Zug, Switzerland— 14.09.2026— Klepsydra is pleased to announce the launch of a new Edge AI demo in the Torizon Demo Gallery, marking the first-ever Edge AI demo available on the platform.
The new demo provides developers and technology teams with an accessible way to explore Edge AI applications using ONNX models, the de facto standard for deep learning model interoperability. By supporting ONNX, the demo enables users to work with models from a broad range of deep learning frameworks and explore how AI inference can be deployed in embedded and edge environments.
The demo runs on Toradex Apalis and Aquila boards, demonstrating how modern AI workloads can be brought closer to where data is generated. This approach can help enable responsive, efficient and locally executed AI applications without relying exclusively on cloud-based processing.
Making Edge AI Easier to Explore
The Torizon Demo Gallery provides users with instant access to a range of applications designed to demonstrate the capabilities of the Torizon platform. With the addition of Klepsydra’s Edge AI demo, developers can now experience an AI workload directly on supported embedded hardware and explore the possibilities of deploying deep learning models at the edge.
“We are excited to bring the first Edge AI demo to the Torizon Demo Gallery,” said Klaus Buchheim, Head of Business development at Klepsydra “Supporting ONNX gives developers a straightforward way to experiment with established deep learning models and understand how they can be deployed in real-world edge environments.”
The demo is available now through the Torizon Demo Gallery:
https://www.torizon.io/demo-gallery/onnx-klepsydra-demo
About Klepsydra
Klepsydra develops software solutions for high-performance, real-time data processing and Edge AI, helping organizations build responsive and efficient systems for embedded and edge computing environments.
Through its software technology and integration with leading embedded platforms, Klepsydra enables developers to process data and run demanding workloads closer to the source, supporting applications where performance, latency and reliability are critical.
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