Artificial Intelligence, Technology News

NVIDIA revenue surges 106% to $96.2 billion in latest quarter

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August 26, 2026 by Ivy Delaney

NVIDIA reported second quarter revenue of $96.2 billion, a 106% increase year-over-year, as demand for AI infrastructure continues to surge. The company states that artificial intelligence has “reached its inflection point,” moving beyond experimentation to deliver productive and profitable results. Jensen Huang, founder and CEO of NVIDIA, said, “AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.” The current buildout is being supported by the full production of the Vera Rubin platform and bolstered by strategic partnerships mobilizing over $500 billion in third-party capital.

Record Q2 Fiscal 2027 Revenue of $96.2 Billion Driven by AI Demand

NVIDIA’s current production relies heavily on the Vera Rubin platform, now operating at full capacity to meet escalating demand for artificial intelligence infrastructure. Both GAAP and non-GAAP gross margins for the quarter reached 75.0%, indicating strong profitability alongside this rapid expansion. NVIDIA distributed approximately $26.0 billion to shareholders through share repurchases and dividends during the second quarter of fiscal 2027.

As of the end of the second quarter, the company had approximately $99.0 billion remaining under its share repurchase authorization. NVIDIA will pay its next quarterly cash dividend of $0.25 per share on October 1, 2026, to all shareholders of record on September 10, 2026.

NVIDIA anticipates revenue of $108.0 billion for the third quarter of fiscal 2027, with a potential variance of plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 74.0%, with a potential variance of 50 basis points, suggesting continued high profitability despite increased scale.

AI has reached its inflection point. It’s doing useful work. Its tokens are productive and profitable. Now, compute is revenue.

Jensen Huang, founder and CEO of NVIDIA

NVIDIA Vera Rubin Platform and Blackwell Benchmarks Expand AI Infrastructure

The NVIDIA Vera Rubin platform is now operating at full capacity, supporting a surge in demand from partners including CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, and Nebius. This ramp-up in production was specifically designed to meet the current acceleration in AI infrastructure requirements, according to the company. NVIDIA has also secured land, power, and shell capacity through a partnership with SB Energy at the PORTS-Pike Technology Campus in Ohio to further host NVIDIA compute resources.

Performance benchmarks demonstrate NVIDIA’s continued dominance in AI processing; the Blackwell architecture led across all categories in MLPerf Training 6.0 benchmarks and also in AgentPerf, the first industry benchmark for agentic AI infrastructure, the company says. NVIDIA is expanding beyond hardware with new software, open source models, and partnerships with leading software platform providers to build autonomous AI agents for various industries and enterprises.

This includes an expanded NVIDIA Agent Toolkit featuring NVIDIA PhysicsNeMo and updated CUDA-X libraries. The company stated, “Vera Rubin, now in full production, was built to power exactly this moment,” underscoring the strategic investment in scaling infrastructure to meet growing needs.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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