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Post by Sebastian Raschka on X

Sebastian Raschka@rasbt

An updated back-of-the-envelope calculation of LLM pretraining costs based on the just-released DeepSeek-v3 report. And that doesn't even account for hyperparameter tuning, failed runs, or personnel costs. It really makes me appreciate the value of openly shared model weights!

317 likes6 repliesPosted Dec 26, 2024

View the original on X Written by Sebastian Raschka. 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.

From likes, comments.

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: personal, energetic
Written for: People building with AI

Analysis by ViralLoop — not written by the original author.

Topics

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More from Sebastian Raschka

Sebastian Raschka

Interesting surprise drop from Thinky! The Inkling model looks pretty solid on benchmarks, and it has some little surprises in its architecture: - Small conv layers in several places - An RMSNorm for the embeddings (before the block RMSNorm) - Rel. position bias instead of RoPE

Sebastian Raschka

Ok, so what Ilya saw was extreme benchmaxxing, which in turn prompted him to create his own company to do LLM development the proper way?! Makes sense, I sympathize with that.

Sebastian Raschka on AI · ViralLoop