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AI silicon has advanced faster than the tools to manage it in the field. Enterprises can build powerful edge AI hardware, but deploying, updating, and securing models across fleets of devices remains largely manual, slow, and hard to secure. Edge intelligence provider ZEDEDA and Ambarella, a developer of edge AI semiconductor solutions, are looking to address this shortcoming via a strategic partnership intended to bring secure, cloud-orchestrated AI to the devices where the physical world meets AI: cameras, robots, vehicles, industrial systems, and edge appliances.
Through this collaboration, ZEDEDA's open-source EVE-OS, governed by the Linux Foundation, now runs on Ambarella's N1 family of edge generative AI SoCs, and ZEDEDA's Edge Intelligence Platform can deploy, update, and operate AI models directly on Ambarella's power-efficient AI acceleration hardware. Enterprises and OEMs can now orchestrate vision models, LLMs, and multimodal AI workloads on Ambarella-based systems at any scale, from a single control plane, with zero-touch security built in.
The partnership unites two complementary solutions: Ambarella's SoCs power AI perception across security cameras, robotics, automotive, and industrial systems, with more than 50 million AI chips shipped cumulatively, while ZEDEDA's platform operates tens of thousands of edge nodes in the field. Together, the companies are looking to remove the biggest barrier to physical AI adoption: getting models onto distributed devices, keeping them current, and keeping them secure, without sending trucks or exposing infrastructure.
This announcement extends the momentum both companies have built in 2026:
In March, ZEDEDA unveiled its Edge Intelligence Platform, which is used to create, secure, and operate edge and physical AI at scale, alongside Edge Intelligence Labs and Edge Intelligence Appliances.
In January, Ambarella launched its CV7 edge AI vision SoC at CES 2026, delivering more than 2.5x the AI performance of the prior generation on 4nm process technology, and opened its Developer Zone to broaden the Ambarella edge AI ecosystem.
This partnership connects those threads: Ambarella's newest silicon and developer ecosystem, now operable at fleet scale through ZEDEDA’s Edge Intelligence Platform and edge AI ecosystem.
What the partnership delivers:
EVE-OS Validated on Ambarella N1: ZEDEDA's open-source, Linux Foundation LF Edge-based edge operating system runs on the Ambarella N1-655, providing a secure, vendor-neutral foundation for containerized, virtualized, and Kubernetes workloads on-device.
Model Deployment from the ZEDEDA Model Hub: Ambarella-optimized AI models are deployed to Ambarella-based systems directly from ZEDEDA's Edge Intelligence Platform, with verified inference on Ambarella's AI acceleration engine and full observability, monitoring, and lifecycle management for ongoing updates and optimization in the field.
A Full-Stack Developer Path: Ambarella's edge AI platform combines proprietary AI acceleration and computer vision with its Cooper development environment: 12 edge AI SoC families supporting more than 200 AI model architectures in customer production and now orchestrated at fleet scale by ZEDEDA.
Developer Kits, Ready Out of the Box: Development kits featuring Ambarella technology with EVE-OS preinstalled are expected to become available through Ambarella's DevZone, so developers and system integrators can go from unboxing to orchestrated Physical AI deployment in minutes.
An Open Ecosystem Path: The companies are working with ISVs, system integrators, and hardware partners to deliver full-stack edge AI solutions, and will explore deeper participation in the LF Edge ecosystem, of which ZEDEDA is a founding member.
To address this market, the companies have defined a five-phase strategic roadmap, and—contingent on customer adoption and on the companies electing to proceed—the roadmap includes deployments reaching hundreds of thousands of managed edge nodes across more than one hundred enterprises, with associated Ambarella revenue that could exceed hundreds of millions of dollars over a five- to seven-year period. These figures represent agreed-upon planning objectives rather than minimum revenue commitments, purchase obligations, or guaranteed forecasts.
The collaboration initially focuses on use cases where AI must operate locally with low latency, high reliability, and constrained power budgets, including robotics and autonomous machines, industrial automation and machine vision, intelligent cameras and smart infrastructure, automotive and mobility, retail and logistics, and connected industrial equipment.
Learn more about the market for digital transformation at the industrial edge.