Edge AI
Running AI models on-device or on local hardware instead of in the cloud.
Edge AI runs inference close to where data is generated — on phones, cameras, industrial PLCs, or dedicated accelerators like NVIDIA Jetson, Google Coral, or Qualcomm's Hexagon NPUs. It cuts latency, bandwidth, and cloud cost, and keeps sensitive data on-premises.
Small, distilled, or quantized models (Llama 3 8B, Phi-3, Gemma, Whisper.cpp) now deliver useful accuracy on hardware that costs a few hundred dollars. This has made offline voice, vision, and text applications commercially viable at scale.
The tradeoff is model capability: edge models will lag frontier cloud models by roughly a generation. Hybrid architectures — edge for latency-sensitive perception, cloud for heavy reasoning — are common.
Key points
- Sub-100ms latency for real-time applications
- Bandwidth and cloud costs collapse for camera and audio workloads
- Data never leaves the device — a compliance win
- Quantization (INT8, INT4) is essential for practical deployment
Common use cases
Frequently asked
Related terms
The field of AI that enables machines to interpret and act on visual information from images and video.
A compact LLM (1B–15B parameters) tuned for cost, latency, and on-device deployment.
Compressing a model by reducing the numerical precision of its weights, cutting memory and speed cost.
Applying Edge AI in your business?
We help enterprises design, ship, and govern AI systems end-to-end.