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2 days agoVery 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.
If you want me to be your personal search engine you’ll need to wait a bit, im making dinner right now and would rather look for the articles on my desktop.
Not talking about caching (though there would be some decent memory savings due to that on general platforms like ChatGPT and tools like Codex). I am talking about large batch sizes, which are concurrent requests all accessing the same memory at the same time. The model is loaded once onto the GPU(s) and then many simultaneous requests can read that memory at the same time. When those requests are all processing their responses simultaneously, the energy per token drops off a cliff.
And yes, running a smaller model would generally take less power, but thats not really a fair comparison. Small models just wont give you the same results as larger ones. You need to compare it apples to apples. If you want to compare your local Qwen model running on your laptop, you compare those numbers to larger systems supplying that same qwen model to thousands of people. Just because we are comparing cloud services to local doesn’t automatically mean GPT 5.6 vs Qwen 3.6 27B. There are plenty of cloud AI providers running all sorts of models and sizes.
As for one of the articles I learned alot of this from originally, this is one I recommend going through. It really goes deep into the whole topic: https://arxiv.org/html/2601.22076v1