That's not at all true.
There have been tons of applications for this. People were using earlier LLMs like BERT for classifiers long before LLMs became viable chatbots.
2,214 karma · joined March 4, 2014
There have been tons of applications for this. People were using earlier LLMs like BERT for classifiers long before LLMs became viable chatbots.
That is very not true for many cases. Agents generating code are usually painfully slow, finding workflows that replace that reasoning with running code usually speed things up in my experience.
You can pay for higher cache time, you can pay for NVMe KV cache for an hour that can just be reloaded, etc., at a lesser tier you can pay for the KV cache to be stored on a network store (I guess I'm unclear if that last tier would be cheaper than recomputation, not even 100% sure of the NVMe with direct GPU<->storage DMA) depending on your model settings.
If a program runs slower when you give it more RAM, the problem isn't giving more RAM.
Sounds like religious discrimination.
Occam's razor is that it's probably not all that different unless there is some specific reason to believe otherwise.
E.g. over-fitting.
> and concentrate the likelihood around the zero loss set. Then reduce the variance on a Gaussian prior.
Those phrases could mean a lot of different things. What are you proposing?
> so that any measure of model quality will monotonically increase with model size and achieve a maximum at infinite model size.
any measure of model quality? You must have some bounds of any measure, since trivially that's false because "fewer parameters is better" is a measure of model quality, even if dumb.
It's hard to even engage when you're being so imprecise, and not even giving one specific example.
The whole idea of "let's change semantics to make it easier" is dumb.
If you want guaranteed tail calls, change your code until it works.
No, it won't change semantics - if you say @musttail or similar, it will simply fail to compile if you, say, introduce a destructor - the semantics will not subtly change.
AWS and Azure give you the same thing for Claude and ChatGPT, no need to be stuck with open weights. They might sometimes store some of it for other purposes (I don't know the specifics), but it is emphatically not being fed back to OpenAI or Anthropic.
You pay more for it, but if you care that much, use it.
It can create obligations to provide future GPUs in return for present money.
Yes at some point people might start to question, but what are the true hard limits there, especially once SPVs and such start to get involved to shuffle things off the books?
If you can fab 1000 chips, and can sell some for $500 and some for $80000 what are you going to do?
The game GPU is at once profitable, but causes them to give up far more profits than they're gaining from it.
They're maintaining the game market to have multiple markets and not go all in, but it's strategic hedging at this point. When NVidia makes a gaming GPU instead of a data center GPU they are leaving money on the table in the short term since they're constrained at the fab level.
That... might actually be an improvement?
Correct, which is why it's crazy to consider a Jeffrey's credible interval as valid, but not a confidence interval.
Perhaps the confidence interval might not be valid under a frequentist interpretation, but the same math can be valid under a Bayesian interpretation even if the person publishing didn't mean it that way.
Does that mean it's valid? Well, the confidence interval is as equally valid as a lot of credible intervals, which is why the credible interval is not some magical better alternative - it's mostly just a choice of how you interpret it, it is not a different procedure that produces different numbers.
I am unsure if it is incapable, but it sounds hard.
They have tons of cores with 64k of SRAM each, and relatively slow paths in/out.
On a GPU you can leave it in SRAM. On Cerebras, you have to send it out of the system which is a giant bottleneck.
TSLA would be worth crap if it were a private company giving off dividends. It is really truly about the insane valuations driven by collective delusion.
These days, it can be sustainable for a tiny team, named Claude and Luna.
Why doesn't every engineer have a side project or three for small market things of this caliber in 2026?
A lot of his great discoveries were mostly that he was very knowledgeable about the bleeding edge research in a number of disparate areas, and was able to have the aha moment where he could make the connections for how to integrate them.
A lot of other thinkers who created new fields from scratch are probably way harder for an LLM to crack.
That is very aligned with an LLMs ability to have superhuman knowledge in wide areas.
The ability to do a ton of book learning in training, and pull in tons of related context at once, is superhuman in some ways, but lags a lot in others.
So don't use them for inference.
Because there are more privacy guarantees there, depending on the provider. "But what if they violate their contract!" is some pretty tin-foil hat stuff.
How is this any different than a business running their website out of the cloud, assuming you are using a provider with appropriate contractual terms?
You can care about tracking and ads but still be comfortable storing your backups in the cloud, and many have been for quite awhile, even sometimes without encryption - that is totally different than e.g. Meta actively trying to track you and understand your relationship graph and your purchases etc.