Kimi Linear: An Expressive, Efficient Attention Architecture

Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient.

How likely are we to NOT see the AI data center apocalypse through better algorithms?

Without policies, gains in efficiency are always compensated by increased demand. Global energy consumption by source is a good example, we've never consumed as much coal as now even though we have alternatives.

https://ourworldindata.org/global-energy-200-years

We have already seen huge efficiency increases over the last two years. Small models have become increasingly capable, the minimum viable model size for simple tasks keeps shrinking, and proprietary model providers have long stopped talking about new milestones in model sizes and instead achieved massive price cuts through methods they largely keep quiet about (but that almost certainly include smaller models and intelligent routing to different model sizes)

But so far this has just lead to more induced demand. There are a lot of things we would use LLMs for if it was just cheap enough, and every increase in efficiency makes more of those use cases viable

> How likely are we to NOT see the AI data center apocalypse through better algorithms?

Near certain IMO. Algorithmic improvements have outpaced hardware improvements for decades. We're already seeing the rise of small models and how simple tweaks can make small models very capable problem solvers, better even than state of the art large models. Data center scaling is nearing its peak IMO as we're hitting data limits which cap model size anyway.

At some threshold, efficiency gains let models move out of the data center though.

I don't think this worry is widespread, or even warranted. China has been able to more than double the US in energy production without massive effects on the environment by using nuclear, solar, and hydro.

If anything, the US is massively underproducing.