NVIDIA announced on September 3, 2026 that it has agreed to acquire Hugging Face, the platform where much of the world’s machine-learning community shares its work, for US$12.9 billion — NVIDIA’s own announcement puts the exact figure at US$12.93 billion. The transaction is expected to close in the first half of 2027, subject to regulatory approval and other customary closing conditions.
Founded in 2016, Hugging Face has grown into something like a GitHub for AI. The scale of what NVIDIA is buying is easiest to grasp in numbers:
- more than 18 million developers, researchers and creators use the platform;
- they share more than 3 million models, 500,000 datasets and 1 million applications;
- more than 200,000 companies use it to discover, evaluate, customize and deploy AI.
NVIDIA is no stranger here: the company describes itself as the largest contributor of data and open models to Hugging Face, having released more than 500 models and 250 open datasets on the platform.
The obvious worry is what happens to an open commons when the dominant AI chipmaker owns it. NVIDIA CEO Jensen Huang addressed it head-on: “Hugging Face will remain an open platform for the entire AI ecosystem,” he said, adding that “NVIDIA compute will not be required to build on or deploy through Hugging Face.” Per Huang, “developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want and the computing platforms they want.” The company says multi-cloud and multi-accelerator development will continue, and Hugging Face will keep its 🤗 brand.
Hugging Face co-founder and CEO Clément Delangue framed the sale as a scaling decision rather than an exit. “Ten years after starting Hugging Face, open-source AI is at an inflection point,” he said, arguing that for open AI to happen at larger scale “it needs more compute, more support, more collaboration, and more visibility.” Huang, for his part, said he was “honored that Clem came to me as he considered the next chapter of Hugging Face and believed NVIDIA would be a great home for the company,” promising to scale the platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide.
NVIDIA’s stated rationale leans on the same openness argument: open models let startups, businesses, universities and public institutions build on advanced capabilities without training every model from scratch. Whether a US$12.9 billion price tag and those promises can coexist for the long haul is the question the entire AI ecosystem — including NVIDIA’s competitors whose accelerators are supposed to remain welcome — will be watching closely once the deal closes.
