Decart Raises $300M to Break NVIDIA's CUDA Lock-In — And NVIDIA Invested Anyway

Correction (October 9, 2026): An earlier version of this article described Decart as one year old and "12 months old"; the company was founded in 2023. It also called the round a Series B (Decart's Series B was its August 2025 raise), said DOS moves models between chips "without rewriting any code," and attributed views on world models to Andrej Karpathy that we could not source. Those claims have been corrected or removed.

Decart, an Israeli AI lab founded in 2023, announced on May 18 a $300 million funding round led by Radical Ventures, with NVIDIA joining as a new investor and existing backers Sequoia Capital, Benchmark and Zeev Ventures participating alongside strategic investors including Adobe Ventures, Toyota Ventures and eBay Ventures. The round values the company at about $4 billion, according to The Wall Street Journal as cited by Globes — up from the $3.1 billion valuation of its $100 million Series B in August 2025. What makes the raise noteworthy isn't just the size. It's who wrote the check. NVIDIA, whose software lock-in Decart's main product is built to loosen, invested.

The product at the center of this is called the Decart Optimization Stack, or DOS. The name is almost certainly a deliberate provocation. DOS is a hardware-agnostic inference and training optimization platform that runs across NVIDIA GPUs, Google TPUs, and Amazon Trainium, cutting the work of optimizing a model for each chip. With the new version, DOS 2.0, Decart claims 1,600 tokens per second for agentic inference workloads — against an industry average Decart pegs at around 200 — and full-HD video inference at up to 100 frames per second. The pitch is simple: stop optimizing for one chip vendor and start treating compute as a commodity.

The CUDA Problem

NVIDIA's dominance in AI compute is not just about building faster chips. It is about CUDA, the proprietary programming environment that every AI developer learns first and never quite leaves. CUDA has been the defining moat of the AI era — not because developers love it, but because rewriting workloads for other architectures is genuinely painful. AMD, Amazon's Trainium and Google's TPUs all compete on performance or price for specific workloads. None of that matters if the engineers who actually ship production AI systems have CUDA-trained muscle memory and cannot afford the months it takes to port.

DOS is a direct attack on that friction. By abstracting the hardware layer, Decart says it compresses a months-long optimization workflow into a few weeks and lets teams move their models between chip vendors with minimal effort. It is the kind of software that makes NVIDIA's hardware competitors suddenly viable at scale. Every company that adopts DOS is one fewer company entirely dependent on H100 allocation and CUDA familiarity.

Why NVIDIA Invested

The counterintuitive move in this round is NVIDIA writing the check. It looks, at first pass, like a company funding its own obsolescence. The likely logic is more interesting. NVIDIA has seen what happened to Intel — a hardware company that believed its architecture was the product, right up until software abstraction made the underlying silicon irrelevant. By investing in Decart, which it also counts as a strategic partner, NVIDIA buys visibility into the shift it knows is coming. It gains intelligence on the precise mechanics of how developers are routing around CUDA lock-in. And if DOS becomes the standard inference abstraction layer, NVIDIA's chips will still run on it — they just won't be the only option.

It is the same calculation that Intel made too late with x86 alternatives and that cloud providers made correctly when they invested in Kubernetes: when abstraction is inevitable, you want equity in the abstraction layer, not just in the hardware it runs on top of. Venture firms face a version of the same trade-off: Benchmark, one of Decart's backers, has since said that AI is eroding the industry's rule against funding competing startups.

World Models Beyond Optimization

DOS is the revenue product — Decart says it earns significant revenue from licensing it to cloud providers and AI labs — but Decart's research direction is arguably more ambitious. The company is building what it calls world models — AI systems that simulate physical and virtual environments in real time. Lucy, its real-time video world model, responds to user input in under 30 milliseconds, according to the company, and is aimed at uses like virtual try-on, live streaming and in-video advertising. Oasis is its counterpart for physical AI, aimed at robotics and autonomous-systems customers. That market is starting to draw public money too, with NVIDIA-backed Agility Robotics taking its Digit humanoid public through a SPAC. Decart says Lucy is already deployed with enterprise customers — if those claims hold, these are not just demos.

The individual investor list is a signal of how seriously the research community takes this trajectory. Andrej Karpathy — co-founder of OpenAI and former head of AI at Tesla — invested personally, alongside former Disney CEO Michael Eisner and members of Nintendo's founding Yamauchi family. A researcher of Karpathy's standing putting in his own money reads as a vote on the technical thesis, not just the business model.

What Comes Next

At $4 billion and less than three years old, Decart has the capital to move fast. The company is clearly positioning itself as the software layer that makes the next phase of AI infrastructure wars irrelevant to developers — a kind of CUDA replacement that does not care which chip company wins. If it succeeds, the economics of AI compute shift dramatically: NVIDIA keeps selling hardware but loses the premium that CUDA dependency allows; Amazon and Google finally get serious volume on their custom silicon; and AI teams stop having their deployment architecture determined by which vendor they happened to hire from.

The more immediate read is simpler: the AI chip industry is about to get much more competitive, and Decart just raised enough money to accelerate that outcome. NVIDIA knows it. That's why they're in the round.

Sources: The Next Web · SiliconANGLE · Globes · Jewish News · Fortune