By Stuart Kerr, Technology Correspondent, LiveAIWire
Nvidia’s revenue-sharing model, announced on July 1, 2026, introduces a fundamental shift in how the company does business: from selling GPU hardware outright to taking a recurring share of the cloud revenue generated by the compute it enables. The announcement, made in a blog post by Chief Financial Officer Colette Kress and VP Raj Mirpuri, allows AI startups, model builders, enterprises, and regional cloud providers to access large-scale GPU infrastructure without the capital commitments that have historically priced out smaller players.
Sharon AI in Australia is already deploying up to 40,000 Nvidia Grace Blackwell GB300 GPUs under the model. Firmus Technologies is building a data centre campus in Batam, Indonesia expected to scale to 360 megawatts and up to 170,000 Nvidia GPUs.
The move arrives at a moment when Nvidia has already committed more than 53 billion dollars across roughly 170 AI-related investment deals since the start of the AI boom, according to PitchBook data. Its 30 billion dollar investment in OpenAI, announced in late February 2026, was one of the largest AI financing deals ever recorded. Revenue sharing is a different instrument entirely, not equity in specific companies but a structural claim on the usage economics of an entire computing ecosystem that Nvidia is positioning itself to supply.
Why the Capital Access Problem Exists
The core problem Nvidia’s revenue-sharing model addresses is structural. Large-scale GPU infrastructure requires enormous upfront capital, site selection, power procurement, construction, and hardware procurement that can take years to come online and hundreds of millions of dollars to finance before a single token is generated. Hyperscalers like Microsoft, Google, Meta, and Amazon can fund this from their balance sheets. Microsoft alone spent over 50 billion dollars on data centres in 2025. Startups and regional AI cloud providers cannot, and even long-term customer commitments have not consistently been treated as bankable assets by capital markets in the way that power purchase agreements are bankable for energy infrastructure.
Nvidia’s blog post was direct about this: emerging AI companies historically have had limited access to capital-intensive infrastructure, with even long-term commitments insufficient to unlock financing for compute. The revenue-sharing model is designed to solve this problem by substituting Nvidia’s balance sheet and credit for the startup’s, giving startups immediate access to full-stack accelerated computing without waiting through the site selection, power procurement, construction, and hardware bring-up cycle.
What Nvidia’s Revenue-Sharing Model Actually Does for Nvidia
The commercial logic from Nvidia’s perspective is equally clear. The revenue-sharing model creates a new recurring, usage-linked earnings stream, described in exactly those terms in Kress and Mirpuri’s blog post, that is a significant evolution from one-time hardware sales. Nvidia currently earns revenue when it sells GPUs. Under the new model, it also earns a share of the cloud revenue generated by those GPUs over their operational life.
The Blockchain.News analysis of the announcement framed this as Nvidia moving from hardware vendor to infrastructure partner with a stake in the outcome, a business model that more closely resembles the royalty model used by semiconductor IP companies like ARM than the traditional chip sale model. For a company that has already captured the dominant position in AI training hardware, extending that position into a recurring share of AI inference economics is the natural next step in the value chain. Nvidia is not just selling the picks and shovels. It is negotiating a royalty on the gold.
What This Means for AI Startups
For AI startups, Nvidia’s revenue-sharing model lowers the barrier to accessing large-scale compute but introduces a new structural cost: a share of revenue flowing to Nvidia in perpetuity, on top of the cloud service costs that would exist in any case. Whether Nvidia’s revenue-sharing model represents a good deal depends on the counterfactual. A startup that could not access large-scale compute at all, or that would have to raise dilutive equity capital to fund hardware purchases, may find that sharing a percentage of revenue with Nvidia is cheaper than the alternatives.
The companies Nvidia cited as early beneficiaries of the model, Baseten, Fireworks AI, and Together AI, are inference and model deployment platforms that serve developers, digital natives, and enterprises building AI applications. These are companies whose revenue scales with usage of the underlying compute. For them, a usage-linked payment to Nvidia is structurally aligned with their own revenue model: they pay more when they earn more. For companies with more complex revenue structures, or where the path from compute usage to revenue generation is longer and less predictable, the model’s terms matter significantly more, a distributional question that runs through LiveAIWire’s broader coverage of AI power concentration.
The Geopolitical and Regional Dimension
Firmus Technologies’ Batam, Indonesia campus is specifically described as serving AI-native companies that need scalable, energy-efficient compute infrastructure to compete globally. Countries and regions that have not had access to hyperscaler-scale AI infrastructure because no hyperscaler has chosen to build data centres there are now potential beneficiaries of the Nvidia-partnered AI factory model. The capital access problem is even more acute for regional players than for US startups, because they also face the challenge of convincing global hyperscalers that their markets are worth the investment.
Nvidia’s willingness to take a revenue share rather than an upfront payment makes the economics of building regional AI infrastructure viable in markets where the revenue trajectory is less certain. It also extends Nvidia’s reach into markets that competing hardware vendors might otherwise be able to address, because the revenue-sharing model only works for Nvidia if the infrastructure is built on Nvidia GPUs, which is the condition of access. This same question of who controls access to frontier AI infrastructure runs through LiveAIWire’s coverage of Austria asking the EU to host Anthropic, where a national government concluded that dependence on foreign-controlled AI infrastructure is itself a strategic vulnerability.
The Compute Concentration Question
Nvidia’s revenue-sharing model sits within a broader pattern of concentrated control over the physical infrastructure that AI depends on. LiveAIWire’s coverage of SpaceX’s Colossus commercial compute platform documents a parallel dynamic in which a small number of companies with the capital and infrastructure scale to build frontier AI compute are becoming gatekeepers for the entire industry, regardless of whether that gatekeeping happens through direct ownership or through financing arrangements like Nvidia’s.
The Risk That Comes With Nvidia’s Revenue-Sharing Model
Nvidia’s revenue-sharing model is not without structural risks. Nvidia is extending something analogous to vendor financing, accepting deferred revenue in exchange for a future share of cloud revenue rather than receiving full payment upfront. If the AI cloud companies that build Nvidia-powered factories do not generate the cloud revenue that the model anticipates, Nvidia’s recurring earnings stream from those factories will be lower than projected. That assumption is consistent with the consensus view of AI industry growth, but it is an assumption rather than a certainty.
The companies that are building under the model, Sharon AI, Firmus, are committing to large GPU deployments at a moment when the AI infrastructure investment cycle is at a historically high intensity. If that cycle moderates, or if competing hardware vendors reduce the cost of alternative compute significantly, the terms of Nvidia’s revenue-sharing model may need to evolve. Nvidia’s revenue-sharing model rests on a bet that its dominance in AI hardware, the foundation of its extraordinary market valuation, can be extended from hardware into the recurring economics of AI services. It is a well-constructed bet. It is not a guaranteed one.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity, and the social impact of emerging technology. He publishes daily at LiveAIWire.com.