#edge-ai (21)
- Gemma 4 QAT: Efficient AI Deployment for Edge Devices
Optimize AI model deployment for mobile and laptop environments using Gemma 4 QAT to achieve efficient on-device performance.
- Model Compression & Quantization for Efficient AI Deployment
Discover how model compression and quantization optimize large language models, enabling efficient deployment on mobile and edge devices.
- Deploy Gemma 4 with QAT for Efficient Multimodal Edge AI
Learn to optimize Gemma 4 multimodal models using Quantization-Aware Training for efficient deployment on mobile and laptop devices.
- Select Gemma 4 QAT Models for Efficient Edge AI Projects
Readers will learn to find, understand, and select optimal Gemma 4 QAT models for their mobile and laptop AI projects, ensuring efficient performance.
- Quantization-Aware Training for Accurate Edge LLMs
Learn to optimize large language models like Gemma 4 for efficient edge deployment using Quantization-Aware Training while preserving model accuracy.
- Evaluate Gemma 4 QAT Model Accuracy and Inference Speed
Understand how to quantify Gemma 4 QAT model performance by benchmarking accuracy, inference speed, and memory footprint to meet real-world application demands.
- Gemma 4 QAT: Efficient AI for Edge Devices
Master Gemma 4 QAT models for efficient AI on mobile and laptops. Learn QAT from first principles, optimize model compression, and integrate new checkpoints with practical steps and benchmarks.
- Deploying Gemma 4 QAT Models to Mobile and Laptop Environments
Learn how to deploy Google's Gemma 4 QAT models to mobile and laptop environments, focusing on efficiency, reduced memory, and faster inference for on-device AI applications.
- Deploying Gemma 4 QAT Models for Edge and Mobile AI Applications
Learn to build and deploy Gemma 4 QAT models for real-world edge and mobile AI applications, gaining confidence to integrate optimized AI into your projects.
- Building On-Device AI Agents with Tiny LLMs: Three Practical Projects
Explore and build three distinct on-device AI agents—a voice assistant, a data summarizer, and an anomaly detector—using tiny LLMs and modern edge tooling on a Raspberry Pi.
- Introduction to Edge AI Agents and Environment Setup
Understand the landscape of on-device AI agents and tiny LLM systems, set up your development environment, and explore core tooling for edge AI.
- Integrate a Tiny Local LLM for Edge Device Language Understanding
Integrate a tiny, quantized LLM directly onto an edge device to enable real-time, privacy-preserving natural language understanding without cloud dependency.
- Implementing On-Device Speech-to-Text with Whisper.cpp
Learn to implement robust, on-device speech-to-text functionality using Whisper.cpp, a high-performance C++ port of OpenAI's Whisper model, for edge AI agent systems.
- Build On-Device AI Agent Intent Mapping with Local LLMs
Learn to build a Python pipeline that uses a local LLM to convert transcribed text into structured user intents and entities for on-device AI.
- Smart Home Integration and Action Execution
Integrate your on-device AI agent with smart home systems to execute real-world actions using local APIs and tiny LLMs for intent mapping.
- Optimizing Performance and Resource Management on Edge Hardware
Master techniques for optimizing AI agent and tiny LLM performance and resource usage on constrained edge devices for real-world production deployments.
- Ensuring Robustness, Error Handling, and Basic Security
Learn how to build robust, secure, and error-tolerant on-device AI agents and tiny LLM systems using modern edge AI tooling as of early 2026.
- Deployment, Maintainability, and Expanding Edge AI Agent Concepts
Learn production-grade deployment strategies, maintainability best practices, and advanced concepts for evolving on-device AI agents and tiny LLM systems.
- Edge AI Agent & Tiny LLM Projects for On-Device Apps
Build intelligent, autonomous AI agent and tiny LLM applications directly on edge hardware using modern edge AI tooling and frameworks.
- Edge LLM Deployment: Strategies for On-Device Production
Learn specialized optimization, hardware, and deployment strategies to achieve sustainable, real-time Edge LLM performance on resource-constrained devices.
- Optimize Real-Time Multimodal AI for Speed and Latency
Learn to optimize multimodal AI systems for real-time speed and low latency across diverse data types, enabling instant responses in critical applications.