Rendered at 13:35:32 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
ndom91 25 minutes ago [-]
Shameless self promotion: I put together an AI infra built-out financials data-vis project/page recently. It includes graphs and tracks updates to stuff like GPU depreciation schedules like this.
GPUs might be 3 years but the rest of the hardware: processor cores, ram, storage can power other cloud applications for a decade or more no?
ericd 2 minutes ago [-]
Except the GPUs last a lot longer than 3 years - A100s are 6 years old at this point and still in heavy use.
deadlast2 1 hours ago [-]
If you use opencode for your AI work or becomes affordable. To the point where you would reasonably build applications with intelligence the LLM version. I think in the near future the intelligence will be free and there is no business in free. I know it is a big statement but if you look around there is already a large amount of free models of gradually increasing strength. For most applications you don't need a gpt6
smashspectacle 2 hours ago [-]
Ed Zitron is going to become the Simpsons of ai critiques...
pllbnk 14 minutes ago [-]
Lots of respect for him for staying unwaveringly principled while knowing when and how to adjust his expectations and predictions. He is really well informed and it's something else to watch him speaking live on the podcasts and be so specific with numbers. He really knows his stuff.
ericd 22 seconds ago [-]
[delayed]
saagarjha 11 minutes ago [-]
He really does not.
sevenzero 8 minutes ago [-]
Good point, very well structured argument here
jdietrich 2 hours ago [-]
Siri, what's the spot price for an A100?
4 minutes ago [-]
DennisP 45 minutes ago [-]
Bad for hyperscalers. Good for Nvidia.
orwin 2 hours ago [-]
I don't want to argue here, but once models are trained, using them is quite cheap. Also in the last few months most improvements I've seen are in the smaller sized models, which might make using models even cheaper.
Driver4732 1 hours ago [-]
If the models are cheap to run, while good for customers, isn't that a problem for hyperscalers and all the investment poured into the AI data center build out?
skeezyboi 2 minutes ago [-]
its a false equivalence.... the dotcom boom wasnt just a fibre rollout, it was all the internet company startups that were overpriced and crashed. no doubt some of the hyperscalars will get busted out, but the models and LLMs are here to stay. just like dot com websites didnt disappear after the dot com crash, neither will AI transformer models
BryanBigs 34 minutes ago [-]
All of this spending only has a chance of paying off if video generation takes off.
KevinMS 43 minutes ago [-]
> using them is quite cheap.
until the other guys comes out with a slightly better model, then using yours wont be cheap enough.
nisegami 2 hours ago [-]
There's this weird tension where small models are getting a lot better, but the gap between the frontier and "what I can run on my gaming GPU" at home isn't necessarily getting smaller. That said, Qwen-3.8-27B-Q3 runs on my 9700 XT and performs well enough for tool calling in agents, which is a critical floor in local model performance imo.
TonyStr 1 hours ago [-]
Do you use qwen for any coding? Do you make a frontier model orchestrate it? Are you running with llama.cpp? Sorry for the barrage of questions, but I've been interesting in local AI for a while, yet I've struggled to set up anything usable.
esseph 59 minutes ago [-]
Just go download and install lemonade server. Open web ui for front end. There are mobile apps to connect to it.
Another thing to pay attention to is the margins of hardware companies like nvidia or micron. Hyperscalers are spending huge amount of money but the money mostly goes to the profits of hardware companies.
https://artificialliabilities.com
until the other guys comes out with a slightly better model, then using yours wont be cheap enough.
https://lemonade-server.ai/