energy consumption by ai models is a growing concern.
a recent paper from korea’s KAIST, coauthored by Google, might be a major step forward.
the paper, titled “Mixture of Recursions”, presents some very promising results.
their architecture trains mini-routers that determine whether a token should go through multiple layers of attention.
think of it this way: common words like “the” don’t need deep processing, but rare, complex ones like “floccinaucinihilipilification” do.
they claim to halve model parameters while achieving state-of-the-art performance on few shot tasks across popular benchmarks.
cool, but what does this actually mean?
put simply: more parameters and tokens means more compute, and more energy.
halving the parameters roughly halves the compute cost per token, measured in FLOPs (how fast a computer does math).
that directly correlates with energy consumption.
in theory, this would cut energy usage for training and inference by 50%. in reality, this would be closer to 20-35%.
still, this trend toward efficiency makes models more viable across domains.
one especially interesting domain is robotics.
smaller models running on less compute could run on lighter hardware, making robotics easier to scale.
but believe it or not, research like this comes up every few days. lots of it looks cool on paper, but doesn’t translate well to production.
the real question: can it scale and maintain efficiency at the same rate?
while it maintains accuracy for 1.7 billion parameter models, recent models have come out with 1 trillion parameters.
it’s a step in the right direction, hopefully the right step.
and i know you’re curious, floccinaucinihilipilification actually means