Generative Edge AI: Architectures, Agents, & Apps
Real-world edge AI isn’t just a model and a camera; it’s a system that must hit KPIs under tight power, thermal, and budget limits. We dive into how Intel’s open edge approach brings together Linux-based base kits, an Edge Microvisor, remote manageability, and optimized libraries to help builders ship reliable, scalable solutions. The star of the show is Edge AI Suites—curated, open-source blueprints for retail, manufacturing, metro, and robotics—that translate best practices into working sample apps and end-to-end workflows.
We walk through what it takes to go from prototype to production: microservice boundaries that keep latency predictable, model management to handle updates safely, and benchmarking that measures application performance instead of raw FPS. You’ll hear how OpenVINO streamlines deployment across CPU, integrated GPU, and NPU on Core Ultra, letting you write once and run on the best accelerator available. Deep Learning Streamer powers robust video pipelines, while the SceneScape microservice fuses multiple sensors and layers VLM and GenAI agents for advanced tasks like smart route planning and event-driven alerts.
The tour includes metro demos—natural language video search, smart intersections, and parking—as well as retail and manufacturing patterns that mix vision with on-device GenAI. We also tackle interoperability head on: PyTorch, llama.cpp, and Ollama are part of the picture, with upstream optimizations and runtime paths that meet developers where they are. Whether you’re outfitting a small store with four cameras or a warehouse with sixty, you’ll walk away with a clearer playbook for device qualification, architecture choices, and choosing hardware that meets your KPIs without overspend.
Explore the resources, clone the repos, and test the blueprints on your target hardware. If this breakdown helps you build smarter at the edge, follow the show, share it with a teammate, and leave a quick review—what edge use case should we unpack next?
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