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jimmydoe 2026-02-20 14:08 UTC link

Amazing. I like the openness of both project and really excited for them.

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

mnewme 2026-02-20 14:08 UTC link

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

HanClinto 2026-02-20 14:14 UTC link

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"?

dmezzetti 2026-02-20 14:25 UTC link

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!

beoberha 2026-02-20 14:32 UTC link

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!

mythz 2026-02-20 14:32 UTC link

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.

the__alchemist 2026-02-20 14:36 UTC link

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?

androiddrew 2026-02-20 14:54 UTC link

One of the few acquisitions I do support

tkp-415 2026-02-20 14:54 UTC link

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... 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.

option 2026-02-20 15:00 UTC link

Isn't HF banned in China? Also, how are many Chinese labs on Twitter all the time?

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

jgrahamc 2026-02-20 15:54 UTC link

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!

superkuh 2026-02-20 15:56 UTC link

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.

ukblewis 2026-02-20 16:55 UTC link

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

0xbadcafebee 2026-02-20 16:58 UTC link

> 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.

simonw 2026-02-20 17:22 UTC link

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...

> 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...

mattfrommars 2026-02-20 19:08 UTC link

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

ontouchstart 2026-02-21 01:19 UTC link

I have played with both mlx-lm and llama.cpp after I bought a 24GB M5 MacBook Pro last year.

Then I fell down the rabbit holes of uv, rust and C++ and forgot about LLMs. Today after I saw this announcement and answered someone’s question about how to set it up, when I got home, I decided play with llama.cpp again.

I was surprised and impressed:

https://ontouchstart.github.io/rabbit-holes/llama.cpp/

I am not going to use mlx-lm or lmstudio anymore. llama.cpp is so much fun.

car 2026-02-21 05:39 UTC link

So great to see my two favorite Open Source AI projects/companies joining forces.

Since I don't see it mentioned here, LlamaBarn is an awesome little—but mighty—MacOS menubar program, making access to llama.cpp's great web UI and downloading of tastefully curated models easy as pie. It automatically determines the available model- and context-sizes based on available RAM.

https://github.com/ggml-org/LlamaBarn

Downloaded models live in:

  ~/.llamabarn

Apart from running on localhost, the server address and port can be set via CLI:

  # bind to all interfaces (0.0.0.0)
  defaults write app.llamabarn.LlamaBarn exposeToNetwork -bool YES

  # or bind to a specific IP (e.g., for Tailscale)
  defaults write app.llamabarn.LlamaBarn exposeToNetwork -string "100.x.x.x"

  # disable (default)
  defaults delete app.llamabarn.LlamaBarn exposeToNetwork

am17an 2026-02-21 11:05 UTC link

One often overlooked after that is ggml, the tensor library that runs llama.cpp is not based on pytorch, rather just plain cpp. In a world where pytorch dominates, it shows that alternatives are possible and are worthy to be pursued.

mhher 2026-02-21 11:48 UTC link

It's great to see the ggml team getting proper backing. Keeping inference in bare-metal C/C++ without the Python bloat is the only way local AI is going to scale efficiently. Well deserved for Georgi, Johannes, Piotr, and the rest of the team.