#edge-ai
5 posts tagged with #edge-ai
Every article below is hand-written, technically reviewed, and focused on edge-ai. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning The Complete Guide to AI Hardware in 2026
A complete 2026 guide to AI hardware — covering GPUs, Apple Silicon, edge devices, CPUs, and the semiconductor supply chain — so you can choose the right setup for running local LLMs and AI workloads without wasting money.
AI and Machine Learning Raspberry Pi 5 vs Jetson Orin Nano 2026: Which Edge AI Board Wins?
The Jetson Orin Nano wins for serious edge AI workloads with its dedicated GPU and CUDA ecosystem, while the Raspberry Pi 5 wins for cost-sensitive prototyping, general computing, and hobbyist projects. Neither is universally better — it depends entirely on whether you need inferencing horsepower or affordability.
AI and Machine Learning Phi-3 vs Gemma 3 in 2026: Which Small LLM Wins for Edge Inference?
Phi-3 wins for ultra-constrained edge devices and Windows/Azure pipelines; Gemma 3 wins for multimodal tasks, Raspberry Pi deployments, and open-ecosystem flexibility. Here's the definitive breakdown.
Technology Gemma 3 on Raspberry Pi 5: Benchmarked [2026]
I benchmarked every runnable Gemma variant on a Raspberry Pi 5 — Gemma 3 1B, 4B, QAT models, and Gemma 3n — with real tokens-per-second numbers, RAM usage, and a definitive size guide for Pi owners.
AI and Machine Learning TinyML at CERN: How FPGAs and hls4ml Solve Physics' Biggest Data Problem [2026]
CERN's Large Hadron Collider generates 40 million bunch crossings per second. Only AI models running on FPGAs at 100-nanosecond inference speeds can decide what's worth keeping — and an open-source tool called hls4ml makes it possible.