But if you're running it on your own hardware you might only generate tokens when you have something useful to do with them, instead of every time you load a Google search results page because Google decided the future is stuffing Gemini-generated answers down your eyeballs instead of letting you read it yourself from the primary source for 0.1 watts.

If LLM's were a mature product then this would be true at some point. However, you could argue (and I will) that the popularization of on-device LLM inference will lead to two things:

- Consumers of LLM inference (developers and hobbyists) will be more aware of compute cost, leading them to develop more token-efficient uses of LLM inference and be incentivized to pick the right model for the right job (instead of throwing Sonnet at the wall and follow up with Opus if that doesn't stick)

- A larger market for on-device (and therefore open weight) LLM's will probably result in more research concentrated on those inherently more efficient (because compute/memory-constrained) models.

I think that despite the inefficiencies, shifting the market towards local inference would be a net positive in terms of energy use. Remember that 50W might seem like a lot, but is still much less than what, let's say, a PS5 draws.

Also remember how AWS had the same promise and now we're just deploying stack after stack and need 'FinOps' teams to get us to be more resource-efficient?

Separate to the self-host/datacentre argument, it would be interesting to see a speed/performance/watts-per-token leaderboard between leading models. Which model is the most watt-efficient?

This is neither a controversial take nor a reason to prefer third-party hosting over self-hosting, so I don't think the internet really needs to be ready for it.

I thought this is a pretty generally accepted fact?

This is pretty much true for all applications.

There's a bunch of companies doing garage GPU datacenters now. Probably can act as a heat source during winter too if you have a heat pump.

Using only this dimension in a vacuum, it sounds like an easy choice, but we're extremely early in this market, and the big providers are already a mess of pricing choices, pricing changes, and sudden quota adjustments for consumers.

Plus, a Mac that's not running inference idles down to 1-5W, only drawing power when it needs to. Datacenters must maximize usage, individuals and their devices don't have to.

A Mac is also the rest of the personal computer!