AIToday
AI Stocks & MarketsAI Business & IndustryYahoo Finance AIPublished: May 8, 2026, 22:01 JST

NVIDIA closed FY2026 with $215.94B in revenue while Palantir reported Q1 2026 revenue of $1.633B, up 85% year over year

NVIDIA closed FY2026 with $215.94B in revenue while Palantir reported Q1 2026 revenue of $1.633B, up 85% year over year

3 Key Points

  1. NVIDIA's Data Center segment generated $62.31B in Q4 FY2026, up 75%, with networking surging 263% as NVLink fabrics shipped inside GB200 and GB300 racks. Non-GAAP EPS came in at $1.62, beating the $1.52 consensus. The company guided $78.0B for Q1 FY2027.

  2. Palantir's U.S. Commercial revenue hit $595M, up 133% year over year, with adjusted EPS of 33 cents beating the 28 cent estimate. The company raised its FY2026 revenue guide to $7.66B, reflecting roughly 71% growth, driven by AIP (its software platform for enterprise decision-making) deployments across Fortune 500 and U.S. government customers.

  3. NVIDIA is pursuing a capital-intensive strategy with $95.2 billion of supply commitments and multiyear deals with Meta, OpenAI, Anthropic, and CoreWeave, while Palantir operates capital-light with adjusted free cash flow of $925M at a 57% margin and a Rule of 40 score of 145%, historically reserved for chip companies.

Not sure about something? Ask the AI

Questions and answers are published on this page.

Summaries like this, in your inbox every morning.

Yahoo Finance AIRead Original Article

AI news that matters for your work, delivered every morning.

Pick your industry and the AI tools you use, and get news related to your work every day.

Free · 30 seconds with Google · unsubscribe anytimeWhat is AIToday? →

Ask AI

Ask AI anything about this article. The AI reads this article, earlier AIToday articles, and Wikipedia, and cites its sources. Q&As are published on this page for other readers too.

Questions and answers are published on this page.

Related Articles

Next articleRobot Talk Episode 155 features Melissa Greeff discussing autonomous navigation and learning for aerial robots