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Ask HN: How expensive are LLMs to query, really?

I'm starting to see things pop-up from well-meaning people worried about the environmental cost of large language models. Just yesterday I saw a meme on social media that suggested that "ChatGPT uses 1-3 bottles of water for cooling for every query you put into it."

This seems unlikely to me, but what is the truth?

I understand that _training_ an LLM is very very expensive. (Although so is spinning up a fab for a new CPU.) But it seems to me the incremental costs to query a model should be relatively low.

I'd love to see your back-of-the-envelope calculations for how much water and especially how much electricity it takes to "answer a single query" from, say, ChatGPT, Claude-3.7-Sonnet or Gemini Flash. Bonus points if you compare it to watching five minutes of a YouTube video or doing a Google search.

Links to sources would also be appreciated.

( 3 comments | 4 points | on HN )
Re:Ask HN: How expensive are LLMs to query, really? by teach on Friday May 09, 2025 @04:12PM (#43940477)
These are excellent, thank you!
Re:Ask HN: How expensive are LLMs to query, really? by a_conservative on Friday May 09, 2025 @05:21PM (#43940982)
my m4max macbook can run local inference on a medium-ish gemini model (32b IIRC). The power consumption spikes by about 120 watts over idle (with multiple electron apps, docker, etc). It runs about 70 tokens/sec and usually responds within 10 to 20 seconds.

So.. picking some numbers for calculation. 4 answers per minute @ 120 watts is about .5 watt-hours per answer. ~200 responses would be enough to drain the (normally quite long lasting battery).

How does that compare to the more common nvidia GPUs? I don't know.