Ce Zhang, co-founder and CTO of Together AI, responds to questions about whether the AI industry is reaching the physical limits of available training tokens.
Insight
Zhang: ML systems optimization is fundamentally always about data movement
“Fundamentally, the thing we are actually optimizing is actually not that different. It's always about data movement across essentially all the stacks, right? So when you do distributed, like computing, it's about communication across different machines. When y…”
Insight
Zhang: Pre-training data evolved from a model byproduct into a standalone asset
“So, so I think one fundamental thing that changed in the last year, essentially, in the beginning when people think about data, is, is always like a byproduct of the model, right? You release the model, you also release the data, right? The data side is there …”
Insight
Zhang: Targeted task training yields smaller, cheaper, and more accurate models
“The benefit you can get out of that is you could build a, Better open model, often smaller, often easier to do inference if you know what you want, right? So I think the whole trade-off would be, and the x-axis would be how generic the hosting will be. The y-a…”
Insight
Zhang: Next 10x AI inference gain requires multi-layer co-optimization
“If you only push on one direction, you are going to reach diminution return really, really quickly. Yeah, there's only that much you can do on the system side, only that much you can do on the algorithm side. And since the only big thing that's going to happen…”
Insight
Zhang: Good benchmarks must anticipate how developers over-optimize toward metrics
“A good benchmark should think about how it's going to incentivize the field to actually move forward, right? So the benchmark will become kind of standard. How are people going to over optimize to the benchmark because people are going to do that? And when peo…”
Prediction Not checkable as stated
Zhang predicts much faster, diverse new embedding models within couple years
“So I think for the next couple years, yeah, we will see a whole bunch of new embeddings maybe of different sites, and much, much faster than today.”