Thread by @EugeneNg_VCap

NVIDIA $NVDA 2Q24 Earnings 🚀

- Rev $13.5b +101% 🚀
- GP $9.5b +225% 🚀 margin 70% +2657 bps ✅
- EBIT $6.8b +1263% 🚀 margin 50% +4290 bps ✅
- NG EBIT $7.8b +155% 🚀 margin 58% +1205 bps ✅
- Net Inc $6.2b +843% 🚀 margin 46% +3603 bps ✅
- NG Net Inc $6.7b +148% 🚀 margin 50% +943 bps ✅
- OCF $6.4b +407% 🚀 margin 48% +2872 bps ✅
- FCF $6b +129% 🚀 margin 45% +535 bps ✅

Rev by Market Platform
- Total Rev $13.5b +101% 🚀 +88% QoQ ⤴️
- ⭐️ Data Center Rev $10.3b +171% 🚀 +141% QoQ ⤴️
- Gaming $2.5b +22% ↗️🟢 + 11% QoQ ⤴️
- Prof Visualization Rev $379m -24% ↘️🔴 +28% QoQ ⤴️🟢
- Automotive Rev $253m +15% YoY ↗️🟡 -15% QoQ ↘️🟠

Revenue by Segment
- Compute & Networking Rev $10.4b +166% 🚀 +133% QoQ ⤴️
- Graphics Rev $3.1b +11% ↗️🟡 +14%

Mgmt Guide - 3Q24

- Rev $16bn + 170% 🚀🟢
- GPM 71.5%
- OPEX $2.95b
- EBIT $8.5b (est)

Increasing shareholder returns via share buybacks

- $3.4bn return to shareholders

- $3.3bn share buybacks, rest cash dividends

- Board approved an additional $25bn of share buybacks

𝟭 | 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗾𝘂𝗮𝗿𝘁𝗲𝗿 𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘆 𝗱𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲, $𝟭𝟯.𝟱𝗯 𝗥𝗲𝘃 > $𝟭𝟭𝗯 𝗴𝘂𝗶𝗱𝗲

We had an exceptional quarter. Record Q2 revenue of $13.51 billion was up 88% sequentially and up 101% year-on-year and above our outlook of $11 billion.

Data Center compute revenue nearly tripled YoY, driven primarily by accelerating demand from cloud service providers and large consumer Internet companies for our HGX platform, the engine of generative AI and large language models.

𝟮 | 𝗦𝘁𝗲𝗹𝗹𝗮𝗿 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿 - 𝗹𝗲𝗱 𝗯𝘆 𝗟𝗟𝗠 𝗮𝗻𝗱 𝗚𝗲𝗻𝗔𝗜

Data Center revenue was a record, up 171% YoY and up 141% QoQ, led by cloud service providers and large consumer internet companies. Strong demand for the NVIDIA HGX platform based on our Hopper and Ampere GPU architectures was primarily driven by the development of large language models and generative AI.

Data Center Compute grew 195% YoY and 157% QoQ, largely reflecting the strong ramp of our Hopper-based HGX platform. Networking was up 94% YoY and up 85% QoQ, primarily on strong growth in InfiniBand infrastructure to support our HGX platform.

𝟯 | 𝗚𝗼𝗼𝗱 𝗺𝗶𝘅 𝗳𝗿𝗼𝗺 𝗖𝗦𝗣𝘀, 𝗖𝗜𝗣𝘀, 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗮𝗻𝗱 𝗛𝗣𝗖

…our large CSPs are contributing a little bit more than 50% of our revenue within Q2. And the next largest category will be our consumer Internet companies. And then the last piece of that will be our enterprise and high-performance computing

𝟰 | 𝗛𝗶𝗴𝗵𝗲𝗿 𝗴𝗮𝗺𝗶𝗻𝗴 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗼𝗻, 𝗱𝗲𝗺𝗮𝗻𝗱 𝗿𝗲𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘂𝗽𝗴𝗿𝗮𝗱𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆

Gaming revenue was up 22% YoY and up 11% QoQ, primarily reflecting demand for our GeForce RTX 40 Series GPUs based on the NVIDIA Ada Lovelace architecture following normalization of channel inventory levels.

We believe global end demand has returned to growth after last year's slowdown. We have a large upgrade opportunity ahead of us…Q2 and Q3 as the stronger quarters of the year, reflecting the back-to-school and holiday build schedules for laptops.

𝟱 | 𝗟𝗼𝘄𝗲𝗿 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗱𝘂𝗲 𝘁𝗼 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Professional Visualization revenue was down 24% YoY and up 28% QoQ. The YoY decrease primarily reflects lower sell-in to partners following normalization of channel inventory levels. The sequential increase was primarily due to stronger enterprise workstation demand and the ramp of NVIDIA RTX products based on the Ada Lovelace Architecture.

𝟲 | 𝗔𝘂𝘁𝗼 𝗹𝗼𝘄𝗲𝗿 𝗱𝘂𝗲 𝘁𝗼 𝗹𝗼𝘄𝗲𝗿 𝗖𝗵𝗶𝗻𝗮 𝗱𝗲𝗺𝗮𝗻𝗱

Automotive revenue was up 15% YoY and down 15% QoQ. The YoY increase was primarily driven by sales of self-driving platforms. The sequential decrease primarily reflects lower overall auto demand, particularly in China.

𝟳 | 𝗨𝗦 𝗲𝘅𝗽𝗼𝗿𝘁 𝗿𝗲𝘀𝘁𝗿𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿 𝗚𝗣𝗨𝘀 𝘁𝗼 𝗖𝗵𝗶𝗻𝗮 𝗶𝗺𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝘁𝗼 𝗡𝗩𝗜𝗗𝗜𝗔 𝗯𝘂𝘁 𝗯𝗶𝗴𝗴𝗲𝗿 𝗽𝗲𝗿𝗺𝗮𝗻𝗲𝗻𝘁 𝗹𝗼𝘀𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗳𝗼𝗿 𝗨𝗦

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. However, over the long term, restrictions prohibiting the sale of our Data Center GPUs to China, if implemented, will result in a permanent loss and opportunity for the U.S. industry to compete and lead in one of the world's largest markets.

𝟴 | 𝗠𝗮𝗷𝗼𝗿 𝗰𝗹𝗼𝘂𝗱𝘀 𝗮𝗿𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗚𝗣𝗨𝘀

Major companies, including AWS, Google Cloud, Meta, Microsoft Azure and Oracle Cloud as well as a growing number of GPU cloud providers are deploying, in volume, HGX systems based on our Hopper and Ampere architecture Tensor Core GPUs. Networking revenue almost doubled year-on-year, driven by our end-to-end InfiniBand networking platform, the gold standard for AI.

𝟵 | 𝗜𝘀𝘀𝘂𝗲 𝘄𝗶𝘁𝗵 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗽𝘂𝗿𝗽𝗼𝘀𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 - 𝘁𝗼𝗼 𝗲𝗻𝗲𝗿𝗴𝘆 𝗰𝗼𝘀𝘁𝗹𝘆, 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝘀𝗹𝗼𝘄, 𝗯𝗲𝘀𝘁 𝘁𝗼 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗴𝗲𝗻 𝗔𝗜 𝗮𝗻𝗱 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴

that general purpose computing is just not and brute forcing general purpose computing. Using general purpose computing at scale is no longer the best way to go forward. It's too energy costly, it's too expensive, and the performance of the applications are too slow.

Going forward, the best way to invest in the data center is to divert the capital investment from general purpose computing and focus it on generative AI and accelerated computing.

𝟭𝟬 | 𝗧𝗿𝗲𝗺𝗲𝗻𝗱𝗼𝘂𝘀 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗡𝗩𝗜𝗗𝗜𝗔 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀

There is tremendous demand for NVIDIA accelerated computing and AI platforms. Our supply partners have been exceptional in ramping capacity to support our needs. Our data center supply chain, including HGX with 35,000 parts and highly complex networking has been built up over the past decade.

With the NVIDIA NeMo platform for developing large language models, enterprises will be able to make custom LLMs for advanced AI services, including chatbot, search and summarization, right from the Snowflake Data Cloud. Virtually, every industry can benefit from generative AI.

𝟭𝟭 | 𝗘𝘅𝗽𝗲𝗰𝘁 𝘀𝘂𝗽𝗽𝗹𝘆 𝘁𝗼 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗲𝗮𝗰𝗵 𝗾𝘂𝗮𝗿𝘁𝗲𝗿 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗻𝗲𝘅𝘁 𝘆𝗲𝗮𝗿

We have also developed and qualified additional capacity and suppliers for key steps in the manufacturing process such as packaging. We expect supply to increase each quarter through next year. By geography, data center growth was strongest in the U.S. as customers direct their capital investments to AI and accelerated computing.

𝟭𝟮 | 𝗦𝗲𝗲𝗶𝗻𝗴 𝗴𝗼𝗼𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗼𝗳 𝗴𝗲𝗻 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗪𝗣𝗣, 𝗦𝗵𝘂𝘁𝘁𝗲𝗿𝘀𝘁𝗼𝗰𝗸 𝗮𝗻𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗡𝗼𝘄

We are seeing some of the earliest applications of generative AI in marketing, media and entertainment.

WPP, the world's largest marketing and communication services organization, is developing a content engine using NVIDIA Omniverse to enable artists and designers to integrate generative AI into 3D content creation.

Visual content provider Shutterstock is also using NVIDIA Picasso to build tools and services that enables users to create 3D scene background with the help of generative AI.

AI Lighthouse unites the ServiceNow enterprise automation platform and engine with NVIDIA accelerated computing and with Accenture consulting and deployment services.

𝟭𝟯 | 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱 𝗻𝗲𝘄 𝗡𝗩𝗜𝗗𝗜𝗔 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗿𝗲𝗮𝗱𝘆 𝗟𝟰𝟬𝗦 𝗚𝗣𝗨 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 𝗮𝗻𝗱 𝗕𝗹𝘂𝗲𝗙𝗶𝗲𝗹𝗱-𝟯

We also announced new NVIDIA AI enterprise-ready servers featuring the new NVIDIA L40S GPU built for the industry standard data center server ecosystem and BlueField-3 DPU data center infrastructure processor.

L40S' focus is to be able to fine-tune models, fine-tune pretrained models, and it'll do that incredibly well. It has a transform engine. It's got a lot of performance. You can get multiple GPUs in a server. It's designed for hyperscale scale-out, meaning it's easy to install L40S servers into the world's hyperscale data centers. It comes in a standard rack, standard server, and everything about it is standard.

𝟭𝟰 | 𝗗𝗚𝗫 𝗚𝗛𝟮𝟬𝟬, 𝗮 𝗻𝗲𝘄 𝗰𝗹𝗮𝘀𝘀 𝗼𝗳 𝗹𝗮𝗿𝗴𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗔𝗜 𝘀𝘂𝗽𝗲𝗿𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿…𝗚𝗖𝗣, 𝗠𝗲𝘁𝗮 & 𝗠𝗦𝗙𝗧 𝗵𝗮𝘃𝗲 𝗳𝗶𝗿𝘀𝘁 𝗱𝗶𝗯𝘀

We announced the DGX GH200, a new class of large memory AI supercomputer for giant AI language model, recommendator systems and data analytics.

The DGX GH200 systems are expected to be available by the end of the year, Google Cloud, Meta and Microsoft among the first to gain access.

𝟭𝟱 | 𝗡𝗲𝘄 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗲𝗿𝗮 𝗼𝗳 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗴𝗲𝗻 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗚𝗣𝗨𝘀/𝗖𝗨𝗗𝗔 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲/𝗠𝗲𝗹𝗹𝗮𝗻𝗼𝘅 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴

A new computing era has begun. Companies worldwide are transitioning from general-purpose to accelerated computing and generative AI.

NVIDIA GPUs connected by our Mellanox networking and switch technologies and running our CUDA AI software stack make up the computing infrastructure of generative AI.

𝟭𝟲 | 𝗡𝗩𝗜𝗗𝗜𝗔’𝘀 𝗺𝗼𝗮𝘁 𝗶𝘀 𝗶𝘁𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘁𝗼𝗼𝗸 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱

And it's taken us 2 decades to get here. But what I would characterize as probably our -- the elements of our company, if you will, are several. I would say number 1 is architecture. The flexibility, the versatility and the performance of our architecture makes it possible for us to do all the things…from data processing to training to inference, for preprocessing of the data before you do the inference to the post processing of the data, tokenizing of languages so that you could then train with it.

𝟭𝟳 | 𝗘𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗡𝗩𝗜𝗗𝗜𝗔 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗹𝗼𝘄𝗲𝘀𝘁 𝗰𝗼𝘀𝘁 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽

the combination of our architecture makes it possible for us to deliver the lowest cost ownership.

𝟭𝟴 | 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗡𝗩𝗜𝗗𝗜𝗔 𝘀𝗽𝗲𝗰𝗶𝗮𝗹 - 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲, 𝗶𝗻𝘀𝘁𝗮𝗹𝗹𝗲𝗱 𝗯𝗮𝘀𝗲, 𝗿𝗲𝗮𝗰𝗵

What makes NVIDIA special are: one, architecture. NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision and giant recommenders to vector databases. The performance and versatility of our architecture translates to the lowest data center TCO and best energy efficiency.

Two, installed base. NVIDIA has hundreds of millions of CUDA-compatible GPUs worldwide. Developers need a large installed base to reach end users and grow their business. NVIDIA is the developer's preferred platform. More developers create more applications that make NVIDIA more valuable for customers.

Three, reach. NVIDIA is in clouds, enterprise data centers, industrial edge, PCs, workstations, instruments and robotics.

𝟭𝟵 | 𝗧𝗵𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗜𝗻𝗳𝗶𝗻𝗶𝗕𝗮𝗻𝗱 - 𝗯𝗲𝗶𝗻𝗴 𝗮𝗯𝗹𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝘁𝗼 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 𝗼𝗳 𝗚𝗣𝗨𝘀

Strong networking growth was driven primarily by InfiniBand infrastructure to connect HGX GPU systems. Thanks to its end-to-end optimization and in-network computing capabilities, InfiniBand delivers more than double the performance of traditional Ethernet for AI….only InfiniBand can scale to hundreds of thousands of GPUs. It is the network of choice for AI practitioners.

𝟮𝟬 | 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 $𝟭𝗧 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿, $𝟮𝟱𝟬𝗺 𝗔𝗻𝗻𝘂𝗮𝗹 𝗖𝗮𝗽𝗲𝘅 𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘆 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗴𝗲𝗻𝗔𝗜

The world has something along the lines of about $1 trillion worth of data centers installed, in the cloud, in enterprise and otherwise. And that $1 trillion of data centers is in the process of transitioning into accelerated computing and generative AI. We're seeing 2 simultaneous platform shifts at the same time. One is accelerated computing.

enabled by accelerated compute and generative AI came along. And this incredible application now gives everyone 2 reasons to transition to do a platform shift from general purpose computing, the classical way of doing computing, to this new way of accelerated computing. It's about $1 trillion worth of data centers, call it, $0.25 trillion of capital spend each year.

𝟮𝟭 | 𝗡𝗼𝘁 𝗮 𝗻𝗲𝗮𝗿-𝘁𝗲𝗿𝗺 𝗳𝗮𝗱, 𝗯𝘂𝘁 𝗺𝗼𝗿𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹

You're seeing the data centers around the world are taking that capital spend and focusing it on the 2 most important trends of computing today, accelerated computing and generative AI. And so I think this is not a near-term thing.

𝟮𝟮 | 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝟮𝟬𝟮𝟰

We have excellent visibility through the year and into next year. And we're already planning the next-generation infrastructure with the leading CSPs and data center builders. If you think about the demand, the world is transitioning from general-purpose computing to accelerated computing. That's the easiest way to think about the demand.

𝟮𝟯 | 𝗦𝘁𝗿𝗼𝗻𝗴 𝗼𝘂𝘁𝗹𝗼𝗼𝗸 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗯𝘆 𝘁𝗿𝗲𝗺𝗲𝗻𝗱𝗼𝘂𝘀 𝗮𝗻𝗱 𝗯𝗿𝗼𝗮𝗱-𝗯𝗮𝘀𝗲𝗱 𝗱𝗲𝗺𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗻𝗲𝘅𝘁 𝘆𝗲𝗮𝗿

…outlook for 3Q24 2024. Demand for our Data Center platform where AI is tremendous and broad-based across industries on customers. Our demand visibility extends into next year. Our supply over the next several quarters will continue to ramp as we lower cycle times and work with our supply partners to add capacity.

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

➡️ Final Takeaways on NVIDIA $NVDA:

NVIDIA is at the pivot of the shift from general purpose computing to accelerated computing and gen AI with significantly stronger use cases than the previous Crypto/De-Fi/NFT hype.

Strong visibility and broad-based demand reflects the start of a potential longer-term shift that NVIDIA with its strong architecture moat of CUDA, InfiniBand, Mellanox have taken decades to build.

The seemingly “expensive” valuations seem less expensive now, and increasingly more shareholder returns will be via share buybacks (almost Apple like). We continue to own NVIDIA.