Generative Edge AI: Architectures, Agents & Apps with Rob Tiffany (IDC)

How do you get real value from generative AI without sending everything to the cloud?
In this deep-dive conversation with Rob Tiffany, Research Director at IDC, we break down how organizations are building private language models, optimizing them for performance, and deploying intelligence all the way out to the edge—from factory floors to kiosks to mobile devices.

We explore a practical, end-to-end framework:
• Centralize your knowledge into an enterprise brain
• Compress, prune, distill, and quantize models for on-device execution
• Align model size with hardware tiers (gateways, kiosks, ARM devices, phones, AI PCs)
• Deploy hybrid architectures that keep critical inference local and heavy reasoning in private cores

You’ll hear real examples including an offline kiosk avatar that answered edge AI questions with no cloud connection, plus field-tested insights on balancing memory, latency, cost, and privacy.

We also dig into the economics:
Cloud tokens, egress fees, and latency penalties drive organizations to hybrid inference—running models on-prem or on-device while keeping private large models for deeper tasks behind the firewall.

If you’re shaping your generative edge AI roadmap—SOP guidance, field service assist, part lookup, customer support, or internal policy Q&A—this episode gives you the clarity to choose the right model size, hardware, and architecture.

Full conversation with Rob Tiffany → https://youtu.be/vKGWZQfryjs

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