Practical AI AI at the Edge is a different operating environment
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Mar 25, 2026 
Brandon Shibley, Edge AI solutions engineering lead at Edge Impulse (Qualcomm), helps deploy and optimize ML on constrained devices. He explains what counts as edge in 2026. He contrasts tiny specialized models with large cloud LLMs and shows how cascades save power. He covers real-world constraints like latency, power, privacy, and how tooling and hardware advances make practical edge AI possible.
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Practical Definition Of The Edge
- Edge means anything outside the cloud and is defined by proximity to sensors and the real world rather than a single distance metric.
- Brandon Shibley contrasts far/near edge terms but emphasizes the practical definition: compute not in a data center, often embedded close to sensors.
Small Models And Cascades Are Edge-Friendly
- Large models live in data centers while smaller LLMs and specialized models are becoming practical at the edge.
- Brandon notes single-digit to tens-of-billions-parameter models and specialized fine-tuning or cascades make edge LLMs useful.
Edge Constraints Drive Different Tradeoffs
- Edge constraints shape design: size, power, connectivity, cost, latency, reliability, and privacy dominate decisions.
- Brandon frames privacy as both a challenge and opportunity because edge can keep sensitive sensor data local.