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.
data-ottawa 1 hours ago [-]
Can we toss in the work unsloth does too as an unsung hero?
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.
disiplus 54 minutes ago [-]
Yeah, they're the good guys. I suspect the open source work is mostly advertisements for them to sell consulting and services to enterprises. Otherwise, the work they do doesn't make sense to offer for free.
cubie 1 hours ago [-]
I'm a big fan of their work as well, good shout.
Tepix 11 minutes ago [-]
It's insane how much traffic HF must be pushing out of the door. I routinely download models that are hundreds of gigabytes in size from them. A fantastic service to the sovererign AI community.
zozbot234 2 hours ago [-]
> We still need good value hardware to run Kimi/GLM in-house
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.
HPsquared 1 hours ago [-]
At a certain point the energy starts to cost more than renting some GPUs.
sowbug 48 minutes ago [-]
Why doesn't HF support BitTorrent? I know about hf-torrent and hf_transfer, but those aren't nearly as accessible as a link in the web UI.
embedding-shape 13 minutes ago [-]
> Why doesn't HF support BitTorrent?
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.
sowbug 2 minutes ago [-]
I thought of the tracking and gate questions, too, when I vibed up an HF torrent service a few nights ago. (Super annoying BTW to have to download the files just to hash the parts, especially when webseeds exist.) Model owners could disable or gate torrents the same way they gate the models, and HF could still measure traffic by .torrent downloads and magnet clicks.
It's a bit like any legalization question -- the black market exists anyway, so a regulatory framework could bring at least some of it into the sunlight.
Fin_Code 12 minutes ago [-]
I still don't know why they are not running on torrent. Its the perfect use case.
freedomben 10 minutes ago [-]
That would shut out most people working for big corp, which is probably a huge percentage of the user base. It's dumb, but that's just the way corp IT is (no torrenting allowed).
zozbot234 6 minutes ago [-]
It's a sensible option, even when not everyone can really use it. Linux distros are routinely transfered via torrent, so why not other massive, open-licensed data?
heliumtera 10 minutes ago [-]
How can you be the man in the middle in a truly P2P environment?
jgrahamc 21 minutes ago [-]
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!
HanClinto 2 hours ago [-]
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"?
microsoftedging 17 minutes ago [-]
FT had a solid piece a few weeks back: "Why AI start-up Hugging Face turned down a $500mn Nvidia deal"
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
zozbot234 2 minutes ago [-]
These are all big hardware firms, which makes a lot of sense as a classic 'commoditize the complement' play. Not exactly pro-consumer, but not quite anti-consumer either!
dmezzetti 2 hours ago [-]
They have paid hosting - https://huggingface.co/enterprise and paid accounts. Also consulting services. Seems like a pretty good foundation to me.
julien_c 2 minutes ago [-]
and a lot of traction on paid (private in particular) storage these days; sneak peek at new landing page: https://huggingface.co/storage
I_am_tiberius 2 hours ago [-]
I once tried hugging face because I wanted I worked through some tutorial. They wanted my credit card details during the registration as far as I remember. After a month they invoiced me some amount of money and I had no idea what it was. To be honest, I don't understand what exactly they do and what services I was paying for, but I cancelled my account and never touched it again. For me that was a totally intransparent process.
Huggingface is the silent GOAT of the AI space, such a great community and platform
lairv 2 hours ago [-]
Truly amazing that they've managed to build an open and profitable platform without shady practices
al_borland 2 hours ago [-]
It’s such a sad state of affairs when shady practices are so normal that finding a company without them is noteworthy.
beoberha 2 hours ago [-]
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!
tkp-415 1 hours ago [-]
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)?
There’s no way around needing a powerful-enough system to run the model. So you either choose a model that can fit on what you have —i.e. via a small model, or a quantised slightly larger model— or you access more powerful hardware, either by buying it or renting it.
(IME you don’t need Docker. For an easy start just install LM Studio and have a play.)
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.
ryandrake 33 minutes ago [-]
I wasn't able to have very satisfying success until I bit the bullet and threw a GPU at the problem. Found an actually reasonably priced A4000 Ada generation 20GB GPU on eBay and never looked back. I still can't run the insanely large models, but 20GB should hold me over for a while, and I didn't have to upgrade my 10 year old Ivy Bridge vintage homelab.
sigbottle 46 minutes ago [-]
Are mac kernels optimized compared to CUDA kernels? I know that the unified GPU approach is inherently slower, but I thought a ton of optimizations were at the kernel level too (CUDA itself is a moat)
zozbot234 1 hours ago [-]
The general rule of thumb is that you should feel free to quantize even as low as 2 bits average if this helps you run a model with more active parameters. Quantized models are not perfect at all, but they're preferable to the models with fewer, bigger parameters. With 8GB usable, you could run models with up to 32B active at heavy quantization.
Everytime I ask the same thing here, people point me there.
dhruv3006 1 hours ago [-]
Huggingface is actually something thats driving good in the world.
Good to see this collab/
androiddrew 1 hours ago [-]
One of the few acquisitions I do support
the__alchemist 2 hours ago [-]
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?
stephantul 26 minutes ago [-]
Georgi is such a legend. Glad to see this happening
superkuh 19 minutes ago [-]
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.
jimmydoe 2 hours ago [-]
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.
segmondy 59 minutes ago [-]
Great news! I have always worried about ggml and long term prospect for them and wished for them to be rewarded for their effort.
dmezzetti 2 hours ago [-]
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!
logicallee 1 hours ago [-]
>I've been one of the strongest supporters of local AI, dedicating thousands of hours towards building a framework to enable it.
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.
> We have a local model we would like to distribute but don't have a good CDN.
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).
echoangle 6 minutes ago [-]
Maybe stupid question but why not just put it in a torrent?
option 1 hours ago [-]
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!
dragonwriter 29 minutes ago [-]
> Isn't HF banned in China?
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.
disiplus 52 minutes ago [-]
I think in the West we think everything is blocked. But for example, if you book an eSIM, when you visit you already get direct access to Western services because they route it to some other server. Hong Kong is totally different: they basically use WhatsApp and Google Maps, and everything worked when I was there.
embedding-shape 11 minutes ago [-]
But also yes, parent is right, HF is more or less inaccessible, and Modelscope frequently cited as the mirror to use (although many Chinese labs seems to treat HF as the mirror, and Modelscope as the "real" origin).
woadwarrior01 1 hours ago [-]
HF is indeed banned in China. The Chinese equivalent of HF is ModelScope[1].
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.
r_lee 2 hours ago [-]
I think that's because there's less local AI usage now since there's all kinds of image models by the big labs, so there's really no rush of people self hosting stable diffusion etc anymore
the space moved from Consumer to Enterprise pretty fast due to models getting bigger
zozbot234 2 hours ago [-]
Today's free models are not really bigger when you account for the use of MoE (with ever increasing sparsity, meaning a smaller fraction of active parameters), and better ways of managing KV caching. You can do useful things with very little RAM/VRAM, it just gets slower and slower the more you try to squeeze it where it doesn't quite belong. But that's not a problem if you're willing to wait for every answer.
segmondy 56 minutes ago [-]
part of what discussion? anyone in the AI space knows and uses HF, but the public doesn't give a care and why should they? It's just an advanced site were nerds download AI stuff. HF is super valuable with their transformers library, their code, tutorials, smol-models, etc, but how does it translate to investor dollars?
LatencyKills 2 hours ago [-]
It isn't necessary to be part of the discussion if you are truly adding value (which HF continues to do). It's nice to see a company doing what it does best without constantly driving the hype train.
rvz 2 hours ago [-]
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.
2 hours ago [-]
andsoitis 51 minutes ago [-]
I believe ggml.ai was funded by angel investors, not VC.
Filip_portive 2 hours ago [-]
[flagged]
Rendered at 16:14:53 GMT+0000 (Coordinated Universal Time) with Vercel.
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.
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.
It's a bit like any legalization question -- the black market exists anyway, so a regulatory framework could bring at least some of it into the sunlight.
How solid is its business model? Is it long-term viable? Will they ever "sell out"?
https://giftarticle.ft.com/giftarticle/actions/redeem/9b4eca...
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
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.
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:
https://localclaw.io/
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.
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.
Hopefully this does not mean consolidation due to resource dry up but true fusion of the bests.
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
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).
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.
[1]: https://modelscope.cn/
the space moved from Consumer to Enterprise pretty fast due to models getting bigger
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.