alt Hacker News> Having a machine that can run some modest local LLMs, like the Gemma 4 12B, is really worth it.
Cloud models are (much) faster, they don't consume so much power/generate heat, they have much bigger (LLM) context, they're much more precise and they have a much wider (engineering) context of the given problem.
Except privacy and use cases that are blocked by cloud models (e.g. reverse engineering), local LLMs are currently an expensive toy.
When I try to program with a local LLM (I'm on a 32/128 GB system), I end up wasting time compared to a cloud LLM.
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From your post I can only perceive the instinct to pick a side, and trying to make sure it is the "winning side". But the truth is far more nuanced. I have acces to both, paid and local models, and even if slower, the local models have been far more educative about how these technologies are put together, and what is required for local computing to thrive again. Paid models will not suddenly disappear just because I play with glm-4.6 on Ollama. At the same time, my work pays the cloud subscription and I use the cloud models to perform the tasks my work requires. There's no need to choose one side.
> currently
The interesting question is whether that gap will narrow, and if so, how much, and on what timescale.
The exact answer to this question is not knowable, but if you are the kind of person who comes to a site called "hacker news", and you think there is a nonzero chance that the answer is that yes, the gap will narrow and this won't always be an expensive toy, then now seems like a pretty great time to get in the game and start exploring the capabilities.
I agree completely. I think local AI is best limited to purpose built SLMs; all this craze around running quantized coding LLMs has taken the attention off SLMs.
Same. Local LLMs are fun to experiment with, but when I want generated code of a sufficient quality, I use a cloud LLM.
> Cloud models […] don't consume so much power/generate heat
I do realize the cloud is just someone else’s computer right? Power goes in, tokens and heat come out - just in another place
Anything done local will likely come at higher cost and at scale with less energy efficiency and commodity, with less possibility to fine tune engineer deeply on wider horizon of issues.
That's never the point of keeping local alternatives though.
Again, I would not argue against any of this.
And I can't say that I won't switch to openrouter (even just for the same models) at some point.
But one of the things I have found about my own process learning is that some lessons only come to you when you make yourself available to them. And if that means doing things the difficult way, that is what you should do.