When Jensen Huang strides onto the stage of a packed hockey arena to kick off Nvidia’s annual developer conference on Monday, he is likely to reveal products and partnerships geared toward keeping the AI chipmaker atop a growing array of competitors.
Taking over the heart of Silicon Valley for most of a week, Nvidia GTC, as the conference is known, has become CEO Huang’s preferred event to show off Nvidia’s AI advances in chips, data centers, its chip programming software CUDA, digital assistants known as AI agents, and physical AI such as robots.
This year, the four-day event is even more crucial as investors will seek assurance that Nvidia’s strategy of plowing back its profits into the AI ecosystem is paying off.
“I expect Nvidia to present a full-stack roadmap update from Rubin to Feynman while emphasizing inference, agentic AI, networking, and AI factory infrastructure,” said eMarketer analyst Jacob Bourne, using the names for Nvidia’s current and forthcoming generations of chips.
Nvidia’s chips sit at the center of hundreds of billions of dollars in investments in data centers by governments and companies around the globe, but the company is facing competition from other chipmakers and even from some of its customers who are developing their own chips.
Analysts told Reuters they expect that overall AI chip market to keep growing, but Nvidia’s slice to shrink somewhat as the AI chip market changes rapidly to where AI agents scurry back and forth among computer applications carrying out tasks on behalf of humans. That is a shift from training, where AI labs link many Nvidia chips together into one computer to chew through huge amounts of data to perfect their AI models.
Those agents are expected to become so numerous that the humans asking them to do work will even need a new layer of AI middle managers – what technologists call an “orchestration” layer – to sit between human users and their fleets of agents.
In some ways, analysts say, that’s a good thing for Nvidia, because it signals that AI is becoming more useful.
But those tasks, broadly known as “inference” in the AI industry, can also run on other kinds of chips, including the ones that big Nvidia customers such as OpenAI and Meta, which recently said it plans to release new AI chips every six months, can build for themselves.
“Nvidia is definitely going to see more competition compared to a year ago,” said KinNgai Chan, a managing director at Summit Insights Group. “Nvidia still has close to over 90% market share in both training and inference markets today.”
“We think Nvidia will begin to see share loss starting in 2027, once in-house ASIC programs gain some scale especially in the inference market,” he said, referring to application-specific integrated circuits, chips tailored for a single function or custom workload, offering higher efficiency than general-purpose graphics processing units.
NVIDIA IS SHORING UP DEFENSES
The company spent $17 billion in December to purchase Groq, a chip startup that specializes in fast and cheap inference computing work. Talking about Groq on the company’s earnings call last month, Huang said the company would showcase at GTC how Nvidia can plug Groq’s ultra-fast AI technology into their existing CUDA platform.
William McGonigle, analyst at Third Bridge, said his firm expects Nvidia to roll out a new line of servers that will combine Groq’s chips with Nvidia’s networking technologies to create a speedy and cost-efficient product.
Another type of chip that poses an increasing competitive threat to Nvidia is the central processor unit, or CPU, the kind of chip long championed by Intel and Advanced Micro Devices.
While those chips took a backseat to Nvidia’s graphics processor units (GPUs) in recent years, McGonigle said they are “back in focus” and expects Nvidia to show off servers that use only its CPUs, which Huang talked up on a recent earnings call.
“With the rise of agentic AI, the bottleneck is now at the agent orchestration level, which is carried out by the CPUs,” McGonigle said.
Analysts also expect Nvidia to elaborate on why it invested $2 billion each in Lumentum and Coherent, both of which make lasers for sending information between chips in the form of beams of light. Use of those lasers in what are called co-packaged optics could help speed up the connections among Nvidia’s chips inside huge data centers, but they are not currently made in big enough volumes to match the number of chips Nvidia sells each year.
“Nvidia will likely frame co-packaged optics as key to connecting massive AI clusters more efficiently, but the challenge is making it affordable enough to deploy at scale,” said eMarketer’s Bourne.