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.
They provide excellent documentation and they’re often very quick to get high quality quants up in major formats. They’re a very trustworthy brand.
If you stream weights in from SSD storage and freely use swap to extend your KV cache it will be really slow (multiple seconds per token!) but run on basically anything. And that's still really good for stuff that can be computed overnight, perhaps even by batching many requests simultaneously. It gets progressively better as you add more compute, of course.
This is fun for proving that it can be done, but that's 100X slower than hosted models and 1000X slower than GPT-Codex-Spark.
That's like going from real time conversation to e-mailing someone who only checks their inbox twice a day if you're lucky.
Harder to track downloads then. Only when clients hit the tracker would they be able to get download states, and forget about private repositories or the "gated" ones that Meta/Facebook does for their "open" models.
Still, if vanity metrics wasn't so important, it'd be a great option. I've even thought of creating my own torrent mirror of HF to provide as a public service, as eventually access to models will be restricted, and it would be nice to be prepared for that moment a bit better.
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 generally try to include something in a comment that's not information already under discussion - in this case that was the link and quote from the original README.
And for those who think it's just organic with all of the upvotes, HN absolutely does have a +/- comment bias for users, and it does automatically feature certain people and suppress others.
However these things are dynamic and change over time. As I read the discussion just now, the GP comment was the ~5th top-level comment.
How solid is its business model? Is it long-term viable? Will they ever "sell out"?
https://giftarticle.ft.com/giftarticle/actions/redeem/9b4eca...
GitHub is great -- huge fan. To some degree they "sold out" to Microsoft and things could have gone more south, but thankfully Microsoft has ruled them with a very kind hand, and overall I'm extremely happy with the way they've handled it.
I guess I always retain a bit of skepticism with such things, and the long-term viability and goodness of such things never feels totally sure.
Oh no, never. Don't worry, the usual investors are very well known for fighting for user autonomy (AMD, Nvidia, Intel,IBM, Qualcomm)
They are all very pro consumers and all backers are certainly here for your enjoyment only
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
# 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 exposeToNetworkAs for models, plenty of GGUF quantized (down to 2-bit) available on HF and modelscope.
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.
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.
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.
I picked up a second-hand 64GB M1 Max MacBook Pro a while back for not too much money for such experimentation. It’s sufficiently fast at running any LLM models that it can fit in memory, but the gap between those models and Claude is considerable. However, this might be a path for you? It can also run all manner of diffusion models, but there the performance suffers (vs. an older discrete GPU) and you’re waiting sometimes many minutes for an edit or an image.
https://www.reddit.com/r/LocalLLM/
Everytime I ask the same thing here, people point me there.
https://www.docker.com/blog/run-llms-locally/
As far as how to find good models to run locally, I found this site recently, and I liked the data it provides:
Sounds like you're very serious about supporting local AI. I have a query for you (and anyone else who feels like donating) about whether you'd be willing to donate some memory/bandwidth resources p2p to hosting an offline model:
We have a local model we would like to distribute but don't have a good CDN.
As a user/supporter question, would you be willing to donate some spare memory/bandwidth in a simple dedicated browser tab you keep open on your desktop that plays silent audio (to not be put in the background and deloaded) and then allocates 100mb -1 gb of RAM and acts as a webrtc peer, serving checksumed models?[1] (Then our server only has to check that you still have it from time to time, by sending you some salt and a part of the file to hash and your tab proves it still has it by doing so). This doesn't require any trust, and the receiving user will also hash it and report if there's a mismatch.
Our server federates the p2p connections, so when someone downloads they do so from a trusted peer (one who has contributed and passed the audits) like you. We considered building a binary for people to run but we consider that people couldn't trust our binaries, or would target our build process somehow, we are paranoid about trust, whereas a web model is inherently untrusted and safer. Why do all this?
The purpose of this would be to host an offline model: we successfully ported a 1 GB model from C++ and Python to WASM and WebGPU (you can see Claude doing so here, we livestreamed some of it[2]), but the model weights at 1 GB are too much for us to host.
Please let us know whether this is something you would contribute a background tab to hosting on your desktop. It wouldn't impact you much and you could set how much memory to dedicate to it, but you would have the good feeling of knowing that you're helping people run a trusted offline model if they want - from their very own browser, no download required. The model we ported is fast enough for anyone to run on their own machines. Let me know if this is something you'd be willing to keep a tab open for.
[1] filesharing over webrtc works like this: https://taonexus.com/p2pfilesharing/ you can try it in 2 browser tabs.
[2] https://www.youtube.com/watch?v=tbAkySCXyp0and and some other videos
What services would you need that Hugging Face doesn't provide?
That is not true. I am serving models off Cloudflare R2. It is 1 petabyte per month in egress use and I basically pay peanuts (~$200 everything included).
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.
I did use candle for wasm based inference for teaching purposes - that was reasonably painless and pretty nice.
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
But if you're adjacent to some leaf use-case for AI, you're likely already as good as anyone else at productizing it.
And that's who is getting hired: people who show they can deliver product-market fit.
Hopefully this does not mean consolidation due to resource dry up but true fusion of the bests.
In either case - huge thanks to them for keeping AI open!
I think, for some definition of “banned”, that’s the case. It doesn’t stop the Chinese labs from having organization accounts on HF and distributing models there. ModelScope is apparently the HF-equivalent for reaching Chinese users.
That's interesting. I thought they would be somewhat redundant. They do similar things after all, except training.
Always rooting for Hugging Face
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
a) to have an idea how much tokens I use and
b) be independent of VC financed token machines and
c) I can use it on a plane/train
I don't do full vibe coding with a dozen agents though. I read all the code it produces and guide it where necessary.
Last not least, at some point the VC funded party will be over and when this happens one better knows how to be highly efficient in AI token use.
Whats the advantage of qwen code cli over opencode ?
the space moved from Consumer to Enterprise pretty fast due to models getting bigger
Ollama and webui seem to rapidly lose their charm. Ollama now includes cloud apis which makes no sense as a local.
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.