[profsummergig]

IMHO, the biggest problem with the future of open weights models is that currently, open weights models are the result of philanthropy by some private org. (e.g. DeepSeek).

The spigot can be turned off at any time.

Until there's some sort of "community owned hardware", open weights models are always at risk of being discontinued.

[NitpickLawyer]

Yeah, but the biggest plus for open models is that they can never be taken away. In other words, whatever capabilities they reach (even if there will never be another model), those stay forever. That can't be said for API-based models where a provider can sunset models whenever they feel like (i.e. gpt5-mini will soon be gone, and replaced by a more expensive 5.4-mini, same for goog, etc).

And there will always be incentivised parties that release models. Nvda for one has every incentive to keep the nemotron line going, as they're directly profiting from people running this. And the models aren't really far from open SotA anyway.

Goog will probably continue to release the small models, since they'll use them for browser stuff anyway, and know that they'll leak. So for them it's a win-win to release the small models and gain some dev market share.

And the chinese labs also have incentives to keep releasing models, and will likely continue to get gov support to do so (yay commercial wars between nations).

[felooboolooomba]

> they can never be taken away

Your right to 3d print whatever you want is about to be taken away (in California).

What software you can run on your computer can already be restricted.

Absolutely everything can be taken away. The simplest way to remove open models is probably to declare them a tool that terrorists could use. Crazy? Yes, the world is totally crazy these days.

[redox99]

That only affects people in California. Whereas Fable being shut down affects people all over the world.

[anticorporate]

There's also, importantly, a distinction between what are told we can no longer use, and what can actually be taken away.

Open source and open hardware can be called illegal by a government, but, if we collectively invest our energy into open alternatives, they can't be taken away in the same sense. I can build a RepRap printer and I can use a local AI model. It's on all of us to make sure that the open alternatives are viable, maybe in the current global political reality now more than ever.

Making something illegal isn't a disincentive for everyone. When they start banning books, some of us start assembling printing presses.

[echoangle]

Believe me, if the government wants to stop you from having access to something like that, they could do it. Just give people some incentive to report you and make really harsh punishments and everyone will be thinking really hard about how bad they want have access.

[anticorporate]

Well, sure. The same could be said of any freedom they want to take away. The responsibility is on us to preserve those freedoms. Free software, open hardware, right to repair, privacy tools, etc. will all be the weapons of the people in the fight against totalitarianism.

[dvngnt_]

They can stop piracy or child predators. what makes you think they can prevent access to running models that require no internet access to run

[bijowo1676]

the government is not God, they cant do much beyond declaring anything bad.

It is on people to realize we have the ultimate power and oppose the overreach of government in all ways we can to keep our freedoms.

Freedom is not free, after all

[vitally3643]

Just like declaring piracy illegal stopped piracy and removed pirated materials from everyone's computers.

Everything cannot, in fact, be taken away. Don't propagandize yourself. Some things, like information, are free. Not even China can prevent all its citizens from accessing Western internet. USGov simply does not have the resources to find and audit every hard drive and USB stick in the country for illegal files. The internet cannot be censored 100% without literally cutting every cable and confiscating every radio.

The software that runs on my computer cannot, in fact, be restricted. It can be declared illegal, but there literally is no mechanism by which it can be enforced other than a government goon standing over my shoulder 24/7.

Some freedoms really cannot be removed without utterly implausible amounts of effort. Arguing otherwise is helping to erode freedom. So stop it.

[Simran-B]

Remote attestation?

[advael]

On PCs, the best you could really do is restrict access to certain websites on certain boxes with TPMs the users can't disable. Remote attestation can lock people out of your stuff, but not out of their own stuff. For that you need control of the device. Of course, most mobile phones aren't easy for the user to have control of, but most PCs still are, so long as you scrub the rootkits (e.g. windows) off 'em

[bijowo1676]

it doesnt even work in the government's own servers to protect their own shit

[jgalt212]

> What software you can run on your computer can already be restricted.

Are laws that are inherently unenforceable even laws?

[Bolwin]

> Yeah, but the biggest plus for open models is that they can never be taken away. In other words, whatever capabilities they reach (even if there will never be another model), those stay forever.

In theory yes, but the average person can't really run the big open models.

This is already happening, try to find a provider that still hosts older, especially less popular or succeeded open models.

For me personally, I've been trying to access Kimi K2-0711. There seems to be only one provider left on openrouter (NovitaAI) and 3/4 requests error out

[veqq]

> NovitaAI is a low cost provider who's strategy seems to be to host as many models as possible for the lowest cost possible so that OpenRouter's routing algorithm will default to them as often as possible. The problem is that they clearly don't spend much time on actually testing and configuring all of the models they provide. There's a reason they are very often the first provider to host a new model. I also suspect that they run models at lower quants than they claim but that is not something I can prove. https://www.reddit.com/r/LocalLLaMA/comments/1mk4kt0/be_care...

[jfim]

True, but the capabilities and knowledge of that model are also frozen in time, so the value of that model declines over time.

A model that writes code without knowledge of any language or library changes for half a decade is less useful. A 2021 era chatgpt would be quite quaint in 2026.

Right now the Chinese labs might have incentives to release their models for free, and maybe Google is happy to release open weights today, but I'm sure there are already bean counters at Google salivating at the idea of having Gemini in Chrome as part of a Google AI monthly subscription just like YouTube premium and other Google subscriptions.

[api]

Fine tuning and updating is far cheaper than training from scratch.

[UncleOxidant]

> Nvda for one has every incentive to keep the nemotron line going

They're releases so far have been kind of lackluster compared to Qwen and other Chinese models. My suspicion is that Nvidia won't be releasing models that appear to compete with frontier models because that would upset their big customers.

[fridder]

We need a SETI@Home but for model training

[Azantys]

I think model training is pretty hard to do efficiently on a vastly distributed network. If the model cant fit into the VRAM of the node your performance becomes so bad its useless, so a distributed model could only be properly trained if the size of the model doesnt exceed the majority of the nodes VRAM sizes. Maybe there is a different way of doing training but this would be the only way I can see. And it would still be much worse than just using a big datacenter where everything is fully interconnected. BOINC projects work great because its usually just a lot of small compute and memory required so every old desktop and laptop can contribute. Training a model which can compete and is not tiny requires neither low compute or low memory amount. BOINC tasks take minutes usually or sometimes hours but not weeks or months like training a model from scratch. But something like 7B or lower could maybe be trained like this. Im not sure but I think someone is already working on something like this but I dont remember the name of the project.

[wuschel]

My understanding is that in addition to your comment and the development of a method to separate the training data for distributed learning, the latency/bandwidth of systems connected on the internet is a challenge, too. Information has to be sent around before and after any hypothetical number crunching.

[g023]

Slap the gpus in a car and offset the cost of ownership by supplying the grid for GPU power on the go. Either get paid in rebates or tokens. Contribute to a distributed training/inferencing network.

[0x3f]

Consumer hardware over the internet is not really suitable for this, AFAIK.

[baby_souffle]

There's some really early days work on making training loops robust to failure but they all have trade-offs right now.

I remain hopeful that we'll be able to democratize the entire tech stack for this tech.

[calebkaiser]

This has been a (noble) goal of lots of different projects in the community for a long time. Federated learning projects like Flower have been chipping away at it for a long time. There are many many hurdles to be cleared before anything in this area is super feasible as an alternative, but I applaud everyone who works on it.

[ainka-ainka]

Here's a project trying that - https://nousresearch.com/nous-psyche

[kamranjon]

Have been thinking about this a lot lately.

[throwawayffffas]

I don't think that's the case, it's not philanthropy, they are getting something out of it. The labs are learning from one another from the shared models.

Plus I am certain it makes financial sense. I am guessing here but fully utilizing a subscriptions limits probably costs the operator more money than the subscription revenue, that is why anthropic is making such a big stink about the chinese data harvesting. By releasing the weights, you are relieving yourself from that burden because the competition does not need to hammer your subscription service they can just download your model and analyze it and run it all day.

Also for the largest models it makes no sense to run it yourself unless you are a major player. Renting the hardware is ludicrously more expensive than their subscription tens of thousands of dollars. And buying the hardware to run them is in the hundreds of thousands of dollars.

[yorwba]

The primary benefit of releasing weights is the attention it generates. Some people have the hardware to run it, try it out because it's free, tell everyone about it, and then even people who don't have the hardware might get interested and pay the original developer. So it's a marketing expense, basically.

The most popular LLM product in China is Bytedance's Doubao. You probably haven't heard of them since they never released weights and don't benchmark particularly well, but Bytedance already had enough users on its other apps that they could directly advertise Doubao to.

[bijowo1676]

I believe we are still very very early in AI development, so it doesnt even make sense to close models.

Open source and open weights model is how you can harness the potential of all humans to continue development and improving the SOTA of your model. Literally every student on the planet wants to play and improve these models for their own use case.

Plus the ecosystem, once you have users in the ecosystem on your open weight model, this is a giant leverage point in itself

[Shitty-kitty]

It's just a smart business decision that allows their models to compete and gain market-share against much pricier private models. No philanthropy there.

[foxglacier]

It depends how you define philanthropy - obviously corporations don't just donate such valuable products to the world to make it a better place, but in effect that's what they end up doing in their effort to gain market share or brand recognition. Actual human philanthropists are sometimes doing it for the similar reasons of self-promotion.

[notnullorvoid]

> Until there's some sort of "community owned hardware"

Or until some bright people figure out drastically more efficient means of training.

[UncleOxidant]

> The spigot can be turned off at any time.

True. And it's possible that this has already happened at Alibaba Qwen - at least for the smaller models that people had a chance of running at home (122B and smaller).

[gunalx]

We'll see. The qwen team has always released a few close to sota but proprietary models in between tgeir open releases. We did get 3.6 35B and 27B so its not all set in stone yet.

Its higley unlikely we get another open llama model though after the llama4 flop, even if their muse spark seems pretty good.

[recursive]

This seems backwards. Access to Fable can be removed. I don't see how an open weight model can ever be put back into the bag though.

[Smaug123]

The model itself, sure; the comment is about the production of more advanced models (to keep open weights near the frontier).

[recursive]

The proprietary spigots can be turned off at any time also. To me, that seems more likely.

[slashdave]

Training these models is not a "hardware" problem.

[nomel]

I think that simplifies it a bit. You can't train without hardware, which is why the Chinese companies are illegally importing Nvidia cards [1].

[1] https://www.theinformation.com/articles/deepseek-using-banne...

[adrian_b]

The usefulness of the smuggled NVIDIA GPUs has greatly diminished for AI purposes, because the elimination of NVIDIA as a competitor has allowed the growth of the production of domestic GPUs.

Moreover, China has just demonstrated a supercomputer faster than any US supercomputer, which unlike the US supercomputers, which need GPUs, achieves its high computational throughput with custom CPUs designed in China (implementing an Armv9-A ISA with SME, i.e. the scalable matrix extension, and with BF16/INT8 operations for AI).

The CPUs used in that supercomputer can reach both a computational throughput and a memory bandwidth sufficiently high for training any LLMs (they have fast HBM memory). Their only disadvantage in comparison with the best NVIDIA GPUs is a slightly lower energy efficiency, but China has abundant cheap energy so this is not a serious disadvantage for them.

[ForHackernews]

It's not pure philanthropy: https://gwern.net/complement

[jmyeet]

How is this a complaint? Once you have the model, you have the model. Download DeepSeek-R1 671B and you have it. You might not get improvements in the future, just like you may not ever get a future release of an open source project. Is that an indictment of open source?

But consider the alternative. OpenAI and Anthropic can shut off your account or API key at any time for any reason. How is this better? You have way more security when you're running your own model.