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Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency.

Drawn from what Jeff Dean said

semiconductors The chips and the companies that make them: NVIDIA, TSMC, ASML, and what a fab can and cannot do.

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What Jeff Dean actually said

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  1. Jeff Dean

    Chief Scientist at Google DeepMind and Google Research

    Yeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs

    Watch from 3:36 plays here

    Jeff Dean: The 1% Rule for Building in AIyoutube.com

    video · 57m · spoken · machine transcript

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Added to korrents 30 Jul 2026 · How quotes work · Something wrong? Tell us

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