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If you’ve been anywhere near the local AI scene, you already know that [llama.cpp](https://github.com/ggml-org/llama.cpp) is basically the backbone of running models on your own hardware. So when Georgi Gerganov — the person who started it all — [announced](https://huggingface.co/blog/ggml-joins-hf) that the entire GGML team is officially joining Hugging Face, it felt less like a surprise and more like the inevitable conclusion everyone was waiting for.

The news dropped on February 20th and immediately blew up. The [Hacker News thread](https://news.ycombinator.com/item?id=47088037) racked up hundreds of points within hours, and the [GitHub discussion](https://github.com/ggml-org/llama.cpp/discussions/19759) was flooded with community reactions. People are genuinely excited, and I think for good reason.

Here’s what makes this interesting. Hugging Face’s `transformers` library is where most open models are defined and trained. llama.cpp is where those models actually run on regular consumer devices — laptops, desktops, phones. Until now, getting a model from one world to the other involved a bunch of manual steps, format conversions, and occasional headaches. The whole point of this merger is to make that pipeline almost single-click. Train or fine-tune in transformers, deploy locally via llama.cpp, done.

What I appreciate most is what’s *not* changing. Georgi and the core team keep full autonomy over technical decisions. The project stays 100% open-source and community-driven. Hugging Face is essentially providing long-term resources and infrastructure so the team can focus on what they do best — making local inference faster and more accessible. No corporate takeover vibes here.

The bigger picture they’re painting is what they call the “ultimate inference stack” — the idea that open-source models should run efficiently on the devices people already own. Not everyone wants to pay for cloud API calls, and not everyone should have to. With these two projects working under the same roof, the friction between model creation and local deployment should shrink dramatically.

If you care about running AI locally, this is the most significant structural shift in a while. Keep an eye on the [llama.cpp repo](https://github.com/ggml-org/llama.cpp) — things are about to move fast.