Abstract

Recent advancements in large language models (LLMs) have enabled reasoning-like behavior and sophisticated AI agents, but their deployment usually depends on cloud infrastructure or high-performance hardware. Existing local implementations rarely target energy-efficient edge scenarios. This work presents a conversational AI thermostat assistant running fully on a Raspberry Pi 5, a low-cost and moderately constrained device. The system combines Moonshine for speech-to-text, Granite 3.3 (8B) for natural language reasoning, and Piper for speech synthesis, enabling cloud-free voice interaction. Experiments show low latency for speech modules, while LLM inference remains the main bottleneck. The results highlight both the feasibility and current limitations of agentic AI on constrained hardware, stressing the need for further research to achieve practical real-time edge deployment.

Firpo, P., Berta, R., Bellotti, F., Pau, D. (2026). Turning a Thermostat into an Agent at the Edge. In: Ruo Roch, M., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2025. Lecture Notes in Electrical Engineering, vol 1553. Springer, Cham. https://doi.org/10.1007/978-3-032-17174-0_26