What happened
Citi lifted its July-quarter revenue forecast for Nvidia to about $93 billion (roughly $1 billion above Wall Street consensus) and October-quarter revenue to $105 billion (about $1.5 billion above consensus, up 13% sequentially), citing strong ramps of B300 systems and faster-than-expected shipments of 1.6-terabit transceivers. The firm maintained its Buy rating and $300 price target but lowered its valuation multiple assumption to 24 times projected calendar 2027 earnings from 28 times.
Why it matters
Citi's forecast raises suggest Nvidia's AI networking and Blackwell chip momentum is outpacing market expectations. The brokerage also flagged that Nvidia has secured sufficient high-bandwidth memory supply for 2026 and 2027, potentially allowing analyst earnings estimates to move higher. Additionally, Nvidia's new financing role in infrastructure projects—supporting the Ohio PORTS-Pike campus with 4.25 gigawatts of initial IT capacity—may help ease financing constraints for AI labs and cloud providers expanding computing capacity.
What to watch
Citi expects Blackwell GPU shipments in fiscal 2027 to reach 7.7 million units, while lowering its Rubin estimate to 2 million units from 2.2 million because of tighter memory availability. Nvidia reports earnings next week, and the stock is expected to trade higher post-results.
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Citi's upgrade reflects a pattern of Nvidia's AI infrastructure shipments outpacing consensus expectations. The brokerage expects data-center revenue to rise 15% sequentially in the July quarter and 14% in the October quarter, compared with Street expectations of 13% growth in both periods—a meaningful outperformance. Citi raised fiscal 2027, 2028, and 2029 adjusted earnings-per-share estimates by 1%, 2%, and 2% respectively, underpinning its confidence in the revenue trajectory.
A secondary but notable development is Nvidia's pivot into infrastructure financing. By backing the Ohio PORTS-Pike campus project—providing 4.25 gigawatts of initial IT capacity—Nvidia is helping hyperscalers (large cloud providers) and AI labs manage near- to medium-term capital constraints. This could deepen Nvidia's entrenchment in the data-center ecosystem while addressing a structural financing bottleneck in the AI buildout.
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