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

> 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...

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.

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

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.

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... 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.

That's interesting. I thought they would be somewhat redundant. They do similar things after all, except training.

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

It seems to me there is no chance local ML is going to be anywhere out of the toy status comparing to closed source ones in short term

Hopefully this does not mean consolidation due to resource dry up but true fusion of the bests.

Ollama and webui seem to rapidly lose their charm. Ollama now includes cloud apis which makes no sense as a local.

I am somewhat anxious about "integration with the Hugging Face transformers library" and possible python ecosystem entanglements that might cause. I know llama.cpp and ggml already have plenty of python tooling but it's not strictly required unless you're quantizing models yourself or other such things.

In either case - huge thanks to them for keeping AI open!

Both $0 revenue "companies", but have created software that is essential to the wider ecosystem and has mindshare value; Bun for Javascript and Ggml for AI models.

But of course the VCs needed an exit sooner or later. That was inevitable.