As GTC, San Jose, 2026 wraps up, some commentary.
First up, Yahoo Finance, March 19:
Nvidia's changing its strategic approach to AI, going all in on inferencing and agents
Jensen Huang took the stage at Nvidia’s (NVDA)
GTC event in San Jose, Calif., on Monday, clad in his usual leather
jacket, to provide the world with an update about what the world’s most
valuable company has been cooking up over the last few months.
Huang
was as indefatigable as ever as he ran through his roughly
two-and-a-half-hour keynote in front of some 30,000 attendees. But
what’s come to be known as the Super Bowl of AI featured a noticeable
shift in Nvidia’s overall AI strategy — a deeper focus on inferencing,
or powering AI models, and agents.
Nvidia’s chips are traditionally known for their
general-purpose use. They can train and run AI models, power robots, and
serve as the backbone of self-driving cars.
And
while Nvidia’s offerings are still the industry standard, upstart chip
companies like Cerebras and Groq have begun designing and rolling out
processors geared specifically toward running AI models, creating a
potential threat to Nvidia’s formidable AI moat.
Huang and company answered that at GTC with a slew of announcements meant to prove Nvidia is the inferencing leader to beat, including the debut of its Groq 3 chip and rack system.
Nvidia didn’t just go further with its inferencing
capabilities, though. The company also showed off its addition to the
much-hyped world of OpenClaw high-powered AI agents.
OpenClaw,
which debuted as Clawd in November 2025 before being renamed Moltbot
and finally OpenClaw in January, has taken off thanks to its ability to
run AI agents powered by different AI models on users’ machines via apps
like WhatsApp, Discord, Slack, and others.
Now, Nvidia is getting in on the buzz with its NemoClaw platform designed to improve the security and privacy of the agents.
“They
are evolving in a big way, not only in inference, agentic, too,”
TECHnalysis Research founder and chief analyst Bob O’Donnell told Yahoo
Finance.
“The switch to OpenClaw, and now
NemoClaw, to me, is even more indicative of this. It just shows how
quickly they are reacting to the market.”
Nvidia moves further into inferencing
Nvidia’s decision to include Groq 3 as one of the seven chip platforms that make up Vera Rubin is part of its effort to stay ahead of the broader industry.Nvidia signed a $20 billion deal with Groq in December,
hiring founder Jonathan Ross, president Sunny Madra, and other members
of the Groq team and giving Nvidia access to Groq’s intellectual
property.
The results of the deal are Nvidia’s new Groq 3 language processing unit
(LPU) and Groq 3 LPX server rack. That’s right, Nvidia now has graphics
processing units (GPUs), LPUs, and central processing units (CPUs).
It’s a lot of units....
....MUCH MORE
And at Barron's March 19:
Nvidia Is Giving Apple Vibes. Why That Spells Big Things for the Stock.
The
artificial-intelligence revolution has entered a new phase, one in
which running AI models, known as inference, is taking over as the main
source of demand for AI computing. Nvidia
was the winner of round one when training the AI models drove chip
sales. But things change quickly in tech, and the company still has to
convince the market and customers that it remains indispensable.
CEO
Jensen Huang devoted his keynote address at Nvidia’s GTC conference
this past week to make the case. He reminded everyone that Nvidia had
spent two decades building an ecosystem of hardware and software that
makes its platform the least costly for AI. By the end of his speech,
Huang had delivered a vision of Nvidia that reminded me of just one
other company: Apple.
For
years, Wall Street didn’t appreciate that Apple was more than just a
hardware firm. Apple’s version of consumer technology provides a
carefully thought-out bundle. The hardware is expensive, but it comes
with a lot of free software and services that bring everything together
seamlessly. In the end, the platform is sticky and full of value.
This
is sometimes called Apple’s “walled garden.” iPhones, Macs, and Watches
work like one because Apple controls the entire technology stack: the
chips, the devices, the operating systems, the applications, and the
cloud services. It’s all developed together, so it all just works
together.
You’re free to leave the garden through a well-hidden gate, but the flowers are nice and the sun is shining, so why would you?
Nvidia
is employing that Apple model of full control in an entirely different
market: AI computing. More and more, Nvidia is moving toward being a
full platform with an ecosystem of hardware, software, and partnerships
that could be sticky like Apple’s, notwithstanding growing competition
in the AI chip market.
It
begins with Nvidia controlling as many layers of data center
infrastructure as it can, what CEO Jensen Huang calls “extreme
codesign.” A lot of attention is paid to Nvidia GPU chips, the
workhorses of AI data centers, but there are five other Nvidia chips
inside its coming Vera Rubin AI server, each with a crucial role in
making a product that can’t be matched. The chips work better because
they are designed together to work together.
Nvidia
also makes data center network switches that alleviate a key computing
bottleneck. In the last quarter, networking sales were responsible for
16% of Nvidia revenue, up from 8% the year before. It’s now the
fastest-growing unit in Nvidia’s reporting.
This
year, Nvidia will integrate a new server design built around AI
inference chips from start-up Groq. Vera Rubin will work in concert with
Groq on demanding inference tasks. Creating a data center with mixed
servers that collaborate with each other is a thorny problem that Nvidia
solved with software called Dynamo. Nvidia’s hardware still leads the
industry, but the deepest part of the company’s moat is all the software
it’s created to run on its hardware.
Huang
began his GTC keynote by talking about the 20th anniversary of Nvidia’s
most important software known as CUDA, or Compute Unified Device
Architecture. In 2004, Nvidia hired Ian Buck, an engineer fresh out of
Stanford University, to create a way for programmers to use Nvidia GPUs
for a lot more than just computer graphics and gaming. Two years later,
CUDA was born.
Nvidia
kept developing the software, and by 2012, AI researchers had made
Nvidia’s platform their preferred kit. A whole generation of researchers
grew up on it. When ChatGPT triggered the generative AI craze in 2022,
no one was more prepared for it than Nvidia.
Buck remains a Nvidia employee.
Nvidia
has continued to build the ecosystem on top of the GPU-CUDA
combination. The company’s online code portfolio has 700 repositories,
including specialized software for engineering, physics, weather, and
medical science, along with tools for AI training, inference, and
agents. These are active projects with new versions rolling out all the
time. Over a third of the repositories have received updates in the past
month.
Nvidia is also
the world’s largest contributor to open-source AI models with 715 of
them available for download....
....MUCH MORE
Also at Barron's, March 18:
Sure, Nvidia Stock Is Stuck. But Don’t Ignore Its Huge Cash Returns
The stock is down $2.45 (-1.36%) at $177.95.
This week:
Sadly, I don't think we'll see anything as insightful as 2024's "Nvidia CEO Jensen Huang debuts new $8,990 lizard-embossed leather jacket, also says something about AI GPUs: (NVDA)
Although....looking back to 2016's "Huh, This NVIDIA Company May Be On To Something (NVDA)" it's possible I''ll come up with something.
After a series of all-time highs last week the stock looks set to open up a couple pennies at $44.35.
From the Wall Street Journal:
New Chips Propel Machine Learning
Divide by 40 to account for the stock splits and we see $1.11 on the old stock.