And you can't take comfort in knowing that you, personally, will remain in control of your own computing. The majority will let the range and direction of their thoughts and output be determined by the will of the tech giant whose AI they adopt. And that will shape society.
> “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 truly excellent.
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 even for things like classification, context size is immediatly an issue.
I say that with 6m experience running various local models for classifying and summarizing my RSS feeds. Just offline summarizing ans tagging HN articles published on the front page barely make the queue sustainable and not growing continuously.
Used to take me maybe 10-20 minutes per sheet.
Then I got codex to whip up a script that sends each sheet to a fairly low parameter locally running LLM and I have the yaml in a couple seconds.
My dream is to bootstrap myself to local productivity with providers… I know I’ll never get there because hedonic treadmill etc, but I do feel there’s lots more juice to squeeze. I just need to invest more time into AI engineering…
If you project out that hardware just a couple of years, and the trained models out a couple of years, you end up in a place where it makes so much more sense to run them locally, for all sorts of latency, privacy, efficacy, and domain-specific reasons.
Not all that different from the old terminal & mainframe->pc shifts.
Finally - hardware has seemingly gotten out ahead of software that most folks use - watching YouTube, listening to music, playing a game or two. There was a time when playing an mp3 or watching a 4k video really taxed all but the nicest systems. Hardware fixed that problem, like it very well could this one.
Basically small and medium models that are crazy well trained for their sizes.
Then we have a lot of specular decoding stuff like MTP and others coming to speed up responses, and finally better quantisation to use less memory.
Local LLM is the future, and the larger labs know that the open models will eat their lunch once people realise that the gap is only a few months. If we were good with LLMs a couple months ago, we're good with the open models now.
The promised mega-data center deals are meant to boost valuations today, not serve tons of customers three years from now.
I may personally be of modest intelligence, but to acquire the intelligence that I do have, I did not need to train on every book ever written, every Wikipedia article ever written, every blog post ever written, every reference manual ever written, every line of code ever written, and so on. In fact, I didn't train on even 1% of those materials, or even 0.00000000001% of those. The texts themselves were demonstrably not a prerequisite for intelligence.
At minimum, given that it only took me about 20 years of casual observation of my surroundings to approximate intelligence, this is proof positive that the only "dataset" you need is a bunch of sensors and the world around you.
And yes, of course, the human brain does not start from zero; it had a few million years of evolution to produce a fertile plot for intelligence to take root. But that fundamental architecture is fairly generic, and does not at all seem predicated on any sort of specific training set. You could feasibly evolve it artificially.
In the future, when regular home computers have the capabilities of modern servers, we'll be able to train the entire LLM at home.
On the other hand… v4 flash model is actual magic compared to what was available 2 years ago. If the rate of improvement stays as is, we’ll get a similar performance in a ~120B model in a year, which is viable (if expensive) for everyman hardware. Possibly you’ll be able to run its equivalent on a ~$1200 laptop by 2028, which for me-in-2020 would sound straight out of a scifi movie. A good harness that lets the model fetch data from other sources like a local wikipedia copy from kiwix could do a lot for factual knowledge, too; there’s only so much you can encode in the model itself, but even a cheapish (pre-curent prices) 2TB drive can hold an immense amount of LLM-accessible data.
Big caveat: I don’t see local models for programming or generally demanding agentic tasks being worth it anytime soon. You likely want bleeding edge models for it, and speed is far more important. Chat at 20tok/s is fine; working on even a small codebase at 20tok/s, especially on a noticeably weaker model, is just a waste of time. Maybe it’s a PEBKAC but I have no idea how people make any meaningful use out of qwen 3.6.
If we could even get something like GPT 5.5 running locally that would be quite useful.
proceeds to brutalise the reader with an 88-point headline font.
Welcome back to 2014. Let us now continue yelling at the cloud.
The dependency we have with anthropic and openai for coding for instance is insane. Most accept it because either they don't care, or they just hope chinese will never stop open weights. The business model of open weights is very new, include some power play between countries and labs, and move an absurd amount of money without any concrete oversight from most people.
It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control.
The huge difference to open source is that you can't just train an LLM with free time and motivation. You need lots of data and a lot of compute.
I sure want to be wrong on that, I definitely like the open-weight version of the future more
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.
This is what I do not understand as well and advertising the knowledge and more advanced model is also the only thing that comes to my mind.
Since a month I am using gemma4 locally successfully on a MBP M2 for many search queries (wikipedia style questions) and it is really good, fast enough (30-40t/s) and feels nice as it keeps these queries private. But I don't understand why Google does this and so I think "we" need to find a better solution where the entire pipeline is open and the compute somehow crowdfunded. Because there will be a time when these local models will get more closed like Android is closing down. One restriction they might enforce in the future could be that they cripple the models down for "sensitive" topics like cybersecurity or health topics. Or the government could even feel the need to force them to do so.
It builds good will also. it also shows research prowess.
For China it's different. They need to show Americans who don't trust them at all because of propaganda that they have no tricks up their sleeve. It also doesn't hurt when Chinese companies drop models for free people can run at home that are about as good as sonnet for free. Serious mic drop.
I have to conclude that people would like to have powerful local AI but it should at the same time only be a tiny model. In which case it wouldn't be powerful.
Local models need to be resident in expensive RAM, the kind that has fat pipes to compute. And if you have a local app, how do you take a dependency on whatever random model is installed? Does it support your tool calling complexity? Does it have multimodal input? Does it support system messages in the middle of the conversation or not? Is it dumb enough to need reminders all the time?
Spend enough time building against local models and you'll see they're jagged in performance. You need to tune context size, trade off system message complexity with progressive disclosure. You simply can't rely on intelligence. A bunch of work goes into the harness.
Meanwhile, third party inference is getting the benefits of scale. You only need to rent a timeslice of memory and compute. It's consistent and everybody gets the same experience. And yes, it needs paying for, but the economics are just better.
Why not ship your own model? In the age of Electron apps, 10GB+ apps are not unheard of.
It seems easier to have industry specs that define a common interface for local models.
I also assume the OS can, or would need to, be involved in proving the models. That may not be a good thing depending on your views of OS vendors, but sharing a single local model does seem more like an OS concern.
Reading the tea leaves here, it will probably be common for OS’s to have built in models that can be accessed via API. Apple already does this.
The problem is that it's much easier to use the SOTA models (especially if they are subsidized) instead of spending time fixing the knobs with the local one.
I just realized this with coding agents, yeah, you probably shouldn't always use latest version at xhigh, but you will end doing it because you do the job in less time, with less "effort" and basically at the same price.
I guess we'll see a real effort for local AI only when major vendors will start billing based on actual token usage.
I have a sneaking suspicion this is kinda like the situation with Linux in the 90s, where it kinda worked but it reeeeeally wasn't ready for the home user, but you had a lot of people who would insist to your face everything was fine, mostly for ideological reasons.
Damned if they do, damned if they don't.
Not to mention that the LLM that I choose to run requires a monster machine and is infinitely more capable than whatever google chose to put on their browser?
I mean, none of this affects me because I don't use chrome, obviously, but you don't see the difference? Bewildering.
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 point.
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 smartest model you can afford for absolutely every task.
sgt•3h ago