Nvidia buys Hugging Face for $12.93 billion — deal expected to close in 2027
Nvidia has signed a definitive agreement to acquire Hugging Face, the platform where millions of developers find, test and distribute open AI models, for $12.93 billion.
That means the company is paying nearly $13 billion for a business that, according to Reuters, generated roughly $150 million in annualized revenue — a multiple of about 86 times revenue. On the surface, the deal looks inexplicable. The view behind the numbers is about something else: not financing, but distribution to the developers who build and run open models, at a moment when Nvidia's largest customers are building their own chips to reduce their dependence on the company.
The terms of the deal
The agreement was signed on September 2. Under the terms, $11.9 billion goes to Hugging Face's shareholders, while up to around $1 billion is set aside in an equity-based retention program for employees — a signal that Nvidia is paying a substantial premium for the team and the platform's continued operation, not just for ownership on paper (Barchart).
Closing is expected in the first half of 2027, subject to all regulatory approvals. That is an expectation, not a certainty: an acquisition of this size, in which a dominant chip supplier buys the de facto center of gravity for open AI development, is a textbook candidate for thorough competition review on both sides of the Atlantic.
One caveat is worth noting, because it affects how firm the foundation is: the concrete deal facts — the date, the price, the terms and the timeline — rest in this case on three mutually consistent secondary sources (Barchart, Yahoo Finance/TIKR and The Motley Fool), not on Nvidia's own announcement or the merger agreement. The figures match across the sources, but they have not been verified against primary documents.
The number that creates the puzzle
The reason the acquisition raises eyebrows is the ratio of price to revenue. Hugging Face generated roughly $150 million in annualized revenue around the time the deal was signed, according to Reuters as cited by Barchart. With a price of $12.93 billion, that implies a multiple of about 86 times annualized revenue. That figure should be read as an approximate calculation based on the Reuters number, not as verified arithmetic from the company's own financials — which are not available in public form.
The counterpoint is Nvidia's own scale. In the second quarter of fiscal year 2027, the company recorded $96.2 billion in revenue, up 106 percent year over year, of which $89.0 billion came from the data center division. Gross margin was 75.0 percent and adjusted earnings per share were $2.22 (Yahoo Finance). Against that backdrop, $12.93 billion is a small share of Nvidia's quarterly revenue. The question is not whether Nvidia can afford it — it is what the purchase buys that chips and GPUs cannot.
The ecosystem logic: distribution, not revenue
The answer lies in who gathers on Hugging Face. Nvidia itself states that over "18 million developers, researchers and creators use Hugging Face to share more than 3 million models, 500,000 datasets and 1 million applications," and that more than 200,000 companies use the platform for various AI-related tasks (Barchart). These are the company's own figures, but the direction is clear: this is where open AI development actually happens.
CFRA analyst Angelo Zino framed it directly to Yahoo Finance: the acquisition is "less about the financials" and more about "building that AI ecosystem," which "ultimately translates into greater adoption" of what Nvidia sells (Yahoo Finance).
The platform's growth underpins that reading. The number of public model repositories on Hugging Face grew from 2.43 million to 2.96 million during the first seven months of 2026, and chipmakers increasingly use open models to showcase their chips and drive adoption (Yahoo Finance). In other words: Hugging Face is where developers decide which models become popular — and popular models have to run somewhere. Owning that meeting point means owning the distribution channel for an entire industry's workloads.
Structurally, the parallel is Microsoft's acquisition of GitHub in 2018: a platform-intermediary purchase where the value lies not in the financials but in the position as a neutral gathering point for developer behavior. In practice GitHub remained where the code lives, and Microsoft gained a front-row seat to the developer economy. The question for Nvidia is the same as it was then: whether neutrality survives ownership.
The threat the purchase answers
Nvidia's strategic starting point is not just growth, but vulnerability. On September 22, David Russell at TradeStation warned that customers, including Meta and Alphabet, are building their own chips to cut their dependence on Nvidia — a shift he expects will pressure both market share and margins over time (Yahoo Finance).
The remarkable thing is that Nvidia's own risk reporting points in the same direction, mirrored: open models could reduce demand for the company's products if they run primarily on competing hardware (the risk, as Yahoo Finance characterizes it from Nvidia's filing). In other words: open AI can either strengthen Nvidia, by keeping workloads on Nvidia's software stack and chips, or undermine it, by making models portable enough to run just as well on Amazon's, Meta's or Alphabet's own accelerators. Hugging Face is where that contest is decided in practice, because it is where the models are built, shared and chosen.
Ownership of the platform gives Nvidia a grip on this mechanism: optimizations, documentation, reference implementations and standard workflows could in principle be oriented toward Nvidia's stack. It is precisely this possibility that makes the acquisition both valuable and controversial.
Huang's "one platform"
The strategy dovetails with Nvidia's own narrative. On the fiscal Q2 earnings call, CEO Jensen Huang described Nvidia as "one platform, fungible for every model and workload, durable for the entire life cycle of AI" (Yahoo Finance). "Fungible for every model" presupposes that the models exist somewhere in common — and Nvidia now owns the place.
Capital allocation on a grand scale
The purchase must also be read as part of a much larger pattern of capital deployment. Nvidia spent over $42 billion buying equity stakes in other companies in the first half of fiscal 2027, up from around $1.2 billion a year earlier (The Motley Fool). Against that picture, the Hugging Face acquisition is not an alien element in a chip business, but a deliberate move in a portfolio where Nvidia is actively buying positions across the entire AI value chain — from chips to software to, now, the distribution layer for models itself.
The market's immediate reaction was restrained. The stock rose about 1.8 percent to $228.45 in the session after the deal became public, beating the S&P 500 by less than one percentage point (Yahoo Finance). With $150 million in revenue against $96.2 billion in quarterly revenue, little suggests the market sees the deal as transformative for near-term results. The real assessment lies further out, when closing approaches and it becomes possible to see how the platform is actually run.
What "open" means under a chipmaker
Here lies the core of the analysis. Nvidia has reportedly said the platform will remain open to all chips and clouds — a statement Yahoo Finance reports without quoting verbatim, and one that analysts are precisely watching: how "open in principle" translates into practice.
The tension is structural, not hypothetical. Nvidia's own filing acknowledges that open models could reduce demand if they run on competitors' hardware. At the same time, the entire value of Hugging Face as a distribution platform depends on developers continuing to trust it as the place where their work gets seen and used, regardless of which chip they choose. If the platform gradually favors Nvidia's stack — in optimizations, in which models get showcased, in which runtime environments are well documented — Nvidia risks draining the very asset it just paid 86 times revenue for. Developers have historically shown they can abandon platforms that stop acting neutral; the question is how costly such an exodus would be, and whether real alternatives with comparable network effects exist.
It is also possible that neutral operation is optimal for Nvidia itself: the more open models gather on the platform, the more work flows toward Nvidia's dominant accelerator installed base, whatever competitors build. Under that reading, the acquisition is a defensive strategy that pays off best when it is not exploited aggressively.
The open questions
Three factors will determine whether the price was rational:
Regulatory outcome and timing. The closing in the first half of 2027 is an expectation, not a guarantee. Competition authorities will assess whether the dominant chip supplier can own the dominant open-model platform without harming the competitiveness of alternative chip architectures.
Developer loyalty. Nvidia's own figures — 18 million developers, 200,000 companies — are the value in the deal, but those numbers are only valuable if they stay under an ownership that sells what they run on. The retention program of up to $1 billion secures employees, not users.
Practical neutrality. Whether "open to all chips and clouds" means equal treatment in practice — in optimizations, in highlighting, in standard tooling — is impossible to answer before the deal closes and platform governance can be observed over time.
Until then, the reasonable picture is this: Nvidia has paid a price that only makes sense if one regards Hugging Face as the gateway to the next generation of AI workloads — and has done so precisely because the gateway, had it fallen into others' hands or been held neutral independently, could have steered work away from Nvidia's chips. The purchase is cheap relative to Nvidia's coffers, expensive relative to Hugging Face's revenue, and rational only under one condition: that the platform remains where the entire industry gathers, while the work done there largely ends up on Nvidia's hardware. Achieving both at once is the whole strategy's bind.