RTX 6000 Pro retails for $10k so an 8x is $80k before anything else in the computer, and long-context will have... pretty bad performance (20+ seconds of waiting before any tokens come out), but it's true it technically works.
I don't think cloud models are going away; the hardware for good perf is expensive and higher param count models will remain smarter for a looong time. Even if the hardware cost for kind-of-usable perf fell to only $10k, cloud ones will be way faster and you'd need a lot of tokens to break even.
> I don't think cloud models are going away; the hardware for good perf is expensive
I think local AI will win in its niche by repurposing users' existing hardware, especially as cloud hardware itself gets increasingly bottlenecked in all sorts of ways and the price of cloud tokens rises. You don't have to care about "bad" performance when you've got dedicated hardware that runs your workloads 24/7. Time-critical work that also requires the latest and greatest model can stay on the cloud, but a vast amount of AI work just isn't that critical.
Users do not have an existing $80k of hardware, are not going to buy $80k of hardware for worse performance than paying $100/month, and models are continuing to grow in size while memory grows in price.
You said you need $80k in hardware for "good performance". I'm saying the local AI inference workflow will be a lot more flexible about performance than that, and can get away with something vastly cheaper and in line with what the user owns already.
What's the basis for saying local tokens will always be cheaper? As others have outlined, LLMs serving one user at a time are pretty expensive, but concurrent users become much more cost-effective (assuming there's enough RAM for the contexts). If "local" to you means ~10 hours daily use by a team of employees, the company still has to balance against cloud services that can amortize non-recurring costs over 24 hours of service per day.
You probably don't need knowledge about Pokemon or the Diamond Sutra in your enterprise coding LLM.
That's one of the biggest remaining head-scratchers in this whole business. You do need all that unrelated stuff to make a good coding model.
Nobody knows why you can't build a coding model by training on nothing but code, CS texts, specifications, and case studies, but so far it appears that you can't.
This one is kind of obvious - because people prompt coding LLMs with natural language. That's unrelated to stuffing the pre-train set with trivia factoids.
An LLM that knows English very well isn't actually very large and certainly not hundreds of billions of parameters.
If the smarts came from post-training, we could show significant gains by doing that post-training again for previous generations of models. But we know that isn’t happening - effective post training is necessary but not sufficient for model performance.
I don't think cloud models are going away; the hardware for good perf is expensive and higher param count models will remain smarter for a looong time. Even if the hardware cost for kind-of-usable perf fell to only $10k, cloud ones will be way faster and you'd need a lot of tokens to break even.