| ▲ | nhecker 2 hours ago | ||||||||||||||||||||||
In a similar vein: seek efficiency. I.e., /if/ I am going to consume LLM tokens, I figure that a local LLM with 10s of billions of parameters running on commodity hardware at home will still consume far more energy per token than that of a frontier model running on commercial hardware which is very strongly incentivized to be as efficient as possible. Do the math; it isn't even close. (Maybe it'd be closer in your local winter, where your compute heat could offset your heating requirements. But that gets harder to quantify.) Maybe it's different if you have insane and modern local hardware, but at least in my situation that is not the case. | |||||||||||||||||||||||
| ▲ | zozbot234 2 hours ago | parent [-] | ||||||||||||||||||||||
But commodity hardware that's right-sized for your own private needs is many orders of magnitude cheaper than datacenter hardware that's intended to serve millions of users simultaneously while consuming gigawatts in power. You're mostly paying for that hardware when you buy LLM tokens, not just for power efficiency. And your own hardware stays available for non-AI related needs, while paying for these tokens would require you to address these needs separately in some way. | |||||||||||||||||||||||
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