Almost Half of US Data Centers That Were Supposed to Open This Year Slated to Be Canceled or Delayed
Almost Half of US Data Centers That Were Supposed to Open This Year Slated to Be Canceled or Delayed
Ok, so they bought billions of dollars of ram/storage, to put inside servers that haven’t been bought yet, to put inside data centers that haven’t been built yet, in order to run AI that doesn’t work yet, in order to chase profits that are impossible to achieve.
And now, despite driving ram prices up to absurd prices, you’ve begun to realize the same thing all of us knew from before day one. NOBODY WANTS THIS SHIT!!!
NOBODY WANTS THIS SHIT!!!
That’s a popular take, especially around here, but AI does have some pretty nice use cases; just not as many as the TechBros would have you believe.
Here’s some examples I’ve personally seen in the last 14 days:
Does all of the “Agentic” Woo Woo shit work? No, it absolutely doesn’t but it is clearly getting better as time goes on.
IMO this whole AI thing has some very strong parallels to the early '80s computer industry. Right now it often requires specialist knowledge for good results which makes it clunky to use, it is somewhat slow, there’s very little interoperability, and it requires enormous amounts of power. Hell even this “over buying hardware” schtick fits right in, this happened with SRAM and then several times with DRAM as the industry matured.
However the industry is also making progress at almost insane speed; not only is the output getting demonstrably better but the negatives are being addressed. In the past 30 days I’ve seen prototype ASIC-esque hardware that works in a standard desktop PC and processes nearly 10,000 tokens a second with local processing.
The only reason you’re not seeing that kind of kit in the market yet is because the models are still changing too much and no one wants to commit hundreds of millions to making cards that would be outdated before they could be shipped. We’re probably only 18-24 months away though.
I’ve also seen 10x improvements in memory usage (TurboQuant) and literally dozens of little tweaks and tricks to reduce footprint and speed processing. Just like what was going on in the PC industry in the '80s and '90s.
So sure, Fuck AI (mostly) as it exists today but it won’t be long before it’s as ubiquitous as tablets and smartphones.
So sure, Fuck AI (mostly) as it exists today but it won’t be long before it’s as ubiquitous as tablets and smartphones.
In order for it to be this ubiquitous it has to run locally or on commodity hardware IMO. The true lasting effects from this hype cycle are likely the capabilities that are being driven into smaller language models that don’t have out of control resource requirements.
In order for it to be this ubiquitous it has to run locally or on commodity hardware IMO.
LLMs as they are, can already run on smartphones, which pretty are ubiquitous themselves.
So a flagship phone would have 12-16 gigs of RAM these days I believe. A low-end phone 4 gigs.
Here are the sizes of some different parameter count versions of Qwen 3.5, a popular Chinese open-weight LLM:
27B: 17 GB - not yet possible to run on current flagship phones, but once the RAM crisis ends, I could see this happening.
9B: 6.6 GB
4B: 3.4 GB
2B: 2.7 GB
0.8B: 1 GB.
For any recently manufactured device, there will be versions of multiple popular LLMs that will run on the RAM size they have available.
Most people do not have a smartphone with that amount of RAM. But ultimately, yeah, eventually it’ll run on readily available hardware or it’ll go into a dustbin.
There’s already ollama and stuff. That’ll last.