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Local AI needs to be the norm

unix.foo

71–80 of 804 posts

Re: Local AI needs to be the norm

#71

Yet there is another post a few rows down where people are losing their shit that Chrome has a local LLM model that uses a couple of GB of space for local-inference. Damned if they do, damned if they don't.

This is a bit disingenuous. People aren't losing their shit about a local model being installed. It's the lack of user autonomy. Just give the option to download a model instead of a silent install. It's not that hard. This is how every other local option works.

Re: Local AI needs to be the norm

#72
Question: for software development, how much of an AI do you need for local development? Can it be run locally? Can someone train something that knows a lot about software but lacks comprehensive coverage of history, politics, and popular culture?

Re: Local AI needs to be the norm

#73
post #17
post #5

Earlier quoted context omitted.

You're maybe missing the article's point, which is to use local models appropriately: > “But Local Models Aren’t As Smart” > Correct. > But also so what? > Most app features don’t need a model that can write Shakespeare, explain quantum mechanics, and pass the bar exam. They need a model that can do one of these reliably: summarize, classify, extract, rewrite, or normalize. > And for those tasks, local models can be…

This is a bit naive IMHO... I have tried quite a bunch of local models, and the reality is that it's not just a matter of of "it's a small model that should be hostable easily". Its also a matter of whats your acceptable prefill TTFT and decode t/s. All the local models I used, on a _consumer grade_ server (32GB DDR5, AMD Ryzen) have been mostly unusable interactively (no use as coding agent decently possible), and e…

1) Again, I suspect you're missing the point of the article. The iPhone's on-device LLM is (apparently) ~3 Bn parameters - and runs well/fast enough to be used in the manner described. Of course, the iPhone has its GPU to leverage.

2) It's probably not the time/place to trouble-shoot your "consumer grade server" LLM experience, but if you're running on CPU (you don't mention a GPU) then yeah, your inference speed will be slow.

3) Counterpoint: my consumer-grade Macbook Pro (M1 Max, 64GB) runs Qwen3.6-35B-A3B fast enough to be very usable for regular interactive coding support. (And it would fly with smaller models performing simpler tasks.)

Re: Local AI needs to be the norm

#74
post #72

Question: for software development, how much of an AI do you need for local development? Can it be run locally? Can someone train something that knows a lot about software but lacks comprehensive coverage of history, politics, and popular culture?

[deleted]

Re: Local AI needs to be the norm

#75

Earlier quoted context omitted.

If it was such a good and laudable idea why didn't they tell me about it before they activated it? It seems to me like they avoided it in the hopes that I wouldn't notice, because, presumably if I had, I would have IMMEDIATELY disabled it. Also why doesn't their task manager show that it's actually the one downloading? Why does it go out of it's way to hide this activity? Since I have conky on my desktop I could catc…

I'm guessing you immediately close the What's New Chrome tab when you update? https://developer.chrome.com/blog/new-in-chrome-148#prompt-a... https://www.google.com/chrome/ai-innovations/ They have absolutely not been shy about any of this.

I've never had a "What's new" tab ever open because I disable the customized home page where that's displayed. I'm guessing you're not aware that's an option.

Please show me where in either of those documents it explains it's going to download a 4GB model.

Re: Local AI needs to be the norm

#76
post #56

Earlier quoted context omitted.

It should be feasible to crowd fund training runs right?

A training run costs somewhere in the neighborhood of a billion dollars. That’s a thousand millions. How many crowdfunded projects do you know that have raised even one percent of that? Who’s going to be in charge of collecting that scale of money? Perhaps some sort of company formed for the benefit of humanity, which will promise to be a non-profit? Some sort of “Open” AI? Oh, wait.

I can't say that you are lying and you are not exactly exaggerating either. It is true that a new SOTA model -- from literal scratch -- it would be expensive.

But, and it is not a small but, is the starting point really zero?

Re: Local AI needs to be the norm

#77
post #72

Question: for software development, how much of an AI do you need for local development? Can it be run locally? Can someone train something that knows a lot about software but lacks comprehensive coverage of history, politics, and popular culture?

The ones that are good for more than elaborate auto-complete are pretty hefty, but it can be done. They’re still not Opus behind claude code.

Re: Local AI needs to be the norm

#78
post #62

Two issues - 1. Local models are likely to be more power-expensive to run (per-"unit-of-intelligence") than remote models, due to datacenter economies of scale. People do not like to engage with this point, but if you have environmental concerns about AI, this is a pretty important one. 2. Using dumb models for simple tasks seems like a good idea, but it ends up being pretty clear pretty quick that you just want the…

I think using the best model for every tasks makes sense when these models are subsidised. when the prices go up (assuming they do) this could trigger a more varied approach. assuming the model doesn't self select for you.

Re: Local AI needs to be the norm

#79
We need computers with 128gb or maybe even 192gb of memory before local use make sense. From my own experience 32b LLMs are the absolute minimum for proper tool use and decent output quality. But for local ai you want also vision models and maybe even various LLMs. Plus some memory for the system of course. On my 36gb M3 the 24b Gemma model is nice. But the entire system gets allocated for that thing.

Re: Local AI needs to be the norm

#80
They will be, and that moment is not that far off. We've got the progression in place already: first, large data centers could have performant LLMs, we are now firmly in "a bunch of servers with a couple of H100s each" territory, slowly going into "128 GB VRAM on a MacBook Pro or a Strix Halo". Within the next year, the pattern of "expensive remote LLM for planning, local slow-but-faster-than-human LLM for execution" will become the norm for companies, slowly moving to "using local LLM for everything is good enough". And then we'll have the equilibrium we already have with the "classic cloud": you either self-host or pay for flexibility and speed. The question will be: how much of the current compute capacity craze will local hosting give the kiss of death to and what that means for the market.
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