Jeff – Jev-compatible 0.8B decision models, trained at home, ~30 ms

This article describes a new class of tiny, locally trainable decision models that can classify intents, route requests, and score choices in ~30 ms without generating text, which could materially reduce latency and dependency on larger LLMs for high-volume operational workflows. For CIOs and technology leaders, the strategic implication is a shift toward using specialized “System 1” models for fast, calibrated decisions at the edge or on-prem, reserving larger models for harder reasoning tasks and lowering cloud cost, privacy exposure, and integration complexity. It also suggests IT teams may need to rethink application architectures so decisioning can be embedded directly into products, support systems, and automation pipelines with lightweight fine-tuning on enterprise data.