NVIDIA has agreed to acquire Hugging Face for $12.93 billion to scale the open-source model repository’s platform and infrastructure.

The transaction targets platform growth and infrastructure investment, aiming to expand AI access for enterprise developers, software engineers, and research institutions globally.

Built over the past decade by Clem Delangue, Julien Chaumond, Thomas Wolf, and their engineering team, Hugging Face serves as the primary home for the open model developer community.

Platform metrics show more than 18 million developers, researchers, and creators share more than three million models, 500,000 datasets, and one million applications. Commercial adoption includes more than 200,000 companies using the environment to discover, evaluate, customise, and deploy AI models.

Hardware neutrality and multi-cloud commitments

NVIDIA stated that Hugging Face will remain an open platform for the entire AI sector. Developers will retain full control over their selection of models, software frameworks, cloud providers, inference services, and computing hardware.

NVIDIA hardware will not be mandatory to build on or deploy software through the platform. The service will maintain operational support for alternative accelerators, multi-cloud architectures, and open-weight models from all third-party builders.

Jensen Huang, Founder and CEO of NVIDIA, said: “Hugging Face will remain an open platform for the entire AI ecosystem. 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.

“NVIDIA compute will not be required to build on or deploy through Hugging Face.”

Open-weight models and distributed development

Huang noted a recent open letter he co-authored regarding the role of open weights in the AI economy. The position argued that open weights broaden AI access and ensure technical leadership remains distributed across companies, academic institutions, and developer communities.

Under this operational model, commercial businesses, startups, universities, and public bodies can build on advanced capabilities without the expense of training baseline models from scratch.

The approach allows organisations to match specific models to operational tasks across factories, hospitals, farms, classrooms, and commercial businesses, while addressing cybersecurity and data sovereignty requirements.

Julien Chaumond, Co-Founder and CEO of Hugging Face, commented: “AI is at an inflection point. Open-source AI can become less relevant in the coming years if the big closed labs run away with it, or it can become the foundational fabric of the next phase of human civilisation.

“Those are vastly different outcomes, and we need the critical mass to ensure we give our collective best shot to the second outcome. Given Jensen Huang’s stance on open source AI and how he stepped up to defend it when it was under threat earlier in the summer, NVIDIA was the only partner we truly considered.”

Infrastructure expansion and brand preservation

NVIDIA stands as the largest contributor of open models and data to Hugging Face, with a portfolio of more than 500 open models and more than 250 open datasets. The company builds its libraries, tools, and models openly to allow external engineers to inspect, modify, and build atop the software.

Technical integration will focus on applying NVIDIA infrastructure and engineering resources to improve repository reliability, safety controls, model evaluation tooling, inference execution, and deployment pipelines.

“I am 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, its community and the future of open models,” Huang stated.

Hugging Face will retain its independent brand identity following the completion of the transaction, with the existing team continuing operations across multi-cloud and multi-accelerator environments.

“This gives fuel to our long-term vision and mission of unlocking the community’s progress to ensure that AI, which is the greatest breakthrough of our lifetime, is accessible to as many people as possible,” Chaumond concludes.

See also: Motional and MIT AI explains self-driving car decisions

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President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip exports. Nothing did. Not a single Nvidia H200 has shipped to China since Trump first authorised the sales in December 2025, and US Trade Representative Jamieson Greer told Bloomberg that semiconductor controls were not even on the bilateral agenda. 

The summit theatre obscured a more interesting development underneath it. The H200 isn’t stuck because Washington won’t allow it. Washington already has allowed it. Roughly 10 Chinese firms, including Alibaba, Tencent, ByteDance, and JD.com, hold approved US export licences for up to 75,000 units each, with Lenovo and Foxconn authorised as distributors. The chips aren’t moving because Beijing won’t let its own companies take delivery.

Two frameworks, one deadlock

The mechanics of the stalemate are worth understanding clearly. US rules require that all H200 chips ordered by Chinese clients be used only in China. Beijing, meanwhile, has instructed Chinese tech companies to limit their use of Nvidia chips to overseas operations while supporting domestic manufacturing. The two requirements are mutually exclusive. 

Chips cleared for export cannot legally be deployed where Beijing wants to deploy them, and Beijing won’t authorise the domestic use the US licences require, according to Implicator.

Commerce Secretary Howard Lutnick stated at a Senate hearing last month that Chinese firms are trying to keep their investment focused on domestic suppliers, including Huawei. Beijing’s State Council has also ordered a supply-chain security review aimed at cutting dependence on US semiconductors. 

The policy contradiction is not accidental. That is the point.

What Huawei gained while diplomats talked

The days around the summit produced several data points that matter more for the long term than Trump’s parting comment. DeepSeek confirmed its latest model had been optimised to run on Huawei processors. Tencent’s chief strategy officer said Chinese GPU supply would increase progressively through 2026, and an Alibaba executive said its T-Head proprietary GPUs had achieved scaled mass production. 

This follows the April launch of DeepSeek V4, which adapted the model for Huawei’s Ascend chips – the first major Chinese frontier model to do so in training, not just inference. What the summit week confirmed is that the shift is no longer experimental. It is now a supply-chain policy. Nvidia’s China revenue has fallen to roughly 5% in recent quarters, down from above 20% before export controls tightened. The company’s own guidance for the current quarter assumes zero revenue from China. 

Huang’s last-minute inclusion in the delegation – Trump called him directly after seeing media coverage that he had not been invited – suggested urgency. The outcome suggested the limits of what CEO diplomacy can achieve when the obstruction is structural, not procedural.

The read for the AI industry

The stalemate matters beyond bilateral optics. Chinese AI platforms are now operating under a domestic mandate to build on Huawei’s compute stack. The question of which AI hardware architecture becomes dominant in the world’s second-largest AI market is being answered not by technical benchmarks but by government directive.

Beijing steering platforms toward Huawei Ascend chips rather than Nvidia H200S is not just a trade posture. It is a structural bet that the performance gap will close fast enough that being locked into the domestic stack is manageable. DeepSeek V4’s results suggest it may be right, at least for inference workloads. 

Trump said something could happen. Greer said the decision is sovereign for China. Both are true, and neither changes the current position: the H200 deal is approved, licensed, and frozen, with Huawei filling the space it leaves behind.

(Image source: The White House)

See Also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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Nvidia CEO Jensen Huang said he expects $1 trillion worth of orders for the chips.

GTC is Nvidia's flagship annual event, where the chipmaker typically announces new products, partnerships, and its vision for the future of computing. Huang's keynote will focus on Nvidia's role in the future of computing and AI.