Overall, when tested on 40 prompts, DeepSeek was found to have a similar energy efficiency to the Meta model, but DeepSeek tended to generate much longer responses and therefore was found to use 87% more energy.
That’s kind of a weird benchmark. Wouldn’t you want a more detailed reply? How is quality measured? I thought the biggest technical feats here were ability to run reasonably well in a constrained memory settings and lower cost to train (and less energy used there).
The benchmark feels just like the referenced Jevons Paradox to me: Efficiency gains are eclipsed with a rise in consumption to produce more/better products.
More detailed and accurate reply is preferred, but length isn’t a quantifier for that. If anything that’s the problem with most LLMs, they tend to ramble a bit more than they need to, and it’s hard (at least with just prompting) to rein that in to narrow the answer to just the answer.
Longer!=Detailed
Generally what they’re calling out is that DeepSeek currently rambles more. With LLMs the challenge is how to get the right answer most sussinctly because each extra word is a lot of time/money.
That being said, I suspect that really it’s all roughly the same. We’ve been seeing this back and forth with LLMs for a while and DeepSeek, while using a different approach, doesn’t really break the mold.
This is more about the “reasoning” aspect of the model where it outputs a bunch of “thinking” before the actual result. In a lot of cases it easily adds 2-3x onto the number of tokens needed to be generated. This isn’t really useful output. It the model getting into a state where it can better respond.
The FUD is hilarious. Even an llm would tell you the article compares apples and oranges… FFS.
You might think this is apples and oranges, but I think it’s just another dimension: whether it’s better to have quality and bountiful output, or if such gains are eclipsed by the far wider appeal and adoption of such technologies. Just like how the cotton gin’s massive efficiency and yield increase in turning harvested cotton into clothing filling skyrocketed the harvesting of cotton.
The original claims of energy efficiency came from mixing up the energy usage of their much smaller model with their big model I think.
A bit flawed. What if the same prompts are used but both models are required to keep their responses equally brief?
So the answer, as always, is ban useless, power-sucking, unreliable, copyright-infringing AI.
Why do I feel that this will never happen? That we will continue to use this horrible technology to the end of our days with people constantly making excuses for its existence? Ugh.
So the answer, as always, is ban useless, power-sucking, unreliable, copyright-infringing AI.
That’s naive. It’s way too late for any of that. If some country decided to ban AI, all the engineers will just move somewhere else.
Quit having residential rate payer subsidize this trash… they want to develop it, fine. Make them pay for it.
We don’t even get any economic benefits from these subsidies.
