llama.cpp brings fully offline LLMs to Android

This is the kind of scrappy deployment story that matters more than benchmark bragging: local AI keeps getting good enough to be useful on hardware people already own.

• –The most important detail is not 6 t/s, it is full offline ownership: weights, prompts, and inference stay on-device with no API key or remote server.
• –llama.cpp now officially documents Android builds through Termux, so this setup is moving from hacky experiment toward repeatable developer workflow.
• –A 1B model on 7.5GB RAM is modest, but it is enough for scripting help, infra Q&A, and lightweight assistant use where latency and privacy matter more than depth.
• –Mobile inference still hits hard limits on model size and context, so the real win is edge reliability and portability, not replacing desktop-class local rigs.
• –Expect more developer tooling around tiny GGUF models, local web UIs, and phone-first inference as on-device use cases mature.