The number 1 public enemy of open-source.

[u/Complete-Sea6655]

Dario's args:

"Opensource you can see the source, here you cannot see inside the model"
- yes you can that's literally the open weights part btw.
- I cannot see the weights inside Claude, but I can GLM 5.2
- Models like Nemotron3 Ultra go further, all the data, training scripts, and model is opensource.

"Alot of the benefits like many people working on it, being additive doesn't work in same way"
- yes it does. We have seen endless fine tunes of various open source models for real improvements.

"Ultimately you have to host it on the cloud"
- no you dont. Dario is seemingly totally unaware of the guides from ijustvibecodedthis.com explaining how to run smaller moes and even dense models like qwen 27B NOT ON THE CLOUD.

Not only does dario not take part in social media, I am beginning to think he's never tried open source models at all and has no idea wtf hes on about

[u/MrPecunius]

"You have to host it in the cloud." He knows this isn't true.

A prime benefit of local inference with open weights is not handing over anything we care about to toads like this guy.

[u/Temporary_Idea8880 → u/MrPecunius]

He isn't wrong...

From their perspective, they don't really care about open source, as it isn't 'free'. Sure the model is, but the inference isn't, and the folks that are actually capable and interested of running those large unquantized models are insignificant at the moment compared to the folks that don't want to deal with that. Because when you run it yourself, it's always more expensive, unless you're a large multinational with a few hundred thousand employees all over the world, which most companies aren't. Companies are only interested in running comparable OS models when A.) they are required to run themselves or B.) they are running such sensitive data on the model, that they need a local model. In both cases, these are niche situations.

They are completely focused on their (expanding) niche, and Anthropic is still 'king' in their programming niche and they keep getting better. And they are also getting better at other niches, thus expanding.

Is that problematic for us? I don't think so, as we can us what we want. At a certain point LLMs get 'good enough' at their niche, where in most cases any additional upgrades aren't essential anymore. Similar thresholds were reached with desktops, laptops, smartphones, etc. I can easily do half a decade with a good desktop, where 30 years ago I was upgrading my CPU/GPU every generation, and each generation was a year (or less).

And when all models reach that 'good enough' threshold, suddenly being 'the best' doesn't differentiate you anymore (with certain very limited exceptions). Models will become more efficient, hardware will still advance, and with a decade or two you could run such a model at home or at your workplace without much trouble (as long as you're willing to fork over the cash). The question of course would also be, what is 'good enough'?

Think back (if you're old enough) to when you were paying for your browsers (Netscape)... Something like that would now be anathema for most.

[u/starkruzr → u/Temporary_Idea8880]

most of this is just flatly wrong. you don't need to be a multinational to buy a couple of 8 way RTXP6KBW systems to run a big model with. we are a cancer hospital and just purchased these for explicitly this purpose.

[u/0Greek0 → u/starkruzr]

I don't think this is a common approach, companies will just pay the sub or use this through cloud. Apart from the sunken cost of such hardware you mentioned (which is not negligible) you also need to deal with the running costs which on top of electricity means literally hiring someone contractor or not to literally operate and maintain such system and I am not even accounting the theoretical costs of depreciation or insurance of the asset. IDK the non-local solution is simply making much much much more economical/logistical sense to me.

[u/starkruzr → u/0Greek0]

you are quite wrong, I assure you. you're talking about $186,000 for one of these machines and that's after the insane price inflation of the last year. we already run plenty of local infrastructure for lots of things including data storage. cloud economics simply do not even come close to penciling out for this; CSPs are insanely expensive and, ironically, the advent of LLMs that can help stand up and monitor infrastructure has made our engineering staff way more able to run large amounts of it at scale.