Compressing Models for Edge AI: Quantization, Pruning, Distillation, and a Jetson Deployment Flow
Finding it hard to deploy AI models on low-power edge devices outside the cloud? This guide walks you systematically from the core principles of model compression (quantization and pruning) through a practical deployment pipeline using TFLi
May 31
Beyond the Cloud to the Edge: An Essential Tool Comparison Guide for Embedded AI (TFLite vs. ONNX Runtime)
Does deploying AI models onto real hardware (Raspberry Pi, Jetson, and similar devices) feel harder than training them? This guide compares TensorFlow Lite and ONNX Runtime—the core runtimes for edge computing—and walks through deployment w
May 19
Beyond the Cloud to the Edge: An In-Depth Analysis of Modern Architecture Patterns and Tech Stacks for On-Device LLM Deployment
A practical guide to on-device LLM deployment that overcomes cloud API limits and delivers ultra-low latency. From quantization, ONNX, and TensorRT to production architecture patterns, it lays out a roadmap you can apply immediately.
May 17