> So now I joke that even if I have a claude max plan, I've still come out ahead financially.
This is no joke; for better or worse, I see a day when I'm paying a lot more for this and it will be a bargain.
This is no joke; for better or worse, I see a day when I'm paying a lot more for this and it will be a bargain.
And it looks like those very helpful capabilities will continue to transfer to smaller models as well, as architectures and training regimes continue to refine.
I can fairly easily imagine a world where the only people needing to spend a lot of money on models are those that are using them to solve truly novel problems. The rest of us will get plenty of use at reasonable costs for the typical day-to-day helpful stuff.
Nope. Also I'm not GP.
$10k might even be worth it - but i'm assuming that the more expensive it is the beefier it is too, which also means more electricity.. and i already run ~6 computers/servers in my house. If a power surge happens i'm going to go live in the woods lol.
like, yesterday.
I'm hoping that by the time the rugpull happens with SOTA (claude/etc) that at-home will be in the 4.7-5.5 range? We'll see.
Maybe your tooling is what’s keeping you from your dream.
But maybe my limited understanding is thinking of this wrong.
I've run the latest local models over the last year, including the recent Qwen 3.6 30B A3B, on a 9yo GTX 1080 and 32G RAM I have lying around[0]. If I can do that I don't think hardware will be a problem for you in the near term. The only updates I've needed were to Llama.cpp when a new class of model was released.
[0]: In my case, I want to see how local models perform on limited hardware, sacrificing context size and intelligence compared to SOTA models, so I have to really limit my expectations.
I think the same, and it's why i stopped caring about running llama/etc at home last year. That coupled with the models being dumb by comparison to SOTA really make me fine with waiting.
But in a year or two it's going to be difficult to resist at home, assuming the pace of improvement holds.
Anything beyond that is just hobby, or continued education.
(UPS is still a great idea for your expensive gear.)
In reality now, curious about social implications generally. Does this go beyond problem solving? Maybe the intelligence per token you get via your free library card/membership is insufficient to compete with peers in dating/employment/etc. markets, thus puts you at disadvantage.
that’s already how world financial markets and governance work,
and yes, the best of the best models
and $ for tons of compute
will, for now, remain at the top.
For what it's worth, I also used GPT-5.2 (via duck.ai) this year for questions about taxes and it was helpful — which makes sense because there's an abundance of material about taxes out there to be synthesized, so a text predictor trained in that domain should do well.