How to run small language models (SLMs) on-device: model sizing, distillation, quantization, NPU acceleration, memory budgets, and when a 1-8B SLM beats a cloud LLM.
A hands-on NVIDIA Jetson and K3s edge AI cluster tutorial: provision nodes, enable GPU scheduling, deploy a vision model, and run inference at the edge.
A reference architecture for industrial machine vision defect detection: edge AI inference, camera-to-PLC pipelines, model training, and MLOps on the factory floor.
Fact-checking the claim that edge AI slashes cloud bills: where the savings are real, where they hide capital and ops costs, and the break-even math for 2026.
Neuromorphic chips compute like a brain: spikes, not clocks. How spiking neural networks, memristors, and event-driven silicon work, and why they sip power.
A 2026 analysis of lights-out, autonomous factories — what edge AI and digital twins actually deliver, where the hype breaks, and the realistic adoption curve.
A 2026 benchmark methodology for small language models on edge GPUs — latency, tokens/sec, memory, and cost for Phi, Gemma, and Qwen on Jetson-class hardware.