mobile AI

Challenges of Running LLMs on Android

Running large language models (LLMs) on Android devices presents significant challenges, as evidenced by the experience of fine-tuning Gemma 3 1B for multi-turn chat data. While the model performs well on a PC when converted to GGUF, its accuracy drops significantly when converted to TFLite/Task for Android, likely due to issues in the conversion process via 'ai-edge-torch'. This discrepancy highlights the difficulties in maintaining model performance across different platforms and suggests the need for more robust conversion tools or alternative methods to run LLMs effectively on mobile devices. Ensuring reliable LLM performance on Android is crucial for expanding the accessibility and usability of AI applications on mobile platforms.

Read Full Article

Posted on

Jan 8, 2026

by

TweakedGeekTech

in

Commentary, Tools

Topics: LLMs, Android, mobile AI

Rendrflow Update: Enhanced AI Performance & Stability

The recent update to Rendrflow, an on-device AI image upscaling tool for Android, addresses critical user feedback by enhancing memory management and significantly improving startup times. Memory usage for "High" and "Ultra" upscaling models has been optimized to prevent crashes on devices with lower RAM, while the initialization process has been refactored for a tenfold increase in speed. Stability issues, such as the "Gallery Sharing" bug and navigation loops, have been resolved, and the tool now supports 10 languages for broader accessibility. These improvements demonstrate the feasibility of performing high-quality AI upscaling privately and offline on mobile devices, eliminating the need for cloud-based solutions.

Read Full Article

Posted on

Jan 2, 2026

by

FilteredForSignal

in

How-Tos, Tools

Topics: AI performance, AI optimization, user feedback

LFM2 2.6B-Exp: AI on Android with 40+ TPS

LiquidAI's LFM2 2.6B-Exp model showcases impressive performance, rivaling GPT-4 across various benchmarks and supporting advanced reasoning capabilities. Its hybrid design, combining gated convolutions and grouped query attention, results in a minimal KV cache footprint, allowing for efficient, high-speed, and long-context local inference on mobile devices. Users can access the model through cloud services or locally by downloading it from platforms like Hugging Face and using applications such as "PocketPal AI" or "Maid" on Android. The model's efficient design and recommended sampler settings enable effective reasoning, making sophisticated AI accessible on mobile platforms. This matters because it democratizes access to advanced AI capabilities, enabling more people to leverage powerful tools directly from their smartphones.