Their point isn’t that local models are better or even as good more but that if you can do 50%+ of tasks with local then that’s 50% of tokens that aren’t captured as compute done in data centers.

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popularonion 2 hours ago | parent [-]

> As you can see, on average, SLMs are as good if not better than LLMs in 81.2% of the cases, with the LLMs having a significant advantage only in areas like engineering, life sciences, transportation and computer sciences.

So what I’m reading here is “LLMs have a significant advantage” in the most critical areas that have practically infinite demand for more intelligence.

	▲	eigenspace 2 hours ago \| parent [-]
		The article is kinda dumb, and yes this is clearly the area where frontier models having and advantage matters the most, but I'd point out that these smaller open-weight models are performing better than the big Frontier models of just 4-6 months ago. This means that the Frontier labs are under immense pressure to maintain that lead, and could end up in serious trouble if they stumble at all. The other thing id point out is that a lot of us who are token-sensitive do things like build plans using expensive, smart models, and then execute those plans using cheaper dumber models. Then there's the fact that we are still in the age of heavily subsidized Frontier subscriptions + tokenmaxxing initiatives from megacorps. Neither of which are sustainable, and will drive more usage to smaller open models once they end.