Chad Wahlquist(@chadwahl):Counterexample from @nvidia's own supply chain: They post-trained the smallest model, Nemotron 3.5 Lightning, and it outperformed Nemotron 3 Ultra on predictive correctness “by a large gap.” Retraining on 1 GPU, a couple of hours. Nemo + Palantir Ontology + Autopilot, running on-prem because the data is sensitive. A governed learning loop beats “just wait for the next general model” when the work is operational, private, and continuous.

Chad Wahlquist

@chadwahl

Forward Deployed Architect - @PalantirTech

가입 November 2008

864 팔로잉 중 27.8K 팬

Chad Wahlquist@chadwahl

2026.09.29 15:13

Counterexample from @nvidia's own supply chain: They post-trained the smallest model, Nemotron 3.5 Lightning, and it outperformed Nemotron 3 Ultra on predictive correctness “by a large gap.” Retraining on 1 GPU, a couple of hours. Nemo + Palantir Ontology + Autopilot, running on-prem because the data is sensitive. A governed learning loop beats “just wait for the next general model” when the work is operational, private, and continuous.