[flagged]
Like what?
Oh, I don’t know, how about reducing the search space/accelerating the search speed for potential room temperature superconductors? Or how about the same for viable battery chemistries?
New LLM optimization technique slashes memory costs
41–50 of 227 posts
Re: New LLM optimization technique slashes memory costs
#42Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
If we're "lucky" (in an AI-optimist sense) we'll need the nuclear plants despite efficiency increases.
Re: New LLM optimization technique slashes memory costs
#43Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
Are we setting up nuclear plants for AI data centers? If so, I see that as a win all around. We need to rely more on nuclear power, and I'll take whatever we can get to push us in that direction.
Re: New LLM optimization technique slashes memory costs
#44Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
Jevons paradox says as things get more efficient, usage goes up. In this case, even if AI data centers don't pan out, I think we'll still find use for the electricity they generate.
Re: New LLM optimization technique slashes memory costs
#45Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
No, as the things using that power get better (newer models keep getting less garbagey) and cheaper (faster hardware and more efficient use of power), people will keep coming up with more things to use them for.
Re: New LLM optimization technique slashes memory costs
#46Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
No, not really. AWS getting more power and space efficient chips didn't reduce total power demand, they just added more cores.
Even if the data centers didn't keep up with available capacity, energy demanding industry move to and expand with sources of power, like aluminum production.
Re: New LLM optimization technique slashes memory costs
#47Re: New LLM optimization technique slashes memory costs
#48Is it possible that after 3-4 years of performance optimizations, both algorithmic and in hardware efficiency, it will turn out that we didn’t really need all of the nuclear plants we’re currently in the process of setting up to satisfy the power demands of AI data centers?
Nobody is building nuclear power plants for data centres. A few people have signed some paperwork saying that they would buy electricity from new nuclear plants if they could deliver it at a certain price, a price mind you that has not been done before. Others are trying to restart an existing reactor at three mile island (a thing that has never been done before, and likely won't be done now since the reactor was shut down due to being too expensive to run).
And certainly nobody is building one in the next 3-4 years; they'd be lucky to finish the paperwork in that time.
What is actually going to power them is solar, wind, and batteries: https://www.theverge.com/2024/12/10/24317888/googles-data-ce...
Re: New LLM optimization technique slashes memory costs
#49Earlier quoted context omitted.
As far as I know, finance is not all in. I see Goldman Sachs doing experiments, for example, but it doesn't feel like they're convinced yet.
Finance is basically all of the reasons not to use (generative, LLM based) AI , all in one vertical. The poster child of determinism.
Could you please explain?
Finance is a big industry, and they are doing lots of different things.
Re: New LLM optimization technique slashes memory costs
#50[flagged]
We are putting lots of optimisation efforts into lots of worthwhile endeavours.
I dunno, software seems to be getting worse, hardware is getting more expensive and both Microsoft and Apple are distracted by AI, not to mention NVIDIA who seem to have bet the farm on Deus Ex Shovel