Android
51 updates on Android.
MLPerf Mobile v6.0 adds Llama tests in 1B, 3B and 8B sizes
MLCommons added Llama tests in 1B, 3B and 8B sizes to its mobile benchmark app, reporting token throughput next to the existing vision and image tests.
llada.cpp runs a diffusion LLM on a Snapdragon NPU up to 42 times faster
Tsinghua and Beihang researchers report LLaDA-8B generating 128 tokens 17 to 42 times faster on a Hexagon NPU than on the phone CPU.
CAPED redacts phone screenshots before a cloud GUI agent sees them
Researchers at CUHK, Southeast University and Shandong University report a phone-side screenshot filter that cuts seeded leakage from 0.766 to 0.268.
LiteRT-LM reports 52 decode tokens per second for Gemma 4 E2B on an Android GPU
Google publishes prefill and decode figures for its on-device runtime, adds Swift and JavaScript APIs, and reports a 2.2x speedup from multi-token prediction.
Airgap is a React Native kit for support chatbots that answer offline
Xavier Puspus published a React Native kit for support chatbots that answer without a network, running a 2.4 GB Gemma 4 E2B file through llama.rn.
Meta's MobileLLM-Flash goes shallow and wide for 1.8 times faster prefill
Meta designed 350M, 650M and 1.4B models by measuring latency on a Galaxy S25, reversing the deep-and-thin rule of the first MobileLLM.
ClawMobile tries system commands before screen taps and finishes all six test tasks
An agent runtime that runs on the phone itself routes tasks to ADB commands before UI taps, and the authors report 100 percent completion on six tasks.
Alibaba releases GUI-Owl-1.5 agent models from 2B to 32B under MIT
Tongyi Lab open-sourced six GUI agent checkpoints from 2B to 32B and reports 71.6 on AndroidWorld, with every benchmark run server-side, not on a phone.
Show HN: Off Grid runs text, image, vision and speech models offline on phones
An MIT-licensed Android and iOS app bundles llama.cpp, Stable Diffusion and Whisper for offline use. The Show HN thread reached 124 points and 66 comments.
Meta's MobileLLM-Pro runs a 128k context from a 590 MB 4-bit build
Meta Reality Labs released a 1.08B on-device model with a 128k context window, measured at 33.6 tok/s decode on a Galaxy S25 CPU.
Confidant fine-tunes Phi2-2.7B across a phone and two laptops in 40.1 hours
Zhejiang University researchers split a language model across three everyday devices and fine-tuned Phi2-2.7B on Alpaca in 40.1 hours without moving raw data.
Meta trains 140M to 950M reasoning models on 4.2T tokens
MobileLLM-R1 spans 140M to 950M parameters, trained on 4.2T tokens, and Meta scores the 950M model at 74.0 on MATH500.