Nvidia reported fiscal 2027 second-quarter revenue of $96.2 billion, more than doubling from a year earlier, while signaling that the rapid rollout of its Vera Rubin AI platform is bringing higher component costs into sharper focus. The company expects Rubin systems to generate roughly 20% of data center revenue in the current quarter, tying its next phase of growth to a product transition that is also expected to reduce gross margins.
Data center revenue reached $89.0 billion in the quarter, up 117% year over year, according to Nvidia’s earnings release. That business supplied the overwhelming majority of the company’s sales as cloud providers and other large customers continued to build AI computing clusters. Non-GAAP diluted earnings per share came in at $2.22.
Nvidia forecast fiscal 2027 third-quarter revenue of about $108.0 billion, with a range of 2% above or below that figure. The outlook excludes China data center compute revenue, leaving any potential sales in that market outside the company’s baseline projection.
Rubin production lifts revenue expectations
Nvidia said Vera Rubin has entered full-volume production. The company named CoreWeave, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure and Nebius as operators of Rubin systems, giving the platform an early footprint across some of the largest providers of rented AI computing capacity.
The Rubin launch extends Nvidia’s effort to sell more than graphics processors into each AI deployment. The company also pointed to Spectrum-6 networking systems as part of the Rubin platform, reflecting how modern AI clusters rely on networking, optical connections, advanced packaging and high-bandwidth memory alongside the central accelerators.
That broader system design raises the dollar value of an AI cluster, but it also exposes Nvidia and its customers to constrained supplies of specialized components. AI servers require large quantities of high-performance memory that can move data quickly between processors. As more cloud operators bring large systems online at once, the cost and availability of that memory becomes a direct issue for hardware makers’ profitability.
Nvidia said demand is extending beyond frontier AI labs and major cloud providers into enterprise customers, sovereign AI programs and “physical AI” deployments, a term generally used for machines such as robots and autonomous systems that interact with the real world. Those markets can create additional demand for computing infrastructure, though they may have different purchasing cycles and deployment requirements than large cloud platforms.
Gross-margin outlook reflects component costs
The company guided for a non-GAAP gross margin of about 74.0% in the fiscal third quarter, with a 50-basis-point range around the forecast. That would be lower than the 75.0% non-GAAP gross margin reported for the second quarter.
Nvidia also said rising memory and component costs could reduce non-GAAP gross margin further, to around 71% to 72% in the fiscal fourth quarter. Gross margin measures the share of revenue left after the direct costs of making and delivering products. A decline from 75% to the low-70% range would still leave Nvidia with an unusually high margin for a hardware company, but it would mark a meaningful change after a period in which AI demand helped lift both sales and profitability.
The figures point to a practical constraint on the AI infrastructure boom: chip supply is only one part of the buildout. Advanced memory, networking equipment, optical links, power systems and packaging capacity must scale alongside accelerator production. Nvidia’s latest outlook suggests those inputs are becoming more expensive as Rubin systems move from early deployment to volume manufacturing.
For cloud providers, higher costs could affect the economics of offering AI computing to customers. Operators may absorb some of the cost, seek better utilization from their systems, or pass through higher prices for certain AI services. Nvidia’s named Rubin customers include companies that sell computing capacity directly to developers and enterprises, placing them near the point where infrastructure expenses can filter into commercial AI pricing.
China remains outside the baseline forecast
The decision to exclude China data center compute revenue from third-quarter guidance adds another variable to Nvidia’s outlook. The company did not include that revenue in its $108.0 billion forecast, meaning the guidance rests on demand from other markets and customers.
That approach separates the company’s core AI infrastructure demand from uncertainty around its ability to sell advanced computing products into China. For traders assessing Nvidia’s next results, the distinction means reported sales could be affected by developments not embedded in the stated baseline, while the margin outlook already incorporates the cost pressures Nvidia expects from its product mix and supply chain.
Implications for crypto-linked AI narratives
Nvidia’s results do not establish a direct trading relationship between major cryptocurrencies and AI hardware stocks. Claims that digital assets have decisively detached from technology equities, or have become a consistent inflation hedge because of hardware-cost pressure, require broader market evidence than Nvidia’s earnings figures provide.
The earnings report does offer a more grounded reference point for crypto markets that follow AI-related tokens, decentralized computing projects or publicly traded mining and infrastructure companies. Those sectors often depend on the same practical inputs now under pressure in Nvidia’s supply chain: specialized chips, memory, power capacity, networking hardware and data-center space.
A higher-cost AI buildout could therefore favor projects and companies that already control usable computing resources, while making new infrastructure expansion more expensive. It could also raise the value of clear disclosure. Projects presenting themselves as decentralized AI networks will face closer scrutiny over whether they can secure hardware, operate it economically and deliver computing capacity at competitive prices.
Nvidia’s third-quarter results will show whether Rubin’s move into volume production can sustain its revenue acceleration while the cost of building AI systems begins to claim a larger share of each dollar of sales.
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