Edge deployment of AI agents has been a theoretical possibility for a while. With the Raspberry Pi 5's improved CPU and 8GB RAM, combined with 4-bit quantized models, it's now practical for specific use cases.
Setup
We deployed an OpenClaw agent on a Raspberry Pi 5 using llama.cpp as the inference backend with a quantized 7B model. The agent handles home automation tasks: monitoring sensors, triggering actions, and responding to natural language commands.
Performance
Token generation runs at 8 tokens/second — slow for interactive use but adequate for automation tasks where response time of 5-10 seconds is acceptable. Tool execution (GPIO control, HTTP requests) adds negligible overhead.
Power and Cost
The entire setup draws 12W under load, costing approximately $10/year in electricity. Compare this to cloud API costs of $50-200/month for equivalent task volumes. For always-on automation agents, edge deployment pays for itself within weeks.
