Name: MLn Club (ML Reading Group) #4: Touching The Elephant - TPUs
Start: 2026-06-08T19:00:00.000-04:00
End: 2026-06-08T21:00:00.000-04:00
Location: Pittsburgh, PA

We meet weekly on Sundays in person to talk about ML papers! Optional quiet reading from 7 pm, discussion starts 8 pm. Come and join the discussion! Open to all. Website: https://czhs.github.io/mln/

MLn Reading Club

What if the real moat in AI isn't the model, but the chip?

Should AI run on custom silicon or general-purpose chips?

This article is a deep dive into the history and architecture of Google's Tensor Processing Units (TPUs) — custom silicon that now powers the world's most advanced AI models. Reed Oliver traces how Google moved away from general-purpose chips to build dedicated, "monastic" hardware optimised for one thing: matrix multiplication at scale.

The piece explores why Google made the bet on custom hardware when GPUs were already dominant, what makes TPUs architecturally different, and how that decision reshaped the broader AI industry — from training costs to the competitive landscape.

discussion at 8 pm, (optional) quiet reading from 7 pm.

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MLn Club (ML Reading Group) #4: Touching The Elephant - TPUs

Siddharth Radhakrishnan