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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

by anjel 3y ago

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?

SpacePortKnight 3y ago
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.
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lsh123 3y ago
Training is very expensive and requires GPUs. What you read about is running trained model on consumer devices (even phones!).
FreshStart 3y ago
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?
bob1029 3y ago
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.
creer 3y ago
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.
saiojd 3y ago
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.

by anjel 3y ago

SpacePortKnight 3y ago

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.

deleted 3y ago
[deleted]

lsh123 3y ago

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

FreshStart 3y ago

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?

bob1029 3y ago

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.

creer 3y ago

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.

saiojd 3y ago

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.