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Comment by anjel | Hacker News Reader

I've seen a regular stream of reports on HN about people "sort of" getting AI done on laptops and non and lowly GPU machines. Is it unreasonable or far-fetched to imagine that someone figures out how to efficiently get it all done without GPUs and pull the rug out from under Nvidia?

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Just like existence of MariaDB does not prevent Snowflake from being worth $50B, just being good enough on laptop is not enough to replace the need for the cutting edge.

If this happens we will just get more things done with the same amount of compute (see: Blinn's law). The demand for GPUs does not really come from algorithmic compute requirements but from social expectation of progress in the field of AI. People will use all the compute they can get doing research using the budget they are given. What matters is how this budget is set.

Training is very expensive and requires GPUs. What you read about is running trained model on consumer devices (even phones!).

Let's assume for a moment you could sort of trade parallel computation for vast space, fast search and retrieval. So in this hypotheticals computational theory, you could build a lookup machine from CPUs and ssds.. squeezing the parallel cores into one CPU, by squeezing the shaders running into a million hashes.. And before you know it your simulating a micro verse trying desperately to find out how to avoid climate change. What if God hates recursion?

The current giants have shown that "it" can be done. From now on we can reasonnably hope for massive progress in efficency at the low end - as well as massive capability improvements at the high end. That goes both ways, probably.

I have an options strategy that is riding on this possibility right now.

All you have to do is take 5 seconds in a typical code base to determine that the way we write software today isn't exactly... ideal. Given another 6-12 months, I cannot comprehend another ~OOM not being extracted somewhere simply by making the software better.

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