Nvidia Q1 beat: $81.6B revenue, ~$91B Q2 guidance

Nvidia reported fiscal 2027 Q1 EPS of $1.87 and revenue of $81.6B, beating estimates and issuing roughly $91B in Q2 revenue guidance.

Nvidia reported fiscal 2027 first-quarter earnings per share of $1.87 and revenue of $81.6 billion, beating Wall Street estimates. The company issued roughly $91 billion in revenue guidance for fiscal Q2. The results were posted after the bell on Wednesday.

The quarter was widely watched for signs about the semiconductor and technology sectors. Before the release, Cinthia Murphy, director of research at TMX VettaFi, observed: “Sometimes it feels like it’s Nvidia’s world, and we are all just living in it.”

Nvidia has expanded from supplying chips for gaming into selling processors and systems for artificial intelligence infrastructure. The company reports higher gross and net margins than many S&P 500 peers and has converted a large share of revenue into free cash flow.

Market observers cite high return on equity, strong return on invested capital, steady year-over-year earnings growth and low balance-sheet leverage as measures used to assess the company. Those measures are commonly applied by indexes and ETFs that screen for quality characteristics.

Nvidia appears in more than 2,000 exchange-traded funds, according to ETF Database data. Its market capitalization rose from about $336 billion in late 2022 to roughly $5 trillion, a gain of about 1,400% over five years.

On May 20, 2026, Nvidia accounted for about 8.16% of the Fidelity Quality Factor ETF (FQAL), roughly 4.03% of the VictoryShares Free Cash Flow Growth ETF (GFLW) and about 3.46% of the American Century U.S. Quality Growth ETF (QGRO). GFLW emphasizes free cash flow return on invested capital, FQAL ranks companies by return on equity and stable earnings, and QGRO divides its portfolio between established compounders and faster-growing innovators.

The Q1 results and the company’s Q2 revenue guidance add data that index providers and ETF managers use when applying rules-based methodologies to set holdings and weights.

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