ZEDEDA has taken an important step in our edge strategy: we’ve joined Lenovo’s Crosswave OEM Partner Program. This move deepens our collaboration with Lenovo and creates
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
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
For decades, industrial field operations have run on instinct, experience, and scheduled maintenance windows. Equipment runs until it doesn’t. Data gets collected and reviewed hours
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 Technical Deep-Dive into Distributed AI at the Edge Introduction There is a profound architectural parallel hiding in plain sight between two of the most
A technical deep-dive into the ZEDEDA Camera Monitoring Agent for PCB Quality Inspection at the Edge using NVIDIA Jetson Thor Summary Modern electronics manufacturing demands
When I talk to customers about the edge, I always try to get to where the rubber meets the road. Over the last several years,
Why “Edge Intelligence”? When we founded ZEDEDA, our mission was simple: make it effortless to deploy and manage edge infrastructure reliably, securely and at scale, providing
In 2026, AI will increasingly be defined by where it runs. As intelligence is deployed across factories, retail environments, and remote operational sites, the focus