Local AIの未来が加速！Ggml.aiがHugging Faceにジョイン

Local AIの開発と最適化をリードするGgml.aiが、ついにHugging Faceのチームに加わることになりました。この提携により、llama.cppをはじめとするローカル環境でのAI実行エコシステムがさらに強化され、長期的な技術革新が約束されます。エンジニアにとっても、ローカルLLMの活用がこれまで以上に身近で強力なものになりそうです！

Huggingface is the silent GOAT of the AI space, such a great community and platform

I'm regularly amazed that HuggingFace is able to make money. It does so much good for the world.

How solid is its business model? Is it long-term viable? Will they ever "sell out"?

Seems like a great fit - kinda surprised it didn’t happen sooner. I think we are deep in the valley of local AI, but I’d be willing to bet it breaks out in the next 2-3 years. Here’s hoping!

I consider HuggingFace more "Open AI" than OpenAI - one of the few quiet heroes (along with Chinese OSS) helping bring on-premise AI to the masses.

I'm old enough to remember when traffic was expensive, so I've no idea how they've managed to offer free hosting for so many models. Hopefully it's backed by a sustainable business model, as the ecosystem would be meaningfully worse without them.

We still need good value hardware to run Kimi/GLM in-house, but at least we've got the weights and distribution sorted.

Can anyone point me in the direction of getting a model to run locally and efficiently inside something like a Docker container on a system with not so strong computing power (aka a Macbook M1 with 8gb of memory)?

Is my only option to invest in a system with more computing power? These local models look great, especially something like https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_O... (https://huggingface.co/AlicanKiraz0/Cybersecurity-BaronLLM_Offensive_Security_LLM_Q6_K_GGUF) for assisting in penetration testing.

I've experimented with a variety of configurations on my local system, but in the end it turns into a make shift heater.

This is great news. I've been sponsoring ggml/llama.cpp/Georgi since 2023 via Github. Glad to see this outcome. I hope you don't mind Georgi but I'm going to cancel my sponsorship now you and the code have found a home!

The community will continue to operate fully autonomously and make technical and architectural decisions as usual. Hugging Face is providing the project with long-term sustainable resources, improving the chances of the project to grow and thrive. The project will continue to be 100% open-source and community driven as it is now.

I want this to be true, but business interests win out in the end. Llama.cpp is now the de-facto standard for local inference; more and more projects depend on it. If a company controls it, that means that company controls the local LLM ecosystem. And yeah, Hugging Face seems nice now... so did Google originally. If we all don't want to be locked in, we either need a llama.cpp competitor (with a universal abstration), or it should be controlled by an independent nonprofit.

It's hard to overstate the impact Georgi Gerganov and llama.cpp have had on the local model space. He pretty much kicked off the revolution in March 2023, making LLaMA work on consumer laptops.

Here's that README from March 10th 2023 https://github.com/ggml-org/llama.cpp/blob/775328064e69db1eb... (https://github.com/ggml-org/llama.cpp/blob/775328064e69db1ebd7e19ccb59d2a7fa6142470/README.md)

The main goal is to run the model using 4-bit quantization on a MacBook. [...] This was hacked in an evening - I have no idea if it works correctly.

Hugging Face have been a great open source steward of Transformers, I'm optimistic the same will be true for GGML.

I wrote a bit about this here: https://simonwillison.net/2026/Feb/20/ggmlai-joins-hugging-f... (https://simonwillison.net/2026/Feb/20/ggmlai-joins-hugging-face/)

I don’t know if this warrants a separate thread here but I have to ask…

How can I realistically get involved the AI development space? I feel left out with what’s going on and living in a bubble where AI is forced into by my employer to make use of it (GitHub Copilot), what is a realistic road map to kinda slowly get into AI development, whatever that means

My background is full stack development in Java and React, albeit development is slow.

I’ve only messed with AI on very application side, created a local chat bot for demo purposes to understand what RAG is about to running models locally. But all of this is very superficial and I feel I’m not in the deep with what AI is about. I get I’m too ‘late’ to be on the side of building the next frontier model and makes no sense, what else can I do?

I know Python, next step is maybe do ‘LLM from scratch”? Or I pick up Google machine learning crash course certificate? Or do recently released Nvidia Certification?

I’m open for suggestions

I am happy for ggml team. They did so much work for quantization and actually made it available to everyone. Thank you.