On-device
Pixel Watch 5 adds offline Gemini commands and faster on-device smart replies
Offline voice commands on the Pixel Watch 5 run on an on-device model, and a Gemini Nano upgrade makes smart replies 50 percent faster.
Pixel 11 series: Tensor G6 adds 50 percent more TPU compute
Google says Tensor G6 with the latest Gemini Nano model processes on-device AI tasks up to 3.5 times faster while using up to 3.5 times less energy.
RikkaHub Agent test: Android phone agent compiles whisper.cpp on its own
XDA runs RikkaHub Agent on an Oppo Find N5 against a self-hosted Qwen 3.6 27B. The agent installed dependencies and built whisper.cpp in Termux in seven minutes.
Liquid AI releases LFM2.5-2.6B for on-device agents
Liquid AI reports 30 tokens per second on a phone and under 2.5 GB of memory for its 2.6-billion-parameter model, released with open weights.
Gemini Nano 4 ships on Samsung foldables with ML Kit Prompt API access
Google says Samsung’s new foldables carry Gemini Nano 4 with support for more than 140 languages, reachable from apps through ML Kit’s Prompt API.
FBLayout fine-tunes transformers on phone GPUs 2.2 to 5.7 times faster
A MobiSys 2026 paper fine-tunes seven transformer models on phone GPUs 2.2 to 5.7 times faster than MNN, TFLite and TVM, with 4.2 times fewer cache misses.
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.
Apertus Mini distils an open-data 8B into 0.5B, 1.5B and 4B models
Swiss AI distilled its fully open Apertus 8B into 0.5B, 1.5B and 4B models on 1.7T tokens, with 3-bit to 6-bit MLX builds for Apple devices.
Meta AI builds MobileMoE, 5.3B parameters with 0.9B active per token
Meta AI trained three on-device mixture-of-experts models that store 1.3B to 5.3B parameters and run 272M to 922M of them per token.
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.