Posted by root-parent 4 hours ago
In the current climate limiting someone's use of AI might be expected to be about restricting access or restricting what someone can do with it, but the story here ostensibly seems to be about capacity constraints, not any limitation on what models or capabilities Google is giving Meta access to.
Cloud services like to present the illusion of an infinite amount of compute available at a fixed price per unit, but the reality is if you try to use too much of any service you'll find you have a quota and requests to increase it will fall on deaf ears if the provider doesn't have more of that resource.
Too much of my working life has been spent shoehorning services into less space/compute/ram/spindles or migrations to other data centers to solve such issues.
Having said that, I agree with you. You have to request limit increases often and can't scale even in those instances if you don't plan ahead.
There has to be a name for this deceptive marketing tactic where you say something is unlimited and then it is only unlimited as long as you don't use very much.
It would be one thing if you occasionally got a "no more capacity" error when requesting large amounts of resources but it doesn't work that way. They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
The tiny blog sure isn't for the cloud, but also it's not the main client of the cloud.
> it's 20% more than you are currently using and you pay 300% more for that.
I'm assuming you are comparing to self hosting. Then you need to account for things that are difficult to put a price like your time maintaining a physical infrastructure and the lessons you will learn with it.
Sounds like I'm defending the big cloud, but there is a valid use that is disconsidered because it's trendy to hate on the cloud.
> They confine you to a relatively small amount of resources the entire time you have an account. If you want more you have to request it.
It's a form of KYC, nothing wrong with that.
Like literally 10x times more expensive to do so, to run CI jobs...
I dont want to imagine the margin AWS has like generally, cause it can easily be a 90% too
I assume you're using your owned server and not a provider like Hetzner? So you did have a substantial delivery time. Although in my city is a recycled that resells used servers, and I could show up there with a truck and get a server within hours if I'm not too picky. Or use some random desktop or laptop off the pile, short-term.
I want to know how impacted Gemini has been by that, because that will reveal a lot about their margins and revenue generating first party demand. Each MSFT earnings report they discuss the balance they’re dealing with between supplying GPUs to Azure customers and first party demand.
My pet theory is that Gemini is “losing” the LLM race because they’re preferentially selling the TPUs to competitors, while keeping just enough for themselves to stay competitive and build their own products.
It's probably the best multimodal model I've worked with (if somebody knows a better one for audio analysis, please let me know!)
I HIGHLY doubt that Gemini is overloaded, Google has been bullshitting with their crap models since release. Waste of everyone's time.
OTOH, if they are stressing Google's capacity then it seems it has to be for production use, which would relfect a massive failure on Meta's side given their investment in datacenters and AI. If they can't utilize their own models and datacenters, then maybe they should just rent the excess capacity to Google! :)
And their safety tuning is neither effective nor precise on edge models.
Who says they aren't? Could be using all of them for "research".
Image generation and veo models I’d imagine quite effective for creators; new Instagram accounts with AI content that are garnering millions of followers in spans of weeks are quite common now
Llama Meta 70b is 50th or so down the list of popular models.
It has 24.1b tokens used in 7 days vs the top models that have trillions or hundreds of billions of tokens.
So practically dead!