Gemma 4 Deep Dive — Cassidy Hardin, Researcher, Google DeepMind

Open models are getting smaller, faster, and far more capable. In this talk, Cassidy Hardin walks through the latest advances in the Gemma family, with a focus on Gemma 4 and what it enables for developers building on-device and open-weight AI systems. She covers the architecture behind Gemma’s dense, effective, and mixture-of-experts models, including improvements to attention, multimodal support for text, vision, and audio, and the design decisions that make strong reasoning, coding, and agentic workflows possible at practical sizes. Speaker info:   / cassidyhardin   Timestamps: 00:00:28 - Introduction to the Gemma 4 model family and its four size categories 00:01:54 - Shift to Apache 2.0 licensing for developer accessibility 00:02:25 - Deep dive into the 31B dense reasoning and 26B mixture-of-experts (MoE) models 00:03:30 - Overview of on-device effective models (2B and 4B) with multimodal support 00:04:21 - Architectural updates: interleaved local/global attention and grouped query attention 00:06:51 - Explanation of the new MoE architecture (128 experts, 8 active) 00:07:44 - Implementation of Per Layer Embeddings (PLE) to optimize on-device memory 00:11:06 - Multimodal advances: variable aspect ratios and resolutions for vision encoders 00:16:31 - Audio processing enhancements via conformer architecture and audio tokenizers 00:18:07 - Getting started: self-hosting (Hugging Face, Ollama) and cloud deployment (Vertex AI)