Nvidia has posted record quarterly revenue of $81.6 billion for Q1 2027, up 85 percent from the previous year and 20 percent from the prior quarter.

Data center revenue for the three-month period ending April 26 totaled $75.2bn, representing yearly and quarterly increases of 92 percent and 21 percent, respectively.

However, in addition to announcing its earnings, Nvidia said it would be transitioning to a new reporting framework that “better reflects its current and future growth drivers” – breaking its results into data center and Edge computing segments.

Within its data center framework, Nvidia will report two submarkets, Hyperscale and ACIE (AI Clouds, Industrial, and Enterprise). Hyperscale will include revenue from public clouds while ACIE will cover purpose-built AI data centers and so-called AI factories.

The Edge computing segment, meanwhile, will incorporate data processing devices for agentic and physical AI, including PCs, game consoles, workstations, AI-RAN base stations, robotics, and automotive.

Under the previous sub-markets Nvidia used for its financial reporting, data center compute revenue was recorded at $60.4bn for the quarter, up 77 percent Year-on-Year (YoY) and 18 percent Quarter-on-Quarter (QoQ). Q1 data center networking revenue was also record-breaking, up 199 percent YoY and 35 percent QoQ to $14.8bn.

First quarter Edge computing revenue was $6.4bn, up 29 percent YoY and 10 percent QoQ.

Nvidia is forecasting second-quarter revenue of $91bn, plus or minus two percent, with the company noting it is not recording any anticipated compute revenue from China in its projections.

“The buildout of AI factories — the largest infrastructure expansion in human history — is accelerating at extraordinary speed,” said Nvidia CEO, Jensen Huang. “Agentic AI has arrived, doing productive work, generating real value, and scaling rapidly across companies and industries. Nvidia is uniquely positioned at the center of this transformation as the only platform that runs in every cloud, powers every frontier and open source model, and scales everywhere AI is produced — from hyperscale data centers to the Edge.”