“on-device-ai”
Function Calling and Structured Output for On-Device Agents with Gemma 4
Make on-device agents reliable with Gemma 4 function calling and constrained decoding. Learn structured output patterns, parse-error fallbacks, and how to build a trustworthy tool-calling loop with LiteRT-LM.
On-Device AI in the Browser: LiteRT-LM with WebGPU and WebAssembly
Run Gemma 4 LLM inference in the browser with LiteRT-LM, WebGPU, and WebAssembly. Learn the JavaScript API, serverless architecture, and how to build privacy-preserving web apps with zero backend.
Gemma 4 on Arm: SME2 and the Optimized On-Device AI Stack
How Arm SME2 instructions and KleidiAI accelerate Gemma 4 on-device AI. Learn about the hardware-software stack delivering 5.5x prefill speedups and enabling privacy-first edge experiences on Android.
On-Device Agentic Skills in Flutter with flutter_gemma_agent
Turn your Flutter app into an on-device AI agent with flutter_gemma_agent. Learn the SKILL.md catalog, four skill execution mechanisms, two-stage discovery, and the tool-calling loop that runs fully offline.
Running Gemma 4 On-Device in a Multi-Agent Flutter App
Lessons learned running Gemma 4 E4B inside a Flutter app with multi-agent concurrency. Learn the platform channel bridge pattern, engine singleton design, serialization, and the multimodal constraints that bite in production.
Building a High-Performance On-Device LLM Client in Flutter with Qualcomm QNN
Learn how to build a production-quality on-device LLM client in Flutter that runs Gemma 4 on Qualcomm NPUs. Covers Dart FFI vs MediaPipe delegates, model delivery workflows, self-healing registries, and streaming reasoning UX.
Blazing Fast On-Device GenAI with LiteRT-LM
A deep dive into the LiteRT-LM runtime powering on-device GenAI. Learn how multi-token prediction, memory-mapped embeddings, session management, and constrained decoding deliver blazing-fast Gemma 4 inference on mobile.
Bringing Agentic Skills to the Edge with Gemma 4
Learn how to give on-device apps true agentic capabilities with Gemma 4, LiteRT-LM, and Agent Skills. Build multi-step autonomous workflows that plan, call tools, and act entirely on the device.
The Future of Mobile Dev: On-Device Agents and Edge AI
Explore how on-device agents and edge AI are reshaping mobile development with Gemma 4, LiteRT-LM, and Flutter. Learn the architecture, tradeoffs, and code patterns for building privacy-first agentic apps that run entirely on the device.
On-Device AI in Flutter with Gemini Nano and GenKit
Learn how to run AI models directly on-device in your Flutter apps using Gemini Nano and Google GenKit for fast, private, offline-capable AI features.
Hyper-Personalized SLMs: Local Fine-Tuning of Gemini 3 Nano Models
A model that runs on-device still has to be trained somewhere. Learn to fine-tune Nano SLMs locally with LoRA, QLoRA, MeSP, and GradCut — without ever shipping user data to the cloud.
Wasm-Powered Agents: Running Secure Gemini 3 Nano Logic in the Browser
Ship an entire AI agent as a single HTML file. Learn the Wasm-powered agent pattern: Pyodide vs WebLLM, sandboxing agent-generated code in WASM kernels, and wiring MCP tools from a browser tab.
The Rise of Nano-3: Why Small Models are the New Standard for Mobile Intelligence
Gemini Nano v3 is the most capable on-device model Google has shipped. Learn why SLMs are the new mobile standard and how to build with AICore and ML Kit GenAI.
On-Device Intelligence: Running Gemma 4 E4B on Flutter with LlamaDart
How to integrate Gemma 4 E4B directly into your Flutter applications using high-performance LlamaDart bindings for true on-device intelligence.