TinyDecide: A 10M Parameter Decision Model for Microcontrollers and Edge Devices
Developer /u/TheRealREZOR has introduced TinyDecide, an ultra-compact 10-million-parameter decision model that requires only 6MB of storage. Smaller than any model currently listed on the Decision Index leaderboard, it runs efficiently across environments ranging from web browsers and Rust applications to microcontrollers like the ESP32. TinyDecide brings structured decision-making capabilities to extremely hardware-constrained devices, advancing the fields of TinyML and edge AI deployment. It demonstrates that ultra-lightweight, specialized models can execute fast local decision logic on low-power IoT hardware without relying on cloud infrastructure. The model adopts a Jev-style architecture aimed at returning structured decisions rather than generative text, packaged into just 6MB of memory. It supports execution across Python, Node.js, Rust, web browsers, and embedded platforms such as the ESP32 chip.
## BACKGROUND
Unlike general large language models built for open-ended text generation, Jev-style models serve as fast, deterministic engines tailored specifically to analyze input data and output clean, structured decisions. The Decision Index is a benchmark leaderboard that evaluates open-weight decision models across tens of thousands of requests spanning tasks like automation, retrieval, and classification.