3 ms·
It's crazy to see a 400B model running on an iPhone. But moving forward, as the information density and architectural efficiency of smaller models continue to i
by simopa 7mo ago
It's crazy to see a 400B model running on an iPhone. But moving forward, as the information density and architectural efficiency of smaller models continue to increase, getting high-quality, real-time inference on mobile is going to become trivial.
- volemo 6mo ago> moving forward, as the information density and architectural efficiency of smaller models continue to increase If they continue to increase.
- vessenes 6mo agoThey will. Either new architectures will come out that give us greater efficiency, or we will hit a point where the main thing we can do is shove more training time onto these weights to get more per byte. Similar thing is already happening organically when it comes to efficient token use; see for instance https://github.com/qlabs-eng/slowrun https://github.com/qlabs-eng/slowrun.
- simopa 6mo agoThanks for the link.
- simopa 6mo agoThe "if" is fair. But when scaling hits diminishing returns, the field is forced to look at architectures with better capacity-per-parameter tradeoffs. It's happened before, maybe it'll happen again now.
- anemll 6mo agoProbably 2x speed for Mac Studio this year if they do double NAND ( or quad?)