I think it's pretty well understood by the majority of investors that you guys have.

The case.

They were derived from or distilled from or learned from larger models.

Very much locked down share of the training market.

I'm, just as you have professors and teachers and students and so on so forth.

A lot of the smaller markets smaller smaller model inference workloads.

And you're going to see this going forward and so you start from a very large model and has built and has.

Ben Don on Asics, or Cpus in the past and with many of these GPT, they're really large models.

A large amount of generality in generalization.

This new workload Thats accelerating Super Duper quickly on on large model in France, and I think your Grace Hopper Super chip products, and others are pretty well aligned for that but could you maybe talk to us about how youre seeing the inference market.

What's called zero shot capability.

So for a lot of applications.

And questions or skills that you haven't trained specifically on these.

These larger language models miraculously has the capability to perform them that's what makes it so magical.

Segment between small model in France, and large model in France, and how your product portfolio is positioned for that.

On the other hand on the other hand you.

You would like to have.

These capabilities in all kinds of computing devices and so what you do is you distill them down these smaller models might have excellent capabilities in particular skill, but they don't generalize as well. They don't have what is called as good zero shot capabilities.

Yes, Thanks, a lot so let's take a quick step back.

These large language models are fairly.

Pretty phenomenal.

Did it does several things of course, it has the ability to understand unstructured language, but at its core what it has learned is the structure of human language.

And so they all have their own unique capabilities, but you start from very large models.

And it Hasnt coded.

Within it compressed within it a large amount of human knowledge that it has learned by the.

Next we'll go to Vivek Arya with Bofa Securities. Your line is now open.

Alright, thank you.

<unk> studied.

Had a quick clarification on a question Colette if you could please clarify how much incremental supply do you expect to come online in the next year or you think it's up 2030 40, 50%. So just any sense of how much supply because you said, it's growing every quarter.

What happens is you create these large language models and you create as long as you can.

And then you derive from it smaller versions of the model essentially teacher student models.

It's a process called distillation.

And then Jensen. The question for you is when we look at the overall hyperscale or spending that's by is not really growing that much. So what is giving you the confidence that they can continue to carve out more.

And so when you see these smaller smaller models, it's very likely the case.

They were derived from or distilled from or learned from larger models.

I'm, just as you have professors and teachers and students and so on and so forth.

<unk> bye.

A generative AI just give us your sense of how sustainable is this demand as we look over the next one to two years. So if I take your implied Q3 outlook of data center towards 15 billion, what does that say about how many servers are already AI accelerators.

And you're going to see this going forward and so you start from a very large model N has built and has.

A large amount of generality in generalization.

What's called zero shot capability and so for a lot of applications.

Not doing so just give us some confidence that the growth that you're seeing is sustainable into the next one to two years.

And questions or skills that you haven't trained it specifically on these larger language models miraculously has the capability to perform them. That's what makes it so magical.

So thanks for that second question regarding our supply I guess, we do expect to continue increasing ramping our supply over the next quarters as well as into our next.

On the other hand on the other hand you.

You would like to have these.

These capabilities with all kinds of computing devices and so what you do is you distill them down these smaller models might have excellent capabilities in particular skill, but they don't generalize as well. They don't have what is called as good zero shot capabilities.

Next fiscal year in terms of percentage not something that we have here it is a.

Our work across so many different supplier of so many different parts of building and.

So they all have their own unique capabilities, but you start from very large models.

And <unk> and many of our other new products that are coming to market.

But we are very pleased with both the support that we have with our suppliers.

Next we'll go to Vivek Arya with Bofa Securities. Your line is now open.

In the long time that we've spent with them on improving as supply.

Alright, thank you.

Had a quick clarification on a question Colette if you could please clarify how much incremental supply do you expect to come online in the next year. You think it's up 2030 40, 50%. So just any sense of how much supply because you said, it's growing every quarter.

Okay.

The World has something along the lines of about a trillion dollars worth of data centers installed in the cloud and enterprise and otherwise.

The trillion dollars of data centers is in the process of transitioning.

Into accelerated computing is generative AI we're.

We're seeing two simultaneous platform shifts at the same time.

One is accelerated computing and the reason for that is because it's the most cost effective most energy effective and the most performance way of doing computing now.

<unk> bye.

A generative AI just give us your sense of how sustainable is this demand as we look over the next one to two years. So if I take your implied Q3 outlook of datacenter 12, 13 billion, what does that say about how many servers are already accelerated.

And so so what youre seeing and then all of a sudden.

Enabled by generative AI.

Enabled by accelerated computing generative AI came along and this incredible application.

That doing so just give us some confidence that the growth that you're seeing is sustainable into the next one to two years.

Now gives every everyone two reasons to transition to do a platform shift from general purpose computing the classical way of doing computing to this new way of doing computing accelerated computing.

So thanks for that question regarding our supply I guess, we do expect to continue increasing ramping our supply over the next quarters as well as into our next.

It was about a trillion dollars worth of data centers call. It a quarter of a trillion dollars of.

Capital spend each year.

<unk>.

Next fiscal year in terms of percentage not something that we have here it is a.

Youre seeing that the data centers around the world are taking that capital spend and focusing it on the two most important trends of computing today accelerated computing in June to the yard and so so I think this is not a this is not a.

Our work across so many different suppliers, so many different parts of building and.

And <unk> and many of our other new products that are coming to market.

A near term thing this is a long term industry transition.

But we are very pleased with both the support that we have with our suppliers.

And we're seeing these two platform shifts happening at the same time.

And a long time that we've spent with them improving and supply.

Okay.

Next we go to Stacy <unk> with Bernstein Research your line is open.

The World has something along the lines of about a trillion dollars worth of data centers installed in a cloud and enterprise and otherwise.

Hey, guys. Thanks for taking my question I was wondering Colette if you could tell me like how much of data center in the quarter, maybe even the guidance like systems versus GPU like DG Act versus just the eight to 100 and what I'm really trying to get at is how much is like pricing or content to wherever you want to define that.

The trillion dollars of data centers is in the process of transitioning.

Into accelerated computing is generative AI we're.

We're seeing two simultaneous platform shifts at the same time.

Versus units actually driving the growth going forward can you give us any color on that.

One is accelerated computing and the reason for that is because it's the most cost effective most energy effective and the most performance way of doing computing now.

Sure Stacy, let me help within the quarter.

<unk> systems, where a very significant part of our data center as well as our data center growth.

And so so what youre seeing and then all of a sudden.

Enabled by generative AI.

Team.

Enabled by accelerated computing generative AI team alone and this incredible application.

Those systems include our <unk> of our Hopper architecture, but also our counter architecture, yes, we are still selling both of these architectures or in the market.

Now gives every everyone two reasons to transition to do a platform shift from general purpose computing the classical way of doing computing to this new way of doing computing accelerated computing.

Now when you think about that what that does that mean from both the systems of a unit of course is growing quite substantially.

It was about a trillion dollars worth of data centers call. It a quarter of a trillion dollars of of.

And that is driving in terms of the revenue increases. So both of these things are the drivers.

Capital spend each year.

<unk>.

Youre seeing that the datacenters around the world are taking that capital spend and focusing it on the two most important trends of computing today accelerated computing in June to the yard and so so I think this is not a this is not a.

Of the revenue inside data center or <unk> are always a portion of additional systems that we will sell those are great opportunities.

For enterprise customers.

And many other different types of customers that we're seeing even in our consumer internet companies and the importance of there is also coming together with software that we sell with our <unk>, but that's a portion.

<unk>.

A near term thing this is a long term industry transition.

And we're seeing these two platform shifts happening at the same time.

Okay.

Next we go to Stacy <unk> with Bernstein Research your line is open.

All of our sales that we're doing the rest of the Gpus, we have new Gpus coming to market that we talk about the <unk> 40 F.

Hey, guys. Thanks for taking my question I was wondering Colette if you could tell me like how much of data center in the quarter, maybe even the guidance like systems versus GPU like D. G Act versus just the eight to 100 and what I'm really trying to get at is how much is like pricing or content. There wherever you want to define that.

And they will continue.

Continued growth going forward.

But again the largest driver of our revenue within this last quarter was definitely the HD X system.

And Stacy if I could just add something.

You say, it's H 100, and I know you know what your.

Versus units actually driving the growth going forward can you give us any color on that.

<unk> image in your mind, but.

The H 135000 parts.

Sure Stacy, let me help within the quarter.

70 pounds.

<unk> systems.

There are very significant part of our data center as well as our data center growth.

Nearly a trillion transistors in combination.

It takes a robot to build many robots to build.

<unk> those systems include our <unk> of our Hopper architecture, but also our Panther architecture, Yes, we are still selling both of these architectures or in the market now.

Because there's 70 pounds to lift.

And it takes a supercomputer to test the supercomputer.

And so these things are technology marvels.

Now when you think about that what does that mean from both the systems as a unit of course is growing quite substantially.

And.

The manufacturing of them is really intensive and so so I think we call. It <unk> hundred as if it's a chip that comes off of the fab, but H. One hundred's go go out really as <unk> hyperscale or.

And that is driving in terms of the revenue increases. So both of these things are the drivers.

Of the revenue inside data center or <unk> are always a portion of additional systems that we will sell those are great opportunities.

And so they're really really quite large system components, if you will.

Next we go to Mark <unk> with Jefferies. Your line is now open.

For enterprise customers.

Hi, Thanks for taking my question and congrats on the on the success.

Jensen it seems like.

A key part of the success your success in the market is delivering the software ecosystem, along with the chip and the hardware platform and I had a two part question on this.

All of our sales that were doing the rest of the Gpus, we have new gpus coming to market that we talked about the <unk> 40 of them and.

Wondering if you could just help us understand.

And they will continue.

The evolution of your software ecosystem, the critical elements and is there a way to quantify your lead on this dimension like how many person years, you've invested in building. It and then part two I was.

Continued growth going forward.

But again the largest driver of our revenue within this last quarter was definitely the HD X system.

And Stacy if I could just add something.

You you say, it's H 100, and I know you know what your.

Just wondering if you would.

Fair to share with us your view on what percentage of the value of the Nvidia platform as hardware differentiation versus software differentiation. Thank you.

<unk> image in your mind, but.

The <unk> hundred is 35000 parts.

70 pounds.

Yes, Mark we appreciate the questions.

Nearly a trillion transistors in combination.

Let me see if I could use some metrics. So we have a run times claim.

It takes a robot to build what many robots to build.

The price. This is one part of our software stack.

Because of 70 pounds to lift.

And this is this is a if you will the run time.

And it takes a supercomputer to test the supercomputer.

Just about every company uses for the end to end machine learning from data processing.

And so these these things our technology marvels.

And.

The manufacturing of them is really intensive and so so I think we call. It <unk> hundred as if it's a chip that comes off of the SAB, but each one hundred's go go out really as <unk> hyperscale or.

The training of any model to you.

You'd like to do on any framework you would like to do.

The inference.

And the deployment the scaling it out into a datacenter could be of scale out for a hyperscale data center could be of scale out for enterprise data Center for example on Vmware.

And so they're really really quite large system components, if you will.

You can do this on any of our Gpus, we have hundreds of millions of Gpus in the field.

Next we go to Mark <unk> with Jefferies. Your line is now open.

And millions of Gpus in the cloud and just about every single cloud.

Hi, Thanks for taking my question and congrats on the on the success.

<unk>.

Hi.

It runs in a single GPU configuration.

Gentlemen, it seems like.

As well as multi GPU per compute or multi node.

A key part of the success your success in the market is delivering the software ecosystem, along with the chip and the hardware platform and I had a two part question on this.

It also has <unk>.

Multiple.

Multiple sessions over multiple multiple computing instances per GPU. So from multiple instances per GPU to multiple gpus multiple nodes to entire datacenter scale. So.

Wondering if you could just help us understand.

So this runtime Colombia enterprise has.

Something like 4500 software packages software libraries.

And has something like 10000 dependencies.

Just wondering if you would.

Fair to share with us your view on what percentage of the value of the Nvidia platform as hardware differentiation versus software differentiation. Thank you.

Among each other and the run time is is as I mentioned continuously updated.

And optimized for for our installed base for our stock.

Yes, Mark we appreciate the questions.

Let me see if I could use some metrics. So we have a run times claim.

Just one example of what it would take to get accelerated computing to work the number of.

The price. This is one part of our software stack.

Code combinations and type of application combinations is really quite insane.

And this is this is a if you will the run time that just about every company uses for the end to end of machine learning from data processing.

It's taken us two decades to get here.

But.

What I would what I would what I would characterize as probably are are the elements of our <unk>.

The training of any model to you.

You'd like to do on any framework you would like to do.

Of our of our company. If you will are several I would say number one is architecture.

The inference.

And the deployment.

Flexibility diverse agility and the performance of our architecture makes it possible for us.

Scaling it out into data center it could be a scale out for a hyperscale data center it could be a scale out for enterprise data Center for example on Vmware.

To do all the things that I just said.

From data processing to training to inference.

Preprocessing of the data before you do the inference to the post processing of the data.

You can do this on any of our Gpus, we have hundreds of millions of Gpus in the field.

<unk>.

And millions of Gpus in the cloud and just about every single cloud.

Okay.

Languages. So that you could then train train with it the amount of the workflow was much more intense than just training or inference, but anyway.

<unk>.

Hi.

It runs in a single GPU configuration.

As well as multi GPU per compute or multi node.

We will focus on this claim but but when people actually use these computing systems.

It also has <unk>.

Multiple.

Quite John requires a lot of applications and so the combination of our architecture makes it possible for us to deliver the lowest cost of ownership and the reason for that is because because we accelerate so many different things.

Multiple sessions or multiple multiple computing instances per GPU. So from multiple instances per GPU to multiple gpus multiple nodes to entire datacenter scale. So.

So this runtime Colombia enterprise has.

The second characteristic of our of our company as the installed base.

Something like 4500 software packages software libraries.

You have to ask yourself why is it that all of the software developers come to our platform and the reason for that is because software developers seek a large installed base. So that they can reach the largest number of end users. So that they could build a business or you get a return on the investments that they make.

And has something like 10000 dependencies.

Among each other and the run time is is as I mentioned continuously updated and optimized for for our installed base for our stack.

Just one example of what it would take to get accelerated computing to work the number of.

And then the third characteristic is reach.

We're in the cloud today.

Both for public cloud public facing cloud because we have so many customers that use so many developers and customers that use our platform csp's or delays to put it up in the cloud.

Code combinations and type of application combinations is really quite insane.

It's taken us two decades to get here.

But.

What I would what I would what I would characterize as probably are are the elements of our.

Use it for internal consumption.

To develop and train and to operate recommend or systems or <unk>.

Of our of our company. If you will are several I would say number one is architecture.

Search where data processing engines, and we're not always training and inference.

Flexibility diversity and the performance of our architecture makes it possible for us to do all the things that I just said.

And so we are in the cloud where an enterprise yesterday, we had a very big announcement, it's really worthwhile to take a look at that.

From data processing to training to inference.

Where is the operating system of the world's enterprise.

Preprocessing of the data before you do the inference to the post processing of the data.

We've been working together for several years now.

<unk>.

We're going to bring together together, we're going to bring generative AI to the world's enterprises.

Hum.

Languages. So that you could then train train with it the amount of the workflow was much more intense than just training or inference, but anyway.

All the way out to the edge and so reach is another reason and because of reach all of the world's system makers are anxious to put envious.

We will focus in this fine, but but when people actually use these computer systems is key.

Our platform in their systems and so we have.

<unk> requires a lot of.

Very broad distribution from all of the world's Oems.

Locations and so the combination of our architecture makes it possible for us to deliver the lowest cost of ownership and the reason for that is because because we accelerates so many different things.

Odm's and so on and so forth because of our reach and then lastly, because of our scale and velocity.

We're able to sustain.

The second characteristic of our company as the installed base.

This really complex stack of software and hardware networking and compute and across all of these different.

Usage models.

And different computing environments.

And we're able to do all this.

While accelerating the velocity of our engineering it seems like we're introducing a new architecture. Every two years now we are introducing a new architecture, new new product just about every six months.

And then the third characteristic is reach.

We are in the cloud today.

Both for public cloud public facing cloud because we have so many customers that use so many developers and customers that use our platform csp's or delays to put it up in the cloud.

And so these these properties make it possible for the.

The ecosystem to build their company and their business on top of Us and.

They use it for internal consumption.

And so those in.

In combination makes us special.

Developer and train and to operate recommend or systems or.

Serge who are data processing engines, and when not all the way to training and inference.

Next we'll go to <unk> Malik with Citi. Your line is open.

And so we are in the cloud where an enterprise yesterday, we had a very big announcement.

Hi, Thank you for taking my question and great job on the results and outlook.

I have a question on the core work, let Alex talk to yesterday, you guys talked about.

Worthwhile to take a look at that.

Where is the operating system of the world's enterprise and we've been working together for several years now.

Any idea how much of the supply tightness Ken.

We're going to bring together together going to bring generative AI to the world's enterprises.

Our Cody.

It's helped with and if you can talk about the incremental profitability of gross margin contribution from this product. Thank you.

All the way out to the edge and so reach is another reason and because of reach all of the world's system makers are anxious to put envious.

Yes.

Let me, let me take that.

Yes.

Platform in their systems and so we have a very broad distribution from all of the world's Oems.

<unk> 40, <unk> is really designed for a different type of application.

H 100 is designed for large scale.

Odm's and so on and so forth because of our reach and then lastly, because of our scale and velocity.

Language models and.

Processing, just very large models and a great deal of data and so that's that's not <unk> focus all 40 is focus.

We were we're able to sustain this.

This really complex stack of software and hardware networking and compute and across all of these different.

To be able to fine tune models fine tune prescreen models, and they'll do that incredibly well and has a transformer engine has got a lot of performance.

Usage models.

And different computing environments.

And we're able to do all this.

You can get multiple gpus in the server.

It's designed for before.

While accelerating the velocity of our engineering and it seems like we're introducing a new architecture. Every two years now we are introducing a new architecture.

Hyperscale scale out meaning.

It's easy to two.

<unk>.

Install <unk> servers into the world's Hyperscale data centers. It comes into standard rack standard server and.

New product just about every six months.

So these these properties make it possible for.

The ecosystem to build their company and their business on top of us.

Everything about as a standard.

And so it's easy to install <unk>.

And so those in.

<unk> combination makes us special.

<unk> also is with the software stack around it and along with Bluefield III.

Next we'll go to <unk> Malik with Citi. Your line is open.

And although the work that we did with Vmware and the work that we did with <unk>.

Hi, Thank you for taking my question and great job on the results and outlook.

Snow Snowflakes, and and service now and so many other <unk>.

I have a question on the core work less Alex talked yesterday, you guys talked about.

Enterprise partners.

40 us as designed.

Any idea how much of the supply tightness Ken.

For the world's enterprise it systems and Thats, the reason why HP, Dell and Lenovo and.

Our 40.

Helped with and if you can talk about the incremental profitability of gross margin contribution.

Some up 20 other system makers.

Building about 100 different configurations of enterprise servers are going to work with us to take generative AI to the world's enterprise.

Thank you.

Yeah, Let me, let me take that.

Yes.

<unk> 40, <unk> is really designed for a different type of application.

So <unk> is really designed for four.

A difference different type of scale out if you will so of course large language models, it's of course generative AI.

<unk> hundred is designed for large scale.

Language models and.

But it's a different use case and so the <unk> is going to is off to a great start and.

Processing, just very large models and a great deal of data and so that's that's not <unk> focus all 40. His focus is to be able to fine tune models fine tune prescreen models.

The world's enterprise and Hyperscale is are really clamoring to get.

You get <unk> deployed.

<unk> deployed.

We will do that incredibly well and has a transformer engines got a lot of performance.

You can get multiple gpus in the server.

Next we will go to Joe Moore with Morgan Stanley . Your line is now open.

It's designed for before.

Hyperscale scale out meaning.

Great. Thank you.

I guess the thing about these numbers that show remarkable.

It's easy to two.

Two.

Install L 40 S servers into the world's Hyperscale data centers, it's comes into standard rack.

To me is the amount of demand that remains unfulfilled talking to some of your customers.

It's good as these numbers are you sort of more than tripled.

Standard server.

<unk>.

Our revenue in a couple of quarters.

Everything about it is standard.

And so it's easy to install.

There is demand in some cases for for multiples of what people are getting so can you talk about that how much unfulfilled demand.

<unk> also is with the software stack around it and along with Bluefield III.

There is and you talked about visibility extending into next year do you have line of sight into when you'll get to see supply demand equilibrium here.

And although the work that we did with Vmware and the work that we did with <unk>.

Snow Snowflakes, and and service now and so many other.

Okay.

Yes, we have excellent visibility.

Enterprise partners.

Through the year and into next year.

40 us as designed.

And we're already planning.

For the world's enterprise it systems and Thats, the reason why HP, Dell and Lenovo and.

The next generation infrastructure with the leading Csp's in datacenter builders.

Some of 'twenty other system makers.

The demand the easiest way to think about the demand.

Building about 100 different configurations of enterprise servers are going to work with us to take generative AI to the world's enterprise and so <unk> is really designed for four.

Is the world is transitioning from general purpose computing.

Two accelerated computing, that's the easiest way to think about the demand.

The best way for companies to increase their throughput.

A difference different type of scale. If you will is of course larger language models. It's of course generative AI.

Improve their energy efficiency improve their cost efficiency.

But it's a different use case and so the <unk> is going to is off to a great start and.

As to divert their capital budget to accelerated computing and generous of AI.

By doing that Youre going to offload so much workload off of the Cpus that the available Cpus.

The world's enterprise and Hyperscale is are really clamoring to get.

You get <unk> deployed.

<unk> deployed.

In your data center will get boosted.

And so.

Next we'll go to Joe Moore with Morgan Stanley . Your line is now open.

What youre seeing companies do now is recognizing this this tipping.

Great. Thank you.

Tipping point here recognizing the beginning of this transition and diverting their capital investment to accelerated computing and <unk>.

I guess the thing about these numbers that show remarkable.

He is the amount of demand that remains unfulfilled talking to some of your customers.

So that's probably the easiest way to think about the opportunity ahead of us. This isn't a a singular application that does that is driving the demand, but this is a new computing.

As good as these numbers are you sort of more than tripled.

Your revenue in a couple of quarters.

There is demand in some cases for for multiples of what people are getting so can you talk about that how much unfulfilled demand do you think there is and you talked about visibility extending into next year do you have line of sight into when you'll get to see supply.

Platform, if you will a new computing transition.

That's happening.

Datacenters all over the world are responding to this and.

Shifting.

Supply demand equilibrium here.

And a broad based way.

Okay.

Yes, we have excellent visibility.

Okay next we go to <unk> Hari with Goldman Sachs. Your line is now open.

Through the year and into next year.

And we're already planning the.

Hi, Thank you for taking my question I had one quick clarification question for Colm and then another one for Jonathan.

The next generation infrastructure with the leading Csp's datacenter builders.

The demand the easiest way to think about the demand is the world is transitioning from general purpose computing.

I think last quarter, you had said.

We're about 40% of your data center revenue consumer Internet, 30% enterprise, 30% based on your remarks, it sounded like CSP and consumer Internet may have been a larger percentage of your business. If you can kind of clarify that or confirm that that would be super helpful. And then Jensen.

Two accelerated computing, that's the easiest way to think about the demand.

The best way for companies to increase their throughput.

Improve their energy efficiency improve their cost efficiency.

A question for you.

As to divert their capital budget to accelerated computing and generous of AI.

Given your position as the key enabler of AI, the breath of engagements and the visibility you have into customer projects.

Because by doing that youre going to offload so much workload off of the Cpus that the available Cpus.

I'm curious how confident you are that there will be enough applications or use cases for your customers to generate a reasonable return on their investments.

Is in your data center will get boosted.

And so.

What youre seeing companies do now is recognizing this.

I asked the question because there is a concern out there that.

Tipping point here recognizing the beginning of this transition and diverting their capital investment to accelerated computing adjourn to the AI.

There could be a bit of a pause in your and your demand profile in the out years curious some.

There is enough breadth and depth there to support a sustained.

And so so that's that's probably the easiest way to think about the opportunity ahead of us this isn't a.

Increase in your data center business going forward. Thank you.

A singular application that does that is driving the demand, but this is a new computing.

And so I think that especially on the question regarding <unk>.

Our types of customers that we have in our data center business and we look at it in terms of combining our compute as well as our network and together are csp's or large CSP are contributing a little bit more than 50% of our revenue within Q2.

<unk> platform, if you will a new computing transition.

What's happening.

Datacenters all over the world are responding to this end.

Shifting.

And a broad based way.

Okay next we go to <unk> Hari with Goldman Sachs. Your line is now open.

And the next largest category will be our consumer Internet companies and then the last piece of it will be our enterprise and high performance computing.

Hi, Thank you for taking my question I had one quick clarification question for Colin and then another one for Jonathan.

This year I mean.

I think last quarter, you had said.

Reluctance to guests about the future.

Csp's, we're about 40% of your data center revenue consumer and a 30% enterprise, 30% based on your remarks, it sounded like CSP and consumer Internet may have been a larger percentage of your business. If you can kind of clarify that or confirm that that would be super helpful. And then Jensen.

And so I'll answer the question from.

First principles computer science perspective.

It is it is recognized for some time now that.

General purpose computing is just not an brute, forcing general purpose computing using general purpose computing at scale.

A question for you.

Given your position as the key enabler of AI, the breath of engagements and the visibility you have into customer projects.

It's no longer the best way to go forward it's to energy.

Firstly, it's too expensive.

And the performance of the applications are too slow.

I'm curious how confident you are that there will be enough applications or use cases for your customers to generate a reasonable return on their investments I guess I asked the question because there is a concern out there that.

Alright.

And finally, the world has a new way of doing it it's called accelerated computing and what kicked it into Turbocharges generative AI.

But accelerated computing can be used for all kinds of different applications thats already in the data center and by using it.

There could be a bit of a pause in your and your demand profile in the out years curious.

If there's enough breadth and depth there to support a sustained.

You offload the Cpus, you save a ton of money us an order of magnitude.

The increase in your data center business going forward. Thank you.

In cost in order of magnitude in energy and the throughput is higher.

Okay. So thanks, Sasha on the <unk>.

And.

And that's what that's what the industry is really responding to.

And regarding our types of customers that we have in our data center business and we look at it in terms of combining our compute as well as our networking together are csp's or large CSP are contributing a little bit more than 50% of our revenue within Q2.

Going forward.

The best way to invest in the data center.

As to divert the capital investment from General purpose computing and focus it on generative AI and accelerated computing.

<unk> provides a new way of generating productivity.

<unk>.

And the next largest category will be our consumer Internet companies and then the last piece of it will be our enterprise and high performance computing.

A new way of generating new services to offer to your customers and accelerated computing helps you save money and save power.

And and the number of applications as well.

Sure.

I'm reluctant to guests about the future.

Well.

Tonnes lots.

Lots of developers lots of applications lots of libraries.

And so I'll answer the question from.

Is ready to be deployed.

The first principle computer science perspective.

And so I think I think the data centers around the world recognize this but this is the.

It is it is recognized for some time now that.

The best way to deploy resources to deploy capital going forward for data centers. This is true for the.

General purpose computing is just not an brute, forcing general purpose computing using general purpose computing at scale is no longer the best way to go forward it's to energy.

The world's clouds, and youre seeing a whole crop.

Of new GPU specialty GPU specialized cloud service providers.

Costly, it's too expensive and the performance of the applications are too slow.

The famous ones as core <unk> and they are doing incredibly well.

Hi.

But you're seeing that regional GPU specialists.

And finally, the world has a new way of doing it is called accelerated computing and what kicked it into Turbocharges generative AI.

Service providers all over the world now.

And.

Because they all recognize the same thing that the best way to invest your capital going forward is to put it into accelerated computing and regenerative healing, but we're also seeing that the enterprises want to do that.

But accelerated computing can be used for all kinds of different applications thats already in the data center.

By using it.

You offload the Cpus, you save a ton of money us an order of magnitude.

But in order for enterprises to do it you have to support the management system, the operating system for security and software.

In cost in order of magnitude in energy and the throughput is higher.

And.

Software defined data center approach of enterprises, and that's called Vmware and we've been working several years with Vmware.

And that's what that's what the industry is really responding to.

Going forward.

The best way to invest in the data center.

To make it possible for Vmware to support <unk>.

As to divert the capital investment from General purpose computing and focus it on generative AI and accelerated computing.

Not just the virtualization of Cpus, but the virtualization of Gpus as well as the distributed computing capabilities of Gpus.

Sure to have AI provides a new way of generating productivity.

<unk> and videos Bluefield for high performance networking and all of the generative AI libraries, and we've been working on is now going to be offered as a special skew.

New way of generating new services to offer to your customers and accelerated computing helps you save money and save power.

And.

<unk> sales force.

And the number of applications as well.

As we all know quite large because they reach some.

<unk>.

Tonnes lots.

Lots of developers lots of applications lots of libraries.

Several hundred thousand Vmware customers around the world and this new SKU is going to be called Vmware private AI Foundation and.

Is ready to be deployed.

And so I think I think the data centers around the world recognized us but this is the.

The best way to deploy resources to deploy capital going forward for data centers. This is true for the.

And.

Because there'll be a new SKU that makes it possible for enterprises and in combination with HP Dell and Lenovo.

The world's clouds, and youre seeing a whole crop.

New server offerings based on our 40, yes.

Of new GPU specialty GPU specialized cloud service providers.

Any any enterprise could have a state of the art AI data center and be able to engage generative AI and so I think the.

One of the famous ones as core and Theyre doing incredibly well.

But youre seeing the regional GPU specialists.

The answer to that question is hard to predict exactly what's going to happen quarter to quarter.

Service providers all over the world now.

But I think the trend is very very clear now that were seeing a platform shift.

And.

Because they all recognize the same thing that the best way to invest your capital going forward is to put it it's already computing and regenerative healing.

Next we'll go to Timothy Arcuri with UBS. Your line is now open.

We're also seeing that the enterprises want to do that.

But in order for enterprises to do it you have to support the management system, the operating system for security and software.

Thanks, a lot can you talk about the attach rate of your networking solutions to your.

The compute that Youre shipping.

Software defined data center approach of enterprises, and that's called Vmware and we've been working several years would be aware.

In other words is like half of your compute shipping with your networking solutions.

More than half less than half and is this something that maybe you can use to prioritize allocation of the.

To make it possible for Vmware to support.

Not just the virtualization of Cpus, but the virtualization of Gpus as well as the distributed computing capabilities of Gpus.

The Gpus.

<unk>.

Working backwards, we don't use that to prioritize the allocation of our Gpus.

Supporting and videos Bluefield for high performance networking and all of the generative AI libraries, and we've been working on is now going to be offered as a special skew.

Led customers decide what networking they would like to use.

And.

<unk>.

But for the customers that are building very large infrastructure infiniband is.

By Vmware Salesforce, which is as we all know quite large because they they reach some.

I hate to say it kind of a no brainer and the reason for that.

Because because the efficiency of Infiniband.

Several hundred thousand Vmware.

Around the world.

<unk> is so significant.

This new SKU is going to be called Vmware private AI Foundation and.

Some $10 $15, 20% higher throughput for a $1 billion infra.

And.

Infrastructure translates to enormous savings basically the networking is free.

Because there'll be a new SKU that makes it possible for enterprises and in combination with HP Dell and Lenovo.

And so if you have a.

New server offerings based on our 40, yes.

Single application, if you will infrastructure, where it's largely dedicated to large language models are large AI AI systems Infiniband is really really a terrific choice.

Any any enterprise could have a state of the yard AI data center and be able to engage generative AI and so I think the.

The answer to that question is hard to predict exactly what's going to happen quarter to quarter.

If you're if you're hosting for a lot of different users and and.

But I think the trend is very very clear now that were seeing a platform shift.

Ethernet is really core to the way you manage your data center.

We have we have an excellent solution there that we just recently announced and call spectrum X, where we're going to bring the capabilities. If you will not all of it but but.

Next we'll go to Timothy Arcuri with UBS. Your line is now open.

Thanks, a lot can you talk about the attach rate of your networking solutions to your to the compute that youre shipping.

Some of it of the capabilities of Infiniband.

<unk> Ethernet and so that we can we can also within the.

In other words is like half of your compute shipping with your networking solutions.

Environment of Ethernet allow you to enable you to get.

More than half less than half and is this something that maybe you can use to prioritize allocation of the.

Get excellent generative AI capabilities. So spectrum access is just ramping now.

The Gpus.

It requires bluefield three and supports.

<unk>.

Working backwards, we don't use that to prioritize the obligation of our Gpus.

Both our spectrum two in spectrum <unk> III.

Led customers decide what networking they would like to use.

Ethernet switches.

And the additional performance is really spectacular.

And.

<unk>.

But for the customers that are building very large infrastructure infiniband is.

Bluefield three makes it possible.

And a whole bunch of software that goes along with it.

I hate to say it kind of a no brainer and the reason for that.

Brookfield Brookfield as all of you know as is.

Because because the efficiency of Infiniband.

Project really dear to my heart and.

It's off to just a tremendous start I think it's a homerun.

<unk> is so significant.

Some $10 $15, 20% higher throughput for a $1 billion infrastructure translates to enormous savings basically the networking is free.

Is that the concept of.

In network computing, and putting a lot of software in the computing fabric.

Is being realized with with.

We feel <unk>.

It is going to be a home run.

And so if you have a.

A single application if you will infrastructure, where it's largely dedicated to large language models are large AI AI systems Infiniband is really really a terrific choice.

Our final question comes from the line of Ben Reitzes with Melius. Your line is now open.

Hi, Good afternoon. Good evening. Thank you for the question.

Me in here.

If you're if you're hosting for a lot of different users and and.

My question is with regard to <unk> cloud can you talk about the reception that youre seeing and.

Ethernet is really core to the way you manage your data center.

How the momentum is going and then collect can you also talk about your software business what is the run rate right now.

We have we have an excellent solution. There that we just recently announced ends call spectrum X, where we're going to bring the capabilities. If you will not all of it but but.

<unk>.

The materiality of that business and it does seem like its already helping margins a bit. Thank you very much.

Some of it of the capabilities of Infiniband.

<unk> Ethernet and so that we can we can also within the <unk>.

Gtx clouds strategy, let me start there.

Environment of Ethernet.

<unk> cloud strategy is to achieve several things.

Now you to enable you to get.

<unk> excellent generative AI capabilities. So spectrum access is just ramping now.

Number one.

To enable a really close partnership between us and the World Csp's.

It requires bluefield III and supports.

Right.

We recognize that that many of our.

Both our spectrum, two and spectrum three.

We worked with.

Ethernet switches and <unk>.

From 30000 companies around the world.

Additional performance is really spectacular.

<unk> 15000 of them are startups.

Thousands of Mark generative AI companies and the.

Bluefield III makes it possible.

Kind of a whole bunch of software that goes along with it.

The fastest growing segment of course assurance of AI, we're working with all of the all of the worlds.

Brookfield Brookfield as all of you know is a.

AI startups.

Our project really Dear to my heart and.

<unk>.

It's off to just a tremendous start I think it's a home run.

And ultimately they would like to be able to Atlanta in one of the worlds leading clouds and.

Is that the concept of <unk>.

And so we.

In network computing, and putting a lot of software in the computing fabric.

We.

Bill <unk> cloud as a footprint inside the worlds.

Being realized with with Bluefield III.

Leading clouds.

It is going to be a home run.

So that we could simultaneously work with all of our partners.

Yes.

Our final question comes from the line of Ben Reitzes with Melius. Your line is now open.

And help lend them in easily in one of our cloud partners.

Hi, Good afternoon. Good evening. Thank you for the question putting me in here.

The second benefit is that it allows.

Our CSP and ourselves to work really closely together to improve the performance.

My question is with regard to <unk> cloud can you talk about the reception that youre seeing and.

Hyperscale warehouse, which is historically designed for multi tenancy and not designed for high performance distributed computing like generative AOE.

How the momentum is going and then collect can you also talk about your software business what is the run rate right now and.

And so to be able to work closely architecturally to to.

The materiality of that business and it does seem like its already helping margins a bit.

Our engineers worked hand in hand to improve the networking performance in the computing performance.

Thank you very much.

<unk> has been really powerful really terrific and then thirdly of course, Nvidia uses very large infrastructures ourselves.

Gtx clouds strategy, let me start there.

<unk> cloud strategy is to achieve several things.

One.

And.

To enable a really close partnership between us and the World Csp's.

Our self driving car team are eminent research team, our generative AI team our language model team.

We recognize that that many of our.

The amount of infrastructure that we need is quite quite significant and.

We work with.

I'm 30000 companies around the world of 15000 of them are startups.

None of our none of our optimizing compilers are possible without.

<unk> of them are generative AI companies.

Our <unk> systems, even compilers these days require AI and optimizing software and infrastructure software requires AI to even develop.

<unk>.

The fastest growing segment of course assurance of AI, we're working with all of them all of the worlds.

AI startups.

<unk>.

It's been well publicized that our engineering uses AI to designer chips and.

And ultimately they would like to be able to Atlanta, and one of the worlds leading clouds and.

So the internal our own consumption of AI and robotics team, so and so forth omnivores team so on and so forth all of these AI and so.

And so we.

We built.

Bill <unk> cloud as a footprint inside the worlds lead.

Leading clouds.

So that we could simultaneously work with all of our partners.

So our internal consumption is quite large as well and we land.

In <unk> cloud and so DCF Todd has multiple multiple use cases multiple drivers.

And helped land them in easily in one of our cloud partners.

The second benefit is that it allows.

And it's been off to a just an enormous success.

Our CSP and ourselves to work really closely together to improve the performance.

R. R R Csp's love it.

Developers love it and our own internal engineers are clamoring to have more of it.

Hyperscale warehouse, which is historically designed for multi tenancy and not designed for high performance distributed computing like generative AI.

It's a great way for us to engage and work closely with all of the ecosystem around the world.

And so to be able to work closely architecturally to to have.

And let's see if I can answer your question regarding our software revenue.

Our engineers worked hand in hand to improve the networking performance in the computing performance.

And part of our opening remarks, we made as well.

Part of the part of almost all of our products, whether they are data center products and GPU system.

It has been really powerful really terrific and then thirdly of course, Nvidia uses very large infrastructure ourselves.

Any of our products within gaming and our future automotive products.

And.

Our self driving car team are Nvidia research team, our generative AI team our language model team.

Correct, we're also southern Europe .

Standalone.

And that Standalone software continues to grow where we are providing both the software services upgrades.

The amount of infrastructure that we need is quite quite significant and.

None of our none of our optimizing compilers are possible without our.

Across there as well now we're seeing at this point, probably hundreds of millions of dollars annually for the software business and we are looking at Nvidia AI enterprise to be included.

Our <unk> systems, even compilers these days require AI and optimizing software and infrastructure software requires AI even develop.

It's been well publicized that our engineering uses AI to designer chips and.

With many of the products that we're selling such as our <unk> such as our Pcie versions of our <unk> 100, and I think we're going to see more availability with our CSP marketplaces. So we're off to a great start and I do believe you will see this continue to grow going forward.

So.

The internal our own consumption of AI and robotics team, so on and so forth omnivores team, so on and so forth all needs AI and so.

So our internal consumption is quite large as well and we land down in.

<unk> cloud and so <unk> has multiple multiple use cases multiple drivers.

And that does conclude today's question and answer session I'll turn the call back over to Jensen Huang for any additional or closing remarks.

It's been off to adjust the enormous success.

A new computing era has begun.

R. R R Csp's love it.

The industry is simultaneously going through two platform transitions accelerated computing and generative AI.

Developers love it and our own internal engineers are clamoring to have more of it.

Data centers are making a platform shift from general purpose to accelerated computing.

It's a great way for us to engage and work closely with all of the AI ecosystem around the world.

The trillion dollars of global data centers will transition to.

And let's see if I can answer your question regarding our software revenue.

Two accelerated computing to achieve an order of magnitude better performance energy efficiency and cost.

And part of our opening remarks, we made as well bring on the software as a part of almost all of our products whether they are data center.

The accelerated computing enabled generative AI, which is now driving a platform shift in software.

Product GPU system or any of our products within gaming and our future automotive products.

And enabling new never before possible applications.

Together accelerated computing and generative AI are driving a broad based computer industry platform shift.

Correct, we're also southern Europe .

Standalone.

Let stand alone software continues to grow where we are providing both the software services upgrades.

Our demand is tremendous.

We're significantly expanding our production capacity.

Supply will substantially increase for the rest of this year and next year.

<unk> there as well now we're seeing at this point, probably hundreds of millions of dollars annually for the software business.

Nobody has been preparing for this for over two decades and has created a new computing platform that the worlds industry.

And we are looking at Nvidia AI enterprise to be included.

With many of the products that we're selling such as <unk> such as our Pcie versions of our <unk> 100, and I think we're gonna see more availability with our CSP marketplaces. So we're off to a great start and I do believe we will see this continue to grow going forward.

World's industries can build upon.

What makes a video special or one architecture, Nvidia accelerates everything from data processing training inference every.

Our model real time speech to computer vision and giant recommended to vector databases.

The performance and versatility of our architecture translates to the lowest datacenter TCR and best energy efficiency.

And that does conclude today's question and answer session I will turn the call back over to Jensen Huang for any additional or closing remarks.

To install base Nvidia has hundreds of millions of cuda compatible Gpus worldwide.

A new computing era has begun.

The industry is simultaneously going through two platform transitions accelerated computing and generative AI.

Developers need a large installed base to reach end users and grow their business Nvidia is the developers preferred platform.

Data centers are making a platform shift from general purpose to accelerated computing.

More developers.

More applications that make nvidia more valuable for customers.

The trillion dollars of global data centers will transition to.

Two accelerated computing to achieve an order of magnitude better performance energy efficiency and cost.

Three reach and videos and clouds enterprise Datacenters industrial edge Pcs workstations instruments and robotics, each has fundamentally unique computing models and ecosystems.

Accelerated computing enabled generative AI, which is now driving a platform shift in software.

And enabling new never before possible applications.

System suppliers like Oems computer Oems can confidently invest in Nvidia, because we offer significant market demand and reach.

Together accelerated computing and generative AI are driving a broad based computer industry platform shift.

Our demand is tremendous.

Scale and velocity.

Nvidia has achieved significant significant scale and is 100% invested and accelerated computing insurer to AI.

We are significantly expanding our production capacity supply will substantially increase for the rest of this year and next year.

Our ecosystem partners can trust that we have the expertise.

Everybody has been preparing for this for over two decades and has created a new computing platform that the worlds industry.

Focus and scale to deliver a strong roadmap and reach to help them grow.

Okay.

World's industries can build upon one.

We are accelerating.

What makes some video special or one architecture, Nvidia accelerates everything from data processing training inference every AI model real time speech to computer vision and giant recommended to vector databases.

Those are the additive results of these capabilities.

We're upgrading and adding new products about every six months versus every two years to address the expanding universe of generative AI.

While we increase the output of <unk> 100 for training and inference of large language models, we're ramping up our new <unk> 40 S Universal GPU.

The performance and versatility of our architecture translates to the lowest datacenter TCR and best energy efficiency.

For scale for cloud scale out and enterprise servers spectrum X, which consists of our Ethernet switch Bluefield III Super Nick and software helps customers, who want the best possible AI performance on Ethernet infrastructures.

To install base Nvidia has hundreds of millions of cuda compatible Gpus worldwide.

<unk> need a large installed base to reach end users and grow their business and Nvidia is the developers preferred platform.

Customers are already working on next generation accelerated computing and generative AI with our Grace Hopper.

More developers Cree.

Create more applications that make nvidia more valuable for customers.

We're extending Nvidia AI to the world's enterprises demand generative AI, but with the model privacy security and sovereignty.

Three reach and videos and clouds enterprise Datacenters industrial edge, Pcs workstations instruments and robotics.

Together with the worlds leading enterprise companies.

Each has fundamentally unique computing models and ecosystems.

Accenture Adobe Getty hugging face Snow Flake service now Vmware and W. P P and.

System suppliers like Oems computer Oems can confidently invest in Nvidia, because we offer significant market demand and reach.

In our enterprise system partners, Dell HP and Lenovo, we are bringing generative AI to the world's enterprise were.

Scale and velocity.

<unk> has achieved significant significant scale and is 100% invested and accelerated computing insurance of AI our.

We're building Nvidia omnivores to digitalize and enabled the worlds multi trillion dollar heavy industries to use generative AI to automate how they build and operate physical assets and achieve greater productivity.

Our ecosystem partners can trust.

We have the expertise focus and scale to deliver a strong roadmap and reach to help them grow.

Generative AI starts in the cloud but.

We are accelerating because of the additive results of these capabilities.

But the most significant opportunities are in the world's largest industries, where companies can realize trillions of dollars of productivity gains.

We're upgrading and adding new products about every six months versus every two years to address the expanding universe of generative AI.

It is an exciting time for Nvidia, our customers partners and the entire ecosystem to drive this generational shift in computing.

While we increase the output of H 100 for training and inference of large language models, we're ramping up our new <unk> <unk> 40 S Universal GPU for.

Look forward to updating you on our progress next quarter.

Okay.

This concludes today's conference call you may now disconnect.

Customers are already working on next generation accelerated computing and generative AI with our Grace Hopper.

We're extending Nvidia AI to the world's enterprises that demand generative AI, but with the model privacy security and sovereignty.

Together with the worlds leading enterprise companies.

Accenture Adobe Getty hugging face Snow Flake service now Vmware and W. P P.

And our enterprise system partners, Dell HP and Lenovo, we are bringing generative AI to the world's enterprise or.

Generative AI starts in the cloud.

But the most significant opportunities are in the world's largest industries, where companies can realize trillions of dollars of productivity gains.

It is an exciting time for Nvidia, our customers partners and the entire ecosystem to drive this generational shift in computing.

Look forward to updating you on our progress next quarter.

Okay.

This concludes today's conference call you may now disconnect.

Yeah.

Okay.

Yeah.

Okay.

Yeah.

Q2 2024 NVIDIA Corp Earnings Call

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