Technology

You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.

I am absolutely not burning more energy than a frontier model by doing things like putting my laptop to sleep or shutting down unused services when I want to conserve battery power.

Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion.

True.

Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal.

That's actually not true. In fact it's much the opposite. Frontier models churn through tokens at a much higher rate, because of their higher complexity and higher number of parameters. Research is still new on this, but having a frontier model analyze your code files versus a small, local model for the same task seems to be enormously wasteful. If you must use a frontier model for something, have it do that work after receiving the output from an agent using a small model to read and summarize your code.

Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?

...Almost never? I'm not a fan of letting AI do much of ANY of my coding, because it will inevitably bloat my codebase with garbage regardless of which model I use. So I severely restrict my model usage to simple, clearly-defined, narrow-scoped tasks that can save me a bit of time, and that's it. With guardrails and discipline like that, I barely ever have the need to re-prompt.

Very serious. Your personal amount of usage means nothing at all in this conversation. It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.

Hold up. Are you talking about caching? Because if you are... yeah. That has nothing to do with the model and everything to do with the service layer around the model. The same service layers can be - and have been - implemented in tools like Lemonade Server, llama.cpp, Ollama, etc.

And I really do want to know your sources.

Mine say GPT 5.5 is probably using quite a lot more than 0.34 Wh per query (0.34 Wh is what Sam Altman claimed for the then-current version of GPT in June of 2025, but he hasn't released numbers since then and no one has done an independent analysis). With Claude, an independent estimate from last year pegged Sonnet at 0.8 Wh for a short prompt, 2.8 Wh for a medium one, and 5.5 Wh for a long one. Current numbers are, again, almost certainly much higher. And just for fun, there's DeepSeek (which I've never used and never would use), with the reasoning-tuned DeepSeek-R1 hitting a whopping 29 Wh for a complex query.

Meanwhile, small, open models are probably in the 0.07 - 0.2 range, depending on the model, the hardware it's running on, and the nature of the query. Of course, there are much weightier open models too, with ones like Llama 3.1 405B using about 9 Wh for a medium-length prompt. On the other hand... who is going to run that on their local machine?

Look... If I'm wrong, and using local models the way I do - sparingly and infrequently - really does consume more electricity than using Claude Code, I want to know. I have no problem whatsoever with eschewing AI models entirely, since I despise all of them. But given how tight-lipped OpenAI and Anthropic are about energy consumption per average prompt, and what independent analyses have estimated, I am highly skeptical that they are acting as some sort of paragons of environmental stewardship.