https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini
Discussion
Hardware and energy to run it
🚀
Time will tell whether closed-source, high parameter "all-in-one" models win, or if smaller open-source models that are focused in more specific areas (i.e. coding, chat, reasoning, etc) can remain competitive.
I'm certainly rooting for the "GPU poor".
Fascinating. I'm so out of my depth. How does this article square with the one a few months back (with one of those two authors) ? : https://www.semianalysis.com/p/google-we-have-no-moat-and-neither
"Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI"
"While our models still hold a slight edge in terms of quality, the gap is closing astonishingly quickly. Open-source models are faster, more customizable, more private, and pound-for-pound more capable. They are doing things with $100 and 13B params that we struggle with at $10M and 540B. And they are doing so in weeks, not months. This has profound implications for us:We have no secret sauce. Our
best hope is to learn from and collaborate with what others are doing
outside Google. We should prioritize enabling 3P integrations.People will not pay for a restricted model when free, unrestricted alternatives are comparable in quality. We should consider where our value add really is.Giant models are slowing us down. In the long run, the best models are the ones which
can be iterated upon quickly. We should make small variants more than
an afterthought, now that we know what is possible in the <20B
parameter regime."
Google goes public with compute power or they don’t have a business model. Only question is, can open source compete and for how long?
Very interesting, thanks for sharing.
I find it odd that Apple isn’t even mentioned. Don’t they excerpt control over a lot if GPUs too?
Wish people would focus more on what kind of data AI is being trained on and who makes those training decisions