SAN FRANCISCO, March 13 (Reuters) – When Jensen Huang takes the stage in a crowded hockey arena on Monday to begin Nvidia’s (NVDA.O) new tab annual developer conference, he is probably going to announce partnerships and products designed to keep the AI chipmaker ahead of an increasing number of rivals.
CEO Huang’s favorite event to showcase Nvidia’s AI advancements in chips, data centers, its chip programming software CUDA, digital assistants known as AI agents, and physical AI such as robots is the conference known as Nvidia GTC, which takes over the center of Silicon Valley for the better part of a week.
The four-day event is even more important this year since investors are looking for proof that Nvidia’s plan to reinvest its revenues in the AI ecosystem is working.Using the names of Nvidia’s current and upcoming chip generations, eMarketer analyst Jacob Bourne stated, “I expect Nvidia will offer a full-stack roadmap update from Rubin to Feynman while stressing inference, agentic AI, networking, and AI factory infrastructure.”
Governments and businesses all around the world have invested hundreds of billions of dollars in data centers using Nvidia’s processors, but the company is up against competition from other chipmakers and even from some of its clients who are creating their own chips.
According to analysts who spoke to Reuters, the market for AI chips as a whole is expected to continue expanding, but Nvidia’s market share is expected to slightly decline as the industry quickly shifts to one in which AI agents scamper between computer programs performing duties on behalf of people. This is a change from training, when AI laboratories combine multiple Nvidia processors into a single computer to process massive volumes of data in order to refine their AI models.
It is anticipated that these agents will proliferate to such an extent that humans requesting them to perform tasks will even require a new layer of AI middle managers, whom technologists refer to as a “orchestration” layer, to sit between human users and their fleets of agents.
Analysts think that is a good thing for Nvidia in some ways since it shows that AI is becoming more helpful.
However, similar tasks—generally referred to as “inference” in the AI industry—can also be performed on other types of chips, such as those used by major Nvidia clients like OpenAI and Meta (META.O), who recently announced plans to introduce new AI chips every six months.
“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 (INTC.O), opens new tab and Advanced Micro Devices (AMD.O), opens new tab.
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.
Reporting by Stephen Nellis in San Francisco and Zaheer Kachwala in Bengaluru; Editing by Sayantani Ghosh and Sonali Paul
