this post was submitted on 01 Jul 2025
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I do, because they're not at full load the entire time it's in use
They are, it'd be uneconomical not to use them fully the whole time. Look up how batching works.
I mean I literally run a local LLM, while the model sits in memory it's really not using up a crazy amount of resources, I should hook up something to actually measure exactly how much it's pulling vs just looking at htop/atop and guesstimating based on load TBF.
Vs when I play a game and the fans start blaring and it heats up and you can clearly see the usage increasing across various metrics
He isn't talking about locally, he is talking about what it takes for the AI providers to provide the AI.
To say "it takes more energy during training" entirely depends on the load put on the inference servers, and the size of the inference server farm.
There's no functional difference aside from usage and scale, which is my point.
I find it interesting that the only actual energy calculations I see from researchers is the training and the things going along with the training, rather then the usage per actual request after training.
People then conflate training energy costs to normal usage cost without data to back it up. I don't have the data either but I do have what I can do/see on my side.
I'm not sure that's true, if you look up things like "tokens per kwh" or "tokens per second per watt" you'll get results of people measuring their power usage while running specific models in specific hardware. This is mainly for consumer hardware since it's people looking to run their own AI servers who are posting about it, but it sets an upper bound.
The AI providers are right lipped about how much energy they use for inference and how many tokens they complete per hour.
You can also infer a bit by doing things like looking up the power usage of a 4090, and then looking at the tokens per second perf someone is getting from a particular model on a 4090 (people love posting their token per second performance every time a new model comes out), and extrapolate that.