NVIDIA Is Buying Hugging Face for $12.9B, and That Includes the llama.cpp Team

Quick verdict

NVIDIA is buying Hugging Face. Business Insider first reported talks for more than $13 billion, then The Information said the deal is agreed at $12.9 billion. If it closes, the company that sells the GPUs also owns the default place people download open model weights, datasets, and code. The reaction from the open-model crowd was not the panic you might expect. NVIDIA makes its money selling chips, so more people running more open models is good for its core business, and a lot of people read that incentive as a reason it will keep Hugging Face open. The worry is narrower and more specific: the deal also hands NVIDIA control of llama.cpp and the ggml team, and nobody wants the most popular local-inference engine drifting toward CUDA-only.

What actually got reported

The story broke on r/LocalLLaMA, pointing at a Business Insider report that NVIDIA had been in talks to buy Hugging Face for over $13 billion. An edit to the thread cited The Information reporting the acquisition as agreed at $12.9 billion. Neither company has posted a formal announcement as of this writing, so treat the exact number as a moving target, but two outlets landing on the same deal within a day of each other is not nothing.

Hugging Face is not a model lab. Its value is that it became the registry: the default hub for weights, datasets, Spaces, model cards, and community discovery. Several commenters made the same point, which is that the platform monetizes less through unique infrastructure and more through being the place everyone already goes. That network effect is exactly what an acquirer is paying for, and it is also what makes people nervous about who owns it.

The part that actually worries people: llama.cpp

The bigger thread for local-inference users was not the hub itself. It was the follow-up realization that buying Hugging Face means buying substantial influence over llama.cpp and ggml. Hugging Face hired Georgi Gerganov and the core ggml team back in February 2026 to keep the projects going, which Hugging Face itself announced at the time and Gerganov discussed on GitHub. So the maintainers of the most widely used local-inference engine now report, indirectly, to a GPU company.

The concern is governance, not the code. The existing releases are open source and can be forked the moment anything goes wrong. What people flagged is subtler: future direction could tilt toward CUDA, and support for non-NVIDIA backends like ROCm and Vulkan could get deprioritized. llama.cpp's whole appeal is that it runs on almost anything, from a Mac to an AMD card to a laptop iGPU. Bend it toward NVIDIA-first optimization and you have technically-still-open software that quietly gets worse everywhere except NVIDIA hardware. The community answer, stated plainly in the thread, is that if stewardship turns hostile to portability, people fork and move on. That keeps the code alive, but a fork also splits effort between a CUDA-focused stack and a vendor-neutral one.

Why the reaction was calmer than you would think

Ask people to rank who they would rather see own Hugging Face and NVIDIA came out ahead of OpenAI, Anthropic, Microsoft, and Google. The reasoning is about incentives. Those other companies build models and would arguably benefit from a weaker open ecosystem competing with their paid APIs. NVIDIA sells the shovels. Every open model someone downloads and runs locally is more demand for the exact hardware NVIDIA makes, whichever model family ends up winning. Under that logic, NVIDIA has a reason to keep the hub broadly open and model-agnostic rather than to lock it down.

The counter-worry is about the models that live at the edges of acceptable use. People raised the risk that abliterated, uncensored, or otherwise policy-sensitive checkpoints could face pressure after a big-company acquisition, and floated mirroring important repositories to torrents or alternate hosts before anything changes. Which is how a story about a $12.9 billion deal turned into a thread about backup strategies.

Video: what the acquisition means for open models

Context on why a GPU maker buying the model hub matters for anyone who runs open weights.

The mirror-everything reflex

The clearest signal of how the community actually feels was a separate, older post that resurfaced: a reminder that you can legally torrent AI model weights when their licenses allow it. Torrenting is a transport, not piracy, and the thread framed it as a decentralized fallback if the central hub ever changes policy. People named ModelScope, Kaggle Models, and Civitai as alternate homes, and one commenter made the important technical point that torrent-distributed models should ship SHA-256 hashes so you can verify a download rather than trust a random peer. None of that is triggered by anything NVIDIA has done. It is a reflex about depending on a single registry, and the acquisition just made the dependency feel heavier.

What it means if you use open models

For most people nothing changes this week. You will still pull weights from Hugging Face and still run them through llama.cpp or a hosted provider. The thing to watch over the next few quarters is whether non-NVIDIA backends keep getting the same care in llama.cpp, and whether the hub stays as permissive about which models are allowed to live there. If either slips, the open-model world has options, from forks to mirrors to competing hubs, but options are not the same as the easy default people rely on now. If you route your work across several models anyway, this is another argument for not tying your whole workflow to one place, whether that place is a single hub or a single subscription.

FAQ

Is the NVIDIA and Hugging Face deal confirmed?

Not officially. Business Insider reported talks above $13 billion and The Information reported an agreed price of $12.9 billion, but neither company had posted a formal announcement when this was written. Treat the number as reported, not final.

Does this mean llama.cpp will become NVIDIA-only?

No one has said that. The realistic concern is prioritization: that CUDA gets the attention while ROCm and Vulkan support lags. The code stays open and forkable, so a portable version can continue even if the main project's focus narrows.

Should I back up models I depend on?

It is reasonable to mirror weights you rely on, especially niche or uncensored checkpoints, and to keep SHA-256 hashes to verify them. This is good practice regardless of the acquisition, since depending on one registry is a single point of failure. See our guide to open-source AI models for where the ecosystem stands.

Sources

• r/LocalLLaMA - NVIDIA in talks to acquire Hugging Face
• Business Insider - NVIDIA in talks to buy Hugging Face for $13 billion
• The Information - NVIDIA agrees to buy Hugging Face for $12.9 billion
• r/LocalLLaMA - NVIDIA is also acquiring llama.cpp and its team
• Hugging Face - the ggml team joins Hugging Face
• Georgi Gerganov - llama.cpp discussion on the ggml move
• r/LocalLLaMA - you can legally torrent AI models

Further reading

• Open-source AI models in 2026: what to actually run
• Cutting AI costs by routing to open models
• NVIDIA's grip on inference and who is trying to break it
• The best app for running multiple AI models in one place