logancbrown
Joined 2 years ago
Joined 2 years ago
The entire reason SETI@Home started was because people had unused hardware sitting idle at home. Every company I've ever been in has had slides on resource utilization for procured hardware.
In fact not only could the two statements be true, in fact both are true. People want to ignore otherwise because doing so will make local LLM inference sound like the only option when only one is considered.
Must why people in this HN thread are throwing out so many ad-hominems instead.
1. Local inference can cause a large under-utilization of resources from users that do not run local inference hardware at capacity over the hardwares lifespan. This is under-utilization and under-utilization is not optimal from an economics perspective.
2. The concentration of inference hardware to singular providers can aggregate power to single systems or organizations, and their incentives may be at odds with individual users.
The same power dynamic and economics struggle has existed nearly everywhere for all of history. For example, if you want to decide to own or rent a piece of heavy construction machinery for business, you will find a long list of do-not's on the rental contractual terms, but you are more than welcome to buy your own and operate your own machinery at several times the price for the hours used and use it however you want.
It effectively already is. It may end up in the same domain as Haiku OS, Plant 9, or Squeak, but they are all still interesting domains of research.
Sure, you could train a XGboost model on a time series with holiday or event covariates, but what if you have 10k different series to forecast for? You could train 10k different models, one for each series, but then they have very little history to learn from. Alternatively, you could train a single generic model to cover all 10k, but then you have to deal with scale mismatch between series in addition to the feature dev overhead between series. A TSFM saves a ton of work in modeling each of these series and could potentially generalize better to produce more accurate forecasts.