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PaperclipPerfector  3mo ago (text post)   25043 thread views

Culture War Roundup for the week of September 29, 2025

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I think that one aspect is the question which performance you actually require from the model.

A fundamental difference between free / open source software and open weight models is that for software, the bottleneck is mostly developer hours, while for models, it is computing power on highly specialized machines.

For software, there have been large fields of application where the best available options are open source, and that has been the case for decades -- for example, try even finding a browser whose engine is proprietary, these days. (Of course, there are also large fields where the best options are all proprietary, because no company considered it strategically important to have open source software, nor was it a fun project for nerds to play with, e.g. ERP software or video game engines.)

For LLMs, tens of billions of dollars worth of computing power have to be sacrificed to summon more powerful shoggoths forth from the void. For the most part, the business model of the AI companies which produce the most advanced models seems to be to sell access to it. If Llama or DeepSeek had happened to be more advanced than OpenAI's models, their owners would not have published their weights but charged for access. (The one company I can imagine funding large open-weight models would be Nvidia, as part of a commodize your complement strategy. But as long as no AI company manages to dominate the market, it is likely more lucrative to sell hardware to the various competitors than to try to run it yourself in the hope of enticing people to spend more on hardware than on model access instead.)

That being said, for a lot of applications there is little gain from running a cutting edge model. I may be nerdier than most, but even I would not care too much what fraction of IMO problems an AI girlfriend could solve.

Unfortunately, Nvidia sucks ass at making LLMs. Nemotron was a joke.

Mistral Nemo was great though.

Nemo? I know No One by that name.

Haven't tried it, unfortunately. I think it's still a poor showing that they needed another company to finish their work for them.

The trick's that the same chips used to produce a model are also usable to run the model for someone else, and a lot of the technologies used to improve training has downstream benefits on inference or implementation improvements. Every AI vendor has its own complement to turn into a commodity.

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