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Post by Dylan Patel on X

Dylan Patel@dylan522p

OpenAI hasn’t even deployed TPUs yet and they’ve already saved ~30% on their entire lab wide NVIDIA fleet. This demonstrates how the perf per TCO advantage of TPUs is so strong that you already get the gains from adopting TPUs even before turning one on. The piece covers a lot

1.2K likes60 repliesPosted Nov 28, 2025

View the original on X Written by Dylan Patel. Captured Sep 4, 2026.

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Based on absolute engagement — the platform doesn’t expose follower counts here, so large accounts score higher.

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Why it worked

Ends on a short line that is easy to quote and repeat.
Short enough to read in full without expanding, which removes a step before the point lands.

The mechanics

Hook: Plain observation
Structure: Hook → Landing
Style: Conversational paragraphs at a steady pace.
Tone: plain, matter-of-fact
Written for: People building with AI

Analysis by ViralLoop — not written by the original author.

Topics

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More from Dylan Patel

OpenAI Jalapeno is spicy Usually first generation chips aren't competitive, but OpenAI is beating Nvidia Blackwell and even Rubin This is huge news! We got to go into OpenAI's lab to dissect their new chip and dive into the software, architecture, and performance Incredible work!

This is a public cry to not waste the silicon and have better AI infra Rivos, DSF, Grand Teton, Ariel and the cut-down MI450X were defensible on some narrow metric that a group inside Meta Infrastructure was optimizing for, but each was a poor decision for the company as a whole.

OpenAI now just needs to sandbag the next model release So the US doesn't export control them So they gain massive market share It's imperative that all the OAI employees don't vauge hype post their next model release as the greatest thing ever Don't know if they are capable tho

Debunking the false narratives around AI Datacenters. First it was that water usage is high, but it's not. This time it's about power prices. The narrative around powe pricing is silly and politically charged with no actually backing Manipulated statistics by people with agendas

Open models show 2.5x faster, 6x more expensive Lower batch size, speculative decoding harder Pareto optimal curve for Deepseek at t.co shows this Claude Opus 4.6 is 100 Tok/s/user Deepseek at 100 is 6k Tok/s/GPU At 250 tok/s/user it's closer to 1k

4% of GitHub public commits are being authored by Claude Code right now. At the current trajectory, we believe that Claude Code will be 20%+ of all daily commits by the end of 2026. While you blinked, AI consumed all of software development. Read more 👇 t.co

Dylan Patel on AI · ViralLoop