Nvidia's $12.9 billion bid for Hugging Face would also buy the llama.cpp team

Update — confirmed, September 3, 2026. Nvidia confirmed the deal on September 3 at $12.93 billion. Jensen Huang says Hugging Face stays an open platform and that Nvidia compute won’t be required to build or deploy on it. Clem Delangue says he approached Huang over the summer. Regulatory approval is pending and the deal isn’t expected to close until the first half of 2027, so the llama.cpp backend question stays open. NVIDIA to Acquire Hugging Face, Nvidia confirms it will buy Hugging Face for $12.9 billion, Nvidia buys Hugging Face for $12.9B, promises not to squeeze too hard

Nvidia has reportedly agreed to buy Hugging Face for $12.9 billion. The Information published the figure on August 26. No agreement has been signed, and neither company has said anything since. If it closes, the company that sells the GPUs would own the hub where three million models live.

The price is the smaller story for anyone who downloads weights. Hugging Face hired the llama.cpp maintainers in February 2026. That project is the reason a quantized model runs on an AMD card, an Apple laptop, or a GPU from 2019, and those maintainers now work for a company Nvidia wants to buy. Nvidia’s silence is doing some work here too, because TechCrunch notes the company usually corrects inaccurate reports fast.

What $12.9 billion actually buys

Hugging Face hosts 3,034,223 models as of Tuesday, by the count on its own listing page. That inventory of open weights, plus the datasets and Spaces around it, is the asset. The business attached to it is modest: roughly $150 million in annual revenue, up from about $100 million two months earlier, and close to profitability after ten years, per TechCrunch.

The count understates the position. Hugging Face is the default in code, not only in habit. A from_pretrained call in transformers resolves to the hub unless you override it, tokenizers and configs are fetched from it at runtime, and most model cards, benchmark harnesses and fine-tuning tutorials assume it exists. Replacing that across an organization is a migration, not a preference. That’s what the price is actually tracking, and it stopped tracking revenue years ago.

So Nvidia isn’t paying for the revenue. TechCrunch lays out four reported motives, and none of them are subtle. Defend GPU dominance while OpenAI, Google, Amazon and Anthropic build their own silicon. Keep the open-model world pointed at CUDA. Get back into cloud after DGX Cloud was scaled back about a year ago. Resell compute Nvidia has already committed to buying.

Put the cheque next to what Nvidia has spent before, and the size registers properly.

Mellanox cost $6.9 billion when Nvidia announced it in March 2019. This is nearly double that, for a company with a rounding error of Mellanox’s revenue.

The llama.cpp problem

On February 20, 2026, Georgi Gerganov and the ggml team joined Hugging Face. The announcement made two promises. “The project will continue to be 100% open-source and community driven as it is now,” it said, and Gerganov and team “still dedicate 100% of their time maintaining llama.cpp and have full autonomy and leadership on the technical directions and the community.”

Hugging Face made those promises. An acquisition transfers who has to keep them.

The technical position is better than the panic suggests. llama.cpp is MIT licensed, with 126,000 stars and 22,000 forks, so nothing already published can be pulled back. Every line shipped to date stays free, and a fork needs no one’s permission. What an owner controls is direction: who gets paid to review pull requests, which backends get continuous-integration time, and which hardware stays a first-class target. llama.cpp today supports CUDA, ROCm, Vulkan and Metal. That breadth is exactly what makes it valuable to people who don’t buy Nvidia hardware.

Developers did the arithmetic within hours. A thread on r/LocalLLaMA drew 1,375 points and 427 comments, and its top reply, at 1,072 points, was short: “If it happens, we shall fork and move on. It is the way of things.” The worry running underneath the jokes went past licensing. Commenters kept coming back to ROCm, Vulkan and Metal support quietly losing attention, and to older CUDA versions getting dropped.

That’s the realistic failure mode. Not a license change anyone could point at, but a backlog that stops moving.

The fork talk is cheaper than it sounds, and also harder. Cloning 22,000 existing forks costs nothing, so the code survives any owner. Sustaining it is the expensive part: llama.cpp absorbs new model architectures within days of release, and that pace comes from a small group of full-time maintainers who would have to be paid by someone else. AMD has the clearest incentive to fund that work. It has not said it will.

Nvidia already tried this once

Late in 2025, Hugging Face turned down a $500 million Nvidia investment that valued it at $7 billion, according to TechCrunch. The reported reason was avoiding one dominant investor. Clem Delangue, Hugging Face’s CEO, described the obligation plainly to TechCrunch in August: “We’re building a platform for the community, and they’re trusting us with sharing their data and their models on the platform, so we have a long-term responsibility to them.”

Months later the number is $12.9 billion, and the reported answer is yes.

The sequence is worth keeping straight, because it decides how firm any of this is. Business Insider reported the talks first, over the weekend before August 24, putting the valuation at $13 billion or more and describing Hugging Face as having been approached rather than shopping itself. The Information followed on August 26 with a concluded agreement and the $12.9 billion figure. Two days after that, Fortune checked and still found no signed contract. Three reports, one direction of travel, zero confirmations.

Delangue and Jensen Huang are not opponents in this. After the breach that hit Hugging Face in July, Delangue told CBS the company defended itself using an Nvidia-modified version of a Chinese open source model, and he has said China is “clearly dominating” open source AI, both reported by TechCrunch. The two were already working closely. That’s part of why the community reaction reads as uneasy rather than shocked.

Merger review is the real gate. Nvidia and SoftBank terminated a $40 billion agreement for ARM in February 2022, citing significant regulatory challenges, after the FTC sued to block the deal. A model hub is a far smaller asset than ARM. The structural argument, that one company would hold both the accelerators and the distribution layer for the software that runs on them, is not smaller at all. It pairs with Nvidia’s broader push to own every chip in the AI data center.

What this means for you

Nothing changes this week. No agreement is signed, Fortune could not independently verify the report, and the talks may still fall apart.

Two things are worth doing anyway, and both are cheap. Mirror the weights you actually depend on, because a hub is a single point of failure no matter who owns it, and Chinese open-weight releases already ship to multiple hosts for exactly this reason. Then, if your inference path runs on ROCm, Vulkan or Metal, track llama.cpp’s commit and merge activity on those backends over the next two quarters instead of waiting for an announcement. Backend neglect is visible in CI configuration and review latency long before it becomes policy.

The tell to watch is Nvidia itself. A company that usually rebuts bad reporting inside a day has now let a $12.9 billion number stand for most of a week.