Edge Inference
Running AI models directly on local devices (phones, laptops, IoT hardware, on-premise servers) rather than sending requests to cloud-hosted APIs. Edge inference eliminates network latency, works offline, and keeps sensitive data on-device—critical for healthcare agents handling patient data, voice agents needing sub-100ms responses, and manufacturing agents operating in facilities without reliable internet. Trade-offs include limited model size (smaller models only) and higher hardware requirements.
Written by Max Zeshut
Founder at Agentmelt · Last updated Oct 26, 2026
Example
A voice agent in a dental office runs speech-to-text and response generation on a local GPU server. Patient conversations never leave the building, latency is under 200ms, and the agent works even when the internet goes down.
Frequently asked questions
- Which models can run at the edge?
- Models up to ~13B parameters run well on consumer GPUs and Apple Silicon (M-series Macs). Quantized models (4-bit, 8-bit) extend this range. For phones and embedded devices, models under 3B parameters are practical. The trade-off is capability: edge models are less capable than cloud models like GPT-4 or Claude, but for focused tasks (classification, extraction, short generation) they perform well enough.