This acquisition is almost the same as the acquisition of Bun by Anthropic.
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.
As someone who's been in the "AI" space for a while its strange how Hugging Face went from one of the biggest name to not a part of the discussion at all.
Their business model is essentially the same as GitHub. Host lots of stuff for free and build a community around it, sell the upscaled/private version to businesses. They are already profitable.
This is really great news. I've been one of the strongest supporters of local AI dedicating thousands of hours towards building a framework to enable it. I'm looking forward to seeing what comes of it!
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.
Does anyone have a good comparison of HuggingFace/Candle to Burn? I am testing them concurrently, and Burn seems to have an easier-to-use API. (And can use Candle as a backend, which is confusing) When I ask on Reddit or Discord channels, people overwhelmingly recommend Burn, but provide no concrete reasons beyond "Candle is more for inference while Burn is training and inference". This doesn't track, as I've done training on Candle. So, if you've used both: Thoughts?
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)?
8GB is not enough to do complex reasoning, but you could do very small simple things. Models like Whisper, SmolVLM, Quen2.5-0.5B, Phi-3-mini, Granite-4.0-micro, Mistral-7B, Gemma3, Llama-3.2 all work on very little memory. Tiny models can do a lot if you tune/train them. They also need to be used differently: system prompt preloaded with information, few-shot examples, reasoning guidance, single-task purpose, strict output guidelines. See https://github.com/acon96/home-llm for an example. For each small model, check if Unsloth has a tuned version of it; it reduces your memory footprint and makes inference faster.
For your Mac, you can use Ollama, or MLX (Mac ARM specific, requires different engine and different model disk format, but is faster). Ramalama may help fix bugs or ease the process w/MLX. Use either Docker Desktop or Colima for the VM + Docker.
For today's coding & reasoning models, you need a minimum of 32GB VRAM combined (graphics + system), the more in GPU the better. Copying memory between CPU and GPU is too slow so the model needs to "live" in GPU space. If it can't fit all in GPU space, your CPU has to work hard, and you get a space heater. That Mac M1 will do 5-10 tokens/s with 8GB (and CPU on full blast), or 50 token/s with 32GB RAM (CPU idling). And now you know why there's a RAM shortage.
With only 8 GB of memory, you're going to be running a really small quant, and it's going to be slow and lower quality. But yes, it should be doable. In the worst case, find a tiny gguf and run it on CPU with llamafile.
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!
I'm glad the llama.cpp and the ggml backing are getting consistent reliable economic support. I'm glad that ggerganov is getting rewarded for making such excellent tools.
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.
Honestly I’m shocked to be the only one I see of this opinion:
HuggingFace’s `accelerate`, `transformers` and `datasets` have been some of the worst open source Python libraries I have ever used that I had to use.
They break backwards compatibility constantly, even on APIs which are not underscore/dunder named even on minor version releases without even documenting this, they refuse PRs fixing their lack of `overloads` type annotations which breaks type checking on their libraries and they just generally seem to have spaghetti code. I am not excited that another team is joining them and consolidating more engineering might in the hands of these people
> 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.
Llama.cpp is an open source project that anyone can fork as needed, so any "control" over it really only extends to facilitating development of certain features.
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.
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?
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[ 3.0 ms ] story [ 62.6 ms ] threadBoth $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.
Hopefully this does not mean consolidation due to resource dry up but true fusion of the bests.
How solid is its business model? Is it long-term viable? Will they ever "sell out"?
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.
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.
For your Mac, you can use Ollama, or MLX (Mac ARM specific, requires different engine and different model disk format, but is faster). Ramalama may help fix bugs or ease the process w/MLX. Use either Docker Desktop or Colima for the VM + Docker.
For today's coding & reasoning models, you need a minimum of 32GB VRAM combined (graphics + system), the more in GPU the better. Copying memory between CPU and GPU is too slow so the model needs to "live" in GPU space. If it can't fit all in GPU space, your CPU has to work hard, and you get a space heater. That Mac M1 will do 5-10 tokens/s with 8GB (and CPU on full blast), or 50 token/s with 32GB RAM (CPU idling). And now you know why there's a RAM shortage.
Is hopelessly dated. There are much better newer models around.
In either case - huge thanks to them for keeping AI open!
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.
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.
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...
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