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Bull of the Day: NVIDIA (NVDA)

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I last profiled NVIDIA ((NVDA - Free Report) ) for the Bull of the Day on February 28 after another big beat-and-raise quarter caught most investors and analysts off-guard and shares jumped from $675 to new highs above $800.

That article also has a video link where I go over an NVIDIA slide deck that should blow your mind.

I've been telling investors since that they won't see $700 again. And that prophecy became further solidified after the semiannual GPU Tech Conference (GTC) the week of March 18.

GTC 2024: March 18th Will Be Remembered For Years

Just as I tell investors to check in with the NVIDIA Newsroom every couple of weeks to see the latest in innovations on the accelerated computing/AI frontier, so too being aware of GTC events is critical.

Because there is always a flurry of new technologies and partnerships unveiled, to say nothing of the most recent scientific R&D that Jensen highlights as accomplished using their hardware and software stacks.

On March 18, GTC 2024 kicked off with these observations from Jensen & Co...

Generative AI promises to revolutionize every industry it touches — all that’s been needed is the technology to meet the challenge.

NVIDIA founder and CEO Jensen Huang on 3/18 introduced that technology — the company’s new Blackwell computing platform — as he outlined the major advances that increased computing power can deliver for everything from software to services, robotics to medical technology and more.

“Accelerated computing has reached the tipping point — general purpose computing has run out of steam,” Huang told more than 11,000 GTC attendees gathered in-person — and many tens of thousands more online — for his keynote address at Silicon Valley’s cavernous SAP Center arena.

“We need another way of doing computing — so that we can continue to scale so that we can continue to drive down the cost of computing, so that we can continue to consume more and more computing while being sustainable. Accelerated computing is a dramatic speedup over general-purpose computing, in every single industry.”

Introducing the Blackwell Superchip

Less than a year after NVIDIA unveiled the DGX GH200 supercomputer pod, at GTC they rolled out the DGX GB200 SuperPOD. I previewed the coming mystery "superchip" -- that was expected to be much faster and more energy efficient -- in my February profile.

As you may know, “GH” is for Grace Hopper. Jensen likes to find unsung heroes of computing and science to name his products after and this was a salute to Grace Brewster Hopper, an American computer scientist, mathematician, and United States Navy rear admiral. One of the first programmers of the Harvard Mark I computer, she was a pioneer of computer programming.

So who is Blackwell? David Blackwell was an American statistician and mathematician who made significant contributions to game theory and information theory. He was the first African American inducted into the National Academy of Sciences, the first African American full professor (with tenure) at the University of California, Berkeley, and the seventh African American to receive a Ph.D. in mathematics.

What happens when you put the Admiral of COBOL together with the Bayesian stats genius? According to Jensen, you get a platform to “power a new era of computing.”

To scale up Blackwell, NVIDIA built a new chip called NVLink Switch. Each can connect four NVLink interconnects at 1.8 terabytes per second and eliminate traffic by doing in-network reduction.

So the GB200 NVL72 is a multi-node, liquid-cooled, rack-scale system for the most compute-intensive workloads. It combines 36 Grace Blackwell Superchips, which include 72 Blackwell GPUs and 36 Grace CPUs interconnected by fifth-generation NVLink.

The Blackwell platform will enable organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.

Convergent Technologies = Exponential Acceleration

The Blackwell GPU architecture features six transformative technologies for accelerated computing, which will help unlock breakthroughs in data processing, engineering simulation, electronic design automation, computer-aided drug design, quantum computing and generative AI — all emerging industry opportunities for NVIDIA.

“For three decades we’ve pursued accelerated computing, with the goal of enabling transformative breakthroughs like deep learning and AI,” said Huang. "Generative AI is the defining technology of our time. Blackwell is the engine to power this new industrial revolution. Working with the most dynamic companies in the world, we will realize the promise of AI for every industry.”

And who is lined up to buy these systems? Only the who’s who of big tech: Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla and xAI. You can read their view of this breakthrough in one of the March 18 press releases.

Estimates Still Levitating

In my February report, I noted that sales estimates would continue to rise in March as analysts re-worked their models and caught up to the real, but still hard-to-believe, demand for solutions from the King of AI.

Next year's topline has moved from $117 billion to $125 billion, with the high estimate at $142B. I expect the consensus for next year to soon be $150B.

And with gross margins above 75%, NVIDIA continues to release the profit flow. This year's EPS consensus has risen over 20% in the past two months to $23.84, representing 84% growth. Next year is projected to top EPS of $27.

Limits to AI Growth and Impacts?

How long can it go on for? And what about the massive energy costs, both financial and environmental, of all these datacenters running at insane speeds and temperatures.

Take it from Taiwan Semiconductor, aka TSMC, who forecasts a 1000x improvement in GPU performance per watt over the next 15 years. JD Ross, cofounder of Opendoor and now working on energy projects, wrote this last week on Twitter @justindross...

"Coupled with major algorithmic improvements we're quickly seeing every week, it isn't crazy to expect 100,000 to 1,000,000x increase in AI performance per dollar in the next decade and a half."

Jensen always told us that GPU-driven massively parallel architectures had reinvented Moore's Law and were going to give exponential life to computing that solves big problems in science, industry, and society. He kept his promise.

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