4 ms·
I’ll flip this around a bit: If I’ve raised $1B to buy GPUs and train a “bigger model”, a major part of my competitive advantage is having $1B to spend on suff
by svnt 2y ago
I’ll flip this around a bit:
If I’ve raised $1B to buy GPUs and train a “bigger model”, a major part of my competitive advantage is having $1B to spend on sufficient GPUs to train a bigger model.
If, after having raised that money it becomes apparent that consumer hardware can run smaller models that are optimized and perform as well without all that money going into training them, how am I going to pivot my business to something that works, given these smaller models are released this way on purpose to undermine my efforts?
It seems there are two major possibilities: one,
people raising billions find a new and expensive intelligence step function that at least time-locally separates them from the pack, or two (and significantly more likely in my view) they don’t, and the improvements come from layering on different systems such as do not require acres of GPUs, while the “more data more GPUs” crowd is found to have hit a nonlinearity that in practical terms means they are generations of technology away from the next tier.
- rvnx 2y agoMining cryptos, some "AI" companies already do that (knowingly or not... and not necessarily telling investors)
- svnt 2y agoIs it still even worth the electricity to do this on a GPU? It wouldn’t surprise me if some startups were renting them out, but is anyone still mining any volume of crypto on GPUs? edit: I guess to your point if it is not knowingly then the electricity costs are not a factor either.
- ComputerGuru 2y ago> Is it still even worth the electricity to do this on a GPU? Only with memcoins.
- jstummbillig 2y agoWhat you suggest is not impossible but simply flies in the face of all currently available evidence and what all leading labs say and do. We know they are actively looking for ways to do things more efficiently. OpenAI alone did a couple of releases to that effect. Because of how easy it is to switch providers, if only one lab found a way to run a small model that competed with the big ones, it would simply win the entire space, so everyone has to be looking for that (and clearly they are, given that all of them do have smaller versions of their models) Scepticism is fine, if it's plausible. If not it's conspiratorial.
- svnt 2y agoThere are at least two different optimizations happening: 1) optimizing the model training 2) optimizing the model operation The $1B-spend holy grail is that it costs a lot of money to train, and almost nothing to operate, a proprietary model that benchmarks and chats better than anyone else’s. OpenAI’s optimizations fall into the latter category. The risk to the business model is in the former — if someone can train a world-beating model without lots of money, it’s a tough day for the big players.
- ComputerGuru 2y agoI disagree. Not axiomatically because you’re kind of right, but enough to comment. OpenAI doesn’t believe in optimizing the traisning costs of AI but believes in optimizing (read: maxing) the training period. Their billions go to collecting, collating, and transforming as much training data as they can get their hands on. To see what optimizing model operation looks like, groq is a good example. OpenAI isn’t (yet) obviously in that kind of optimization, though I’m sure they’re working on it internally.
- svnt 2y agoMy argument wasn’t that the well-funded entities were optimizing to reduce training costs, but the opposite: they need creative ways to spend $1B that provide some tangible advantage. But they need operating costs to be low or they lose money and try to somehow make it up on volume. I would roll data acquisition/cleaning processes into training costs for purposes of this because what else is the data for if not training? If 4o wasn’t an optimization for model operation costs what was it?