Compute scaling fuels the leap from ImageNet to world models

MemCast / episode / insight

#capital-scaling7 #hardware-advances1

The early days of deep learning were defined by moving from CPUs to GPUs, a transition epitomised by AlexNet.
Today a single GPU is roughly a thousand times faster than the GPUs used for AlexNet, allowing far larger models.
This raw compute boost is the primary reason we can now train generative 3‑D world models that were unimaginable in the early 2010s.
Without this scaling, spatial intelligence would remain a research curiosity rather than a deployable product.

Fei-Fei LiLatent Space00:00:00

Supporting quotes

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00:00:00

“I think the whole history of deep learning is in some sense the history of scaling up compute.” — Fei-Fei Li

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00:04:13

“If you think about AlexNet required this jump from CPUs to GPUs but even from AlexNet to today we're getting about a thousand times more performance per card.” — Fei-Fei Li

From this concept

Compute Scaling as the Engine of Progress

The guests argue that every major leap in AI has been driven by orders-of-magnitude increases in compute. The exponential growth in GPU performance and the ability to train on thousands of devices unlocks the data-hungry spatial models that were impossible a decade ago.

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