🔍 Introduction Edge computing and large language models (LLMs) are converging fast — bringing private, low-latency intelligence directly onto devices instead of relying only on cloud APIs . For developers, this shift unlocks new product experiences — instant responses, offline capability, and stronger data control — while demanding new skills in model optimization , scheduling, and systems design. --- 🚀 Why Edge LLMs Now ⚡ Latency and Privacy Drive On-Device Inference Acting near the data source minimizes round-trips and keeps sensitive inputs local — a win for real-time and regulated use cases like healthcare and industrial automation . 💪 Hardware and Runtimes Matured Lightweight runtimes derived from llama.cpp , plus mobile NPUs and compact accelerators, make quantized 7B-class models usable on laptops, gateways, and even some phones. 📚 Research and Surveys Show Momentum Recent reviews document techniques across the lifecycle — design, compression, deployment — all tailored f...
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