TurboQuant RaBitQ: How Big Labs Rebrand Iteration

Google writes a paper about speeding up AI models, the press calls it a breakthrough, and then a RaBitQ author shows up on Reddit with a long, polite post explaining that TurboQuant RaBitQ comparisons quietly airbrushed their work into the appendix and put their method on a single‑core CPU while Google’s ran on a GPU. TL;DR The TurboQuant paper almost certainly under‑credits RaBitQ and earlier PTQ methods like QuIP and QTIP, then amplifies its own gains with lopsided baselines. This isn’t just one bad paper; it’s a playbook: move prior art to the appendix, choose flattering benchmarks, and let prestige plus PR convert incremental engineering into a “new” idea. If you read ML papers (or the coverage about them), you need to treat claims like TurboQuant’s as marketing copy unless you’ve checked three things: where the citations live, how the baselines are configured, and what the appendices quietly admit. TurboQuant vs RaBitQ: The Short Case The facts you actually need fit in one paragra