Nvidia Targets AI Inference as Chip Revenue Opportunity Reaches $1 Trillion
The Chronify
Nvidia said the revenue opportunity for its artificial intelligence chips could reach at least $1 trillion through 2027, as the company used its annual GTC developer conference to sharpen its push into AI inference, the fast growing segment focused on running models in real time rather than training them. Reuters reported that CEO Jensen Huang presented the new forecast as Nvidia moves to defend its dominance against rising competition from CPUs and custom AI chips from companies such as Google.
The new outlook marks a major increase from Nvidia’s earlier estimate of a $500 billion revenue opportunity through 2026 for its Blackwell and Rubin AI systems. Huang said inference demand has reached an inflection point as AI use shifts from model building to serving large numbers of users through chatbots, agents and enterprise tools. Nvidia’s own GTC coverage said Huang now sees at least $1 trillion in revenue from 2025 through 2027 as computing demand accelerates.
At the conference, Huang unveiled a new central processor called Vera and introduced an AI system built with technology licensed from startup Groq, part of Nvidia’s broader attempt to strengthen its position in inference computing. Reuters reported that Nvidia plans to split inference into two stages, with its Vera Rubin chips handling the “prefill” stage and Groq based chips handling the “decode” stage that generates responses.
The strategy reflects a shift in the AI market. Reuters reported that Nvidia has dominated training, where large model builders connect massive clusters of graphics processors to develop AI systems. But as firms such as OpenAI, Anthropic and Meta focus more on serving hundreds of millions of users, demand is rising for inference hardware, including CPUs and specialized processors that compete more directly with Nvidia’s GPUs.
Huang also said Nvidia’s standalone CPU business is already becoming significant, calling it a multibillion dollar opportunity. In addition, the company previewed its Feynman architecture, expected in 2028, and introduced NemoClaw, a platform aimed at adding privacy and safety controls to AI agents built on OpenClaw. Reuters said the announcements show how Nvidia is increasingly positioning itself as a full AI systems company rather than a pure GPU supplier.
The presentation came at a moment of higher investor scrutiny. After Nvidia became the first company to reach a $5 trillion valuation in October 2025, some investors began questioning how durable its growth would remain and whether its heavy reinvestment across the AI ecosystem would continue to pay off. The Financial Times reported that Huang’s trillion dollar forecast helped calm some of those concerns, even as Wall Street remains more conservative in its medium term revenue expectations.
Analysts said the message from GTC was that Nvidia sees inference as the next major battleground in AI infrastructure. Reuters and other market coverage noted that Huang no longer presents Nvidia mainly as a chipmaker unveiling a single new GPU, but as a builder of complete AI platforms, combining processors, networking, software and integrated rack scale systems.
At the conference, Huang unveiled a new central processor called Vera and introduced an AI system built with technology licensed from startup Groq, part of Nvidia’s broader attempt to strengthen its position in inference computing. Reuters reported that Nvidia plans to split inference into two stages, with its Vera Rubin chips handling the “prefill” stage and Groq based chips handling the “decode” stage that generates responses.
The strategy reflects a shift in the AI market. Reuters reported that Nvidia has dominated training, where large model builders connect massive clusters of graphics processors to develop AI systems. But as firms such as OpenAI, Anthropic and Meta focus more on serving hundreds of millions of users, demand is rising for inference hardware, including CPUs and specialized processors that compete more directly with Nvidia’s GPUs.
Huang also said Nvidia’s standalone CPU business is already becoming significant, calling it a multibillion dollar opportunity. In addition, the company previewed its Feynman architecture, expected in 2028, and introduced NemoClaw, a platform aimed at adding privacy and safety controls to AI agents built on OpenClaw. Reuters said the announcements show how Nvidia is increasingly positioning itself as a full AI systems company rather than a pure GPU supplier.
The presentation came at a moment of higher investor scrutiny. After Nvidia became the first company to reach a $5 trillion valuation in October 2025, some investors began questioning how durable its growth would remain and whether its heavy reinvestment across the AI ecosystem would continue to pay off. The Financial Times reported that Huang’s trillion dollar forecast helped calm some of those concerns, even as Wall Street remains more conservative in its medium term revenue expectations.
Analysts said the message from GTC was that Nvidia sees inference as the next major battleground in AI infrastructure. Reuters and other market coverage noted that Huang no longer presents Nvidia mainly as a chipmaker unveiling a single new GPU, but as a builder of complete AI platforms, combining processors, networking, software and integrated rack scale systems.
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