Gemma 4 QAT: Efficient AI for Edge Devices

This comprehensive guide empowers developers to leverage Gemma 4 QAT models for optimized on-device AI. Dive into Quantization-Aware Training (QAT) from its foundational principles, understanding how it dramatically compresses models for mobile and laptop efficiency. Explore practical steps, benchmarks, and real-world use cases to seamlessly integrate these new, high-performance checkpoints into your applications.

Chapters

1. 01 Model Compression & Quantization for Efficient AI Deployment 10m
2. 02 Quantization-Aware Training for Accurate Edge LLMs 13m
3. 03 Deploy Gemma 4 with QAT for Efficient Multimodal Edge AI 13m
4. 04 Select Gemma 4 QAT Models for Efficient Edge AI Projects 11m
5. 05 Setting Up Your Development Environment and Running Initial Inference 14m
6. 06 Evaluate Gemma 4 QAT Model Accuracy and Inference Speed 16m
7. 07 Deploying Gemma 4 QAT Models to Mobile and Laptop Environments 20m
8. 08 Deploying Gemma 4 QAT Models for Edge and Mobile AI Applications 15m