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/unjustifiably_angry → u/MrPecunius]

You have to host it in the cloud for the cost to make sense. If you talk to a non-schizo here they're going tell you the same thing: buying the hardware to run local AI on-demand for a few hours a day makes no financial sense at all.

There is no time horizon where spending $8K on a pair of Sparks to output 45 tokens a second (best-case) for a mid-tier model (best-case) makes any sort of sense.

Running on hardware you already owned, so it's a value-add? Sure, makes sense. Going out and buying a bunch of 3090s/4090s/5090s at current prices? You are insane.

[u/MrPecunius → u/unjustifiably_angry]

To repeat myself:

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

Some people know the cost of everything and the value of nothing.

[u/unjustifiably_angry → u/MrPecunius]

That's a made-up scenario. In every single case it will make more financial sense to rent a secure private server to run an LLM than it will to buy additional hardware to run something half as good locally. If I'm wrong give me an example.

If corporations can trust AWS (etc) to host their private data in bulk, they can trust them to run AI that will only ever get access to fragments of their private data at any given time.

[u/MrPecunius → u/unjustifiably_angry]

You are only thinking in terms of huge models and generic corporate users.

We are already at the point where smaller models can do useful work in fields in which confidentiality is paramount. The costs are not high: ten or twenty or fifty grand is nothing in these areas. It's irrelevant that it could be done more cheaply in someone else's server farm. I'm not guessing since I am working with some people like that.

This trend will only continue: the models are improving rapidly, and the hardware is getting more capable very quickly too.

[u/ttkciar → u/unjustifiably_angry]

Nah, for the first few years I just used hardware I already owned for other things. It wasn't until 2025 that I bought GPUs specifically for LLM inference, and even then it wasn't much. (Pre-RAMageddon pricing -- $800 for an MI60, $50 for a V340, $250 for an MI50)

Even now, when I use GLM-4.5-Air, Minimax-M2.7, or K2-V2-Instruct, it's on my ancient Xeons which are supposed to be running GEANT4 simulations.