Tech article
Running LLMs On-Device in Android: GGUF Models, NNAPI, and the Real Performance Tradeoffs
Community description: A deep technical walkthrough of shipping on-device LLM inference in production Android apps — covering model quantization formats (GGUF, QLoRA), hardware acceleration via NNAPI and GPU delegates, memory pressure management on mid-range devices, and benchmarking methodology that actually reflects user-perceived latency rather than synthetic throughput
Dev.to | Mar 9, 2026 | SoftwareDevs mvpfactory.io
Automated excerpt
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. " 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. | FP16 (baseline) | 6. 0 GB | 7.
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