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this post was submitted on 15 Aug 2025
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I don't care how rough the estimate is, LLMs are using insane amounts of power, and the message I'm getting here is that the newest incarnation uses even more.
BTW a lot of it seems to be just inefficient coding as Deepseek has shown.
Kind of? Inefficient coding is definitely a part of it. But a large part is also just the iterative nature of how these algorithms operate. We might be able to improve that via code optimization a little bit. But without radically changing how these engines operates it won't make a big difference.
The scope of the data being used and trained on is probably a bigger issue. Which is why there's been a push by some to move from LLMs to SLMs. We don't need the model to be cluttered with information on geology, ancient history, cooking, software development, sports trivia, etc if it's only going to be used for looking up stuff on music and musicians.
But either way, there's a big 'diminishing returns' factor to this right now that isn't being appreciated. Typical human nature: give me that tiny boost in performance regardless of the cost, because I don't have to deal with. It's the same short-sighted shit that got us into this looming environmental crisis.
Coordinated SLM governors that can redirect queries to the appropriate SLM seems like a good solution.
That basically just sounds like Mixture of Experts
Basically, but with MCP and SLMs interacting rather than a singular model, with the coordinator model only doing the work to figure out who to field the question to, and then continuously provide context to other SLMs in the case of more complex queries
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And water usage which will also increase as fires increase and people have trouble getting access to clean water
https://techhq.com/news/ai-water-footprint-suggests-that-large-language-models-are-thirsty/
For training yes, but during operation by this studies measure Deepseek actually has an even higher power draw, according to the article. Even models with more efficient programming use insane amounts of electricity
OK I guess I didn't read far enough but your quote says that Deepseek uses less than Open AI?
Less than Open AI's o3, but that's because o3 was estimated to use even more power than GPT 5's 18 Wh per query.
Also don't forget how people like wasting resources by asking questions like "what's the weather today".
My guess would be that using a desktop computer to make the queries and read the results consumes more power than the LLM, at least in the case of quickly answering models.
The expensive part is training a model but usage is most likely not sold at a loss, so it can't use an unreasonable amount of energy.
Instead of this ridiculous energy argument, we should focus on the fact that AI (and other products that money is thrown at) aren't actually that useful but companies control the narrative. AI is particularly successful here with every CEO wanting in on it and people afraid it is so good it will end the world.