Nvidia to Acquire Hugging Face for $12.93 Billion in Bet on Open AI Models

Jensen Huang, Nvidia’s chief executive, said Hugging Face will retain its brand, leadership and neutrality. “Hugging Face will remain an open platform for the entire AI ecosystem,” Huang said. “Nvidia compute will not be required to build on or deploy through Hugging Face.” The platform hosts more than 3 million models, 500,000 datasets and 1 million applications, used by more than 18 million developers and researchers and more than 200,000 companies.

Hugging Face was valued at $4.5 billion in its last disclosed round in 2023 and is estimated to have reached roughly $150 million in annualized revenue this year. Clement Delangue, Hugging Face’s co-founder and chief executive, said the goal is to grow from 18 million builders to 100 million “in the next few years” and that Nvidia’s resources let the company “think about the next 10 years and really optimize for maximum impact.” Justin Boitano, Nvidia’s vice president of enterprise AI, described the deal as helping Hugging Face “achieve stuff that we don’t think either of us would have been able to achieve alone.”

SiliconANGLE reported that the distributed layer of open AI had been underfunded relative to its importance. Nvidia’s stated investment areas, including reliability, safety, model evaluation, inference and deployment, align with the friction enterprises face when moving an open model from a notebook into production. The bottleneck in enterprise AI is no longer model quality, the report said; open-weight models now approach the quality of proprietary models at far lower cost. The bottleneck is everything around the model, from choosing one and proving its safety to serving it and defending the choice to a risk committee.

Better evaluation tooling shortens selection cycles from months to weeks, real supply-chain security such as signing, provenance and scanning removes objections that stalled open-model projects in regulated industries, and turnkey inference paths close the gap between testing and deployment, the report said. Boitano said open models let “every company build their own domain-specific intelligence” and own the competitive advantage of their business. Delangue argued that power in AI is concentrating in proprietary application programming interfaces and that an open model hub is “structurally, by definition, a de-concentration platform.”

Nvidia had already backed open weights: In July, Huang used his first-ever X post to share an open letter on open weights and U.S. AI leadership co-signed by more than 20 organizations. Nvidia is the largest contributor of open models and data to Hugging Face, with more than 500 models and 250 open datasets published on the platform. The report noted an obvious tension in a $5.4 trillion company positioning itself as the champion of de-concentration, but said Nvidia’s incentives differ from a model lab’s: it does not need any single model to win, but needs many models trained, fine-tuned and served everywhere.