Running LLMs On-Device in Android: GGUF Models, NNAPI, and the Real Performance Tradeoffs

--- title : " On-Device LLMs in Android: GGUF Models, NNAPI, and Real Performance Tradeoffs" published : true description : " A practical guide to shipping on-device LLM inference in production Android apps — covering GGUF quantization, NNAPI delegation, memory management, and benchmarking that reflects real user latency." tags : android, kotlin, mobile, performance canonical_url : https://blog.mvpfactory.co/on-device-llms-android-gguf-nnapi-performance-tradeoffs --- ## What You Will Learn By the end of this guide, you will know how to pick the right quantization format for on-device LLM inference, build a chipset-aware backend selection strategy, manage memory pressure on mid-range Android hardware, and benchmark in a way that actually predicts what your users will experience. This comes from shipping to 200K+ devices — not from reading spec sheets. ## Prerequisites - An Android project targeting API 26+ - Familiarity with Kotlin and Android lifecycle callbacks - A physical test devic