Why did GPUs become the main chip for machine learning, according to David Patterson?

David Patterson says GPUs offered far better single-precision floating-point performance per dollar than CPUs, at prices in the hundreds of dollars. Researchers first disguised their problems as image generation to use them, then CUDA made them programmable. The 2012 AlexNet entry, trained on a GPU by a University of Toronto researcher, crushed an image-recognition competition, and within a few years the field had switched to neural networks running on GPUs.

• AlexNet was the only neural-network entry in that 2012 competition, by Patterson's account.

Source: our own transcription of the episode. The full conversation is on Ryan Peterman (2026-11-04). Read the whole episode summary →

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