NVIDIA and the AI Industrial Revolution

🤖 NVIDIA: Industrial Revolution

AI factories are reshaping the future of computing

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APP ECONOMY INSIGHTS

MAY 24, 2024

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NVIDIA (NVDA) soared 10% yesterday after reporting its Q1 results.

The company is now bigger than Amazon and Tesla combined. With a $2.6 trillion market cap, NVIDIA is closing in on Apple and Microsoft.

Data by YCharts

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Jensen Huang explained in his prepared remarks:

“The next industrial revolution has begun.Companies and countries are partnering with NVIDIA to shift the trillion-dollar installed base of traditional data centers to accelerated computing and build a new type of data center, AI factories, to produce a new commodity, artificial intelligence.”

Huang succinctly captured the essence of these AI factories:

“Data comes in and intelligence comes out.”

As a reminder, AI systems operate through two core stages:

  • 🎓 Training: AI learns from vast amounts of data, developing intelligence and pattern recognition. NVIDIA's powerful GPUs dominate this phase.
  • 🧠 Inference: AI applies its knowledge to real-world tasks and decision-making. While facing stiffer competition here, NVIDIA is making significant progress.

Inference workloads contributed roughly 40% of NVIDIA's Data Center revenue in the past year. As more generative AI applications make their way into consumer products, inference is poised to become a massive market, offering customers a significant return on their data center investments.

Let’s unpack the quarter.

Today at a glance:

  1. NVIDIA’s Q1 FY25.
  2. Powering the AI Boom.
  3. Key quotes from the call.
  4. What to watch looking forward.
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1. NVIDIA Q1 FY25

Income statement:

NVIDIA’s fiscal year ends in January, so it was Q1 FY25. I’m focusing on sequential growth (quarter-over-quarter), a better representation of the momentum.

Visual by App Economy Insights

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  • Revenue jumped +18% Q/Q to $26.0 billion ($1.5 billion beat).
    • ⚙️ Data Center grew +23% Q/Q to $22.6 billion.
    • 🎮 Gaming declined 8% Q/Q to $2.6 billion.
    • 👁️ Professional Visualization declined 8% Q/Q to $0.4 billion.
    • 🚘 Automotive grew +17% Q/Q to $0.3 billion.
    • 🏭 OEM & Other dropped by 13% Q/Q to $0.1 billion.

Below is my favorite visual to show the remarkable growth in Data Center revenue in the past two years.

Visual by App Economy Insights

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  • Gross margin was 78% (+2pp Q/Q), compared to a 76% guidance.
  • Operating margin was 65% (+3pp Q/Q).
  • Non-GAAP operating margin was 69% (+3pp Q/Q).
  • Non-GAAP EPS $6.12 ($0.54 beat).

Cash flow:

  • Operating cash flow was $15.3 billion (59% margin).
  • Free cash flow was $14.9 billion (57% margin).

Balance sheet:

  • Cash and cash equivalent: $31.4 billion.
  • Debt: $9.7 billion.

Q2 FY25 Guidance:

  • Revenue +8% Q/Q to $28.0 billion ($1.2 billion beat).
  • Gross margin 76.3% (+0.3pp Q/Q).

So what to make of all this?

  • NVIDIA delivered another impressive revenue beat of 6%. If you recall, the revenue beat was 13% in Q3 and 8% in Q4. The Q2 FY25 revenue guidance was $1.2 billion ahead of consensus. That compares to a $2.0 billion beat in the previous quarter. Despite resetting expectations higher every quarter, NVIDIA still finds a way to surprise by a wide margin.
    • ⚙️ Data Center was 87% of overall revenue (+4pp Q/Q). It was up 427% year-over-year and 23% sequentially. Management broke down Data Center revenue for the first time:
      • ⚡ Compute: Up 478% year-over-year and 29% sequentially to $19.4 billion. Like previous quarters, the driver was strong demand for the Hopper GPU computing platform used for training and inferencing with large language models (LLMs), recommendation engines, and Gen AI apps.
      • 🔌 Networking: Up 242% year-over-year but down 5% sequentially (due to timing of supply) to $3.2 billion. InfiniBand end-to-end solutions are the primary driver here. NVIDIA started shipping the Spectrum-X Ethernet networking solution optimized for AI.
      • Visual by App Economy Insights

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    • 🎮 Gaming was down 8% sequentially due to seasonality, as expected. The segment has doubled compared to pre-COVID. GeForce RTX PCs have an installed base of over 100 million.
    • 👁️ Professional Visualization declined by 8% sequentially but grew 45% year-over-year, driven by demand for workstation GPUs based on the Ada Lovelace architecture.
    • 🚘 Automotive accelerated +17% sequentially, driven by the ramp of AI cockpit solutions and self-driving platforms. Nearly 80 automakers use NVIDIA’s AI structure for autonomous driving and other applications.
  • Margins improved significantly, boosted by more favorable component costs. The gross margin was two percentage points ahead of guidance. Management expects a gross margin in the mid-70s for FY25, implying a contraction in the second half.
  • NVIDIA’s dividend increased by 150%. It’s an impressive soundbite, but it started from a tiny base. And it’s not surprising when considering net income is up 628% year-over-year.
  • Many headlines focused on the 10-1 stock split, which is neither important nor exciting unless you are a day trader.
  • Guidance was ahead of expectations ($1.2 billion beat or 4%), but it was a modest beat relative to the past few quarters.

2. Powering the AI Boom

NVIDIA's latest powerhouse GPU, the H200, is now shipping, and it's already making waves. The H200 powered OpenAI's impressive GPT-4o demo last week (see the video link below if you missed it).

It showcased the model's incredible speed and potential to transform our interactions with AI. We are getting closer to a more seamless, natural human-computer interaction. Yes, like in the movie Her. 😉

This is a major milestone for NVIDIA as it races to deliver the infrastructure for the next generation of AI applications. It illustrates the potential of future consumer AI products teased by Jensen Huang.

But the rapid pace of AI innovation has raised concerns about obsolescence. Will today's cutting-edge chips be outdated tomorrow? Jensen Huang addressed these fears head-on, emphasizing two key points:

  • Leadership matters: Speed is critical in the race to deliver groundbreaking AI products. NVIDIA's focus on getting the fastest chips to market as soon as possible gives them a competitive advantage.
  • Understanding the whole system: NVIDIA's deep understanding of the entire AI infrastructure stack allows them to optimize performance and squeeze out every bit of power from their chips for each generation.

3. Key quotes from the earnings call

CFO Colette Kress:

On Data Center:

“As generative AI makes its way into more consumer Internet applications, we expect to see continued growth opportunities as inference scales both with model complexity as well as with the number of users and number of queries per user, driving much more demand for AI compute.”

She provided updates on the three major customer categories:

  • ☁️ Cloud service providers (CSPs)contributed nearly half of Data Center revenue. All hyperscalers (Amazon, Microsoft, Google) are NVIDIA customers.
  • 🗄️ Enterprise drove strong sequential growth. Tesla expanded their training AI cluster to 35,000 H100 GPUs and used NVIDIA AI for FSD V12 (the latest autonomous software). Kress expects automotive to be the largest vertical within Data Center in FY25.
  • 💬 Consumer Internet companies are also a critical vertical. Meta’s Llama 3 (which powers Meta AI) was trained on a cluster of 24,000 H100 GPUs.

On Sovereign AI:

“Sovereign AI refers to a nation's capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks. […]

From nothing the previous year, we believe Sovereign AI revenue can approach the high single-digit billions this year. The importance of AI has caught the attention of every nation.”

This rapid growth will be critical to Data Center revenue, even at the current scale.

On the impact of export restrictions:

“We ramped new products designed specifically for China that don't require an export control license. Our Data Center revenue in China is down significantly from the level prior to the imposition of the new export control restrictions in October.”

The US regulations affect the highest performance levels.

On new gen H200 and Blackwell:

“Demand for H200 and Blackwell is well ahead of supply and we expect demand may exceed supply well into next year.”

Shipments will start in Q2 and ramp in Q3, a quarter earlier than expected.

CEO Jensen Huang:

Jensen Huang has described a ‘generative AI wave’ worth repeating here. AI is moving from one category to the next:

  1. → Startups and CSPs.
  2. → Consumer Internet.
  3. → Software platforms.
  4. → Enterprise and government.

“Everyone is getting an LLM” is my way of summarizing it.

On the Industrial Revolution:

“We are fundamentally changing how computing works and what computers can do, from general purpose CPU to GPU accelerated computing, from instruction-driven software to intention-understanding models, from retrieving information to performing skills, and at the industrial level, from producing software to generating tokens, manufacturing digital intelligence.”

It’s not hard to appreciate the idea of ‘digital intelligence’ and the new ways we could interact with this technology after the GPT-4o demo.

“Longer term, we're completely redesigning how computers work. And this is a platform shift. Of course, it's been compared to other platform shifts in the past. But time will clearly tell that this is much, much more profound than previous platform shifts. And the reason for that is because the computer is no longer an instruction-driven only computer. It's an intention-understanding computer.”

On training vs. inference:

“Training continues to scale as models learn to be multimodal, understanding text, speech, images, video and 3D and learn to reason and plan.

Our inference workloads are growing incredibly. With generative AI, inference, which is now about fast token generation at massive scale, has become incredibly complex.”

On why NVIDIA is differentiated:

“One, you could use NVIDIA for everything. […] The versatility of our platform results in the lowest TCO for their data center.

Second, we're in every cloud. And so for developers that are looking for a platform to develop on, starting with NVIDIA is always a great choice. […]

The third reason has to do with the fact that we build AI factories. And this is becoming more an apparent to people that AI is not a chip problem only.”

Huang explained that AI is now a systems problem with many considerations beyond the chips powering the Data Center. And a higher performance can lead to a lower TCO (Total Cost of Ownership).

4. What to watch looking forward

Buybacks skyrocket, but is that a good thing?

NVIDIA repurchased $9.5 billion worth of its own stock in FY24.

In Q1 FY25 alone, management bought back $7.7 billion. Yes, you read that right. Nearly triple the previous quarter! As a reminder, management initiated a new $25 billion repurchase program last year, hinting at more buybacks in FY25.

While this could signal management's belief in the company's prospects, some investors may question if the current cash influx would be better spent on research and development or other growth initiatives.

Is it too late to buy?

NVDA was notably absent from high-profile hedge funds’ top picks in the latest round of 13F filings, illustrating that hedge funds have become more cautious.

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💰 Hedge Funds' Top Picks in Q1

APP ECONOMY INSIGHTS

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17 MAY

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NVIDIA's forward PE ratio is roughly 38, according to data from YCharts. For context, that’s only slightly more than Microsoft—trading at 36 times forward earnings while growing revenue 13% Y/Y, excluding the Activision acquisition.

Of course, we are comparing apples to oranges here, and the sustainability of the current demand for NVIDIA’s GPUs is still unclear.

A lot of future demand is already factored in NVIDIA’s forward earnings estimates, and the semiconductor industry is known for its cyclicality. But to be sure, with nearly $15 billion in net profit in Q1, NVIDIA’s valuation is no fluke and is very much anchored on the performance of the underlying business.

Data by YCharts

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With the upcoming Blackwell platform, the expansion of Spectrum-X networking, and the development of new software tools like NIMs, NVIDIA is positioning itself for further growth. And if GPT-4o is any indication, AI is about to enter our lives in a much more meaningful way.