Devices powered by Ambarella silicon can now be deployed, updated and monitored across an entire estate from a single place, turning on site video intelligence
Industrial AI is becoming one of the most valuable competitive assets in modern business. Across energy, manufacturing, logistics, retail, and critical infrastructure, enterprises are investing
Photo by Simon Kadula on Unsplash The single-model deployment era of edge AI is ending. The factories, warehouses, and facilities of tomorrow are being built on collaborative
Six iterations of KV-cache compression: from a naive PyTorch baseline to fused vectorized Triton kernels running real LLM inference on edge hardware. The Problem A
As enterprises push AI beyond the data center, the gap between a successful pilot and a production deployment at scale has become the defining challenge
Getting AI models into production at the edge is hard. Building them, packaging them, and deploying them reliably across hundreds of distributed industrial sites, while
Cloud computing, with its virtually unlimited set of resources, leads people to expect a lot from AI. In a big data center, the most important
A panel of edge AI experts join ZEDEDA’s Chief Product Officer Padraig Stapleton to discuss the state of edge AI – from early computer vision
PV Hardware’s AI-powered solar tracking software, DeepTrack, helps maximize energy production across large-scale solar farms worldwide. By partnering with ZEDEDA, PVH was able to implement
Despite record global AI spending of $2.5 trillion in 2026, most edge AI projects stall in the pilot phase. Organizations frequently struggle to bridge the