We recently released Gemma 4, our most capable open models to date. Since then, they have been downloaded more than 150 million times, and we’ve been expanding the family’s capabilities. We introduced Multi-Token Prediction (MTP) to accelerate inference, and recently released the 12B Unified model and Quantization-Aware-Training (QAT) checkpoints. Released under an Apache 2.0 license, Gemma 4 gives builders and organizations flexibility to fine-tune and deploy models across a variety of environments, from edge devices to local workstations.
Many builders are sharing what they’ve created with Gemma 4, showcasing how the models’ capabilities translate into real-world applications. Here are three highlights of what people and companies are creating.
Build low-latency, on-device apps.
The team at the app building company HubX used Gemma 4 to build BetterSpeak, an offline AI English tutoring platform. BetterSpeak uses the edge-optimized Gemma 4 E2B (effective 2B parameters) model as the reasoning engine for its on-device pipeline, enabling private, low-latency tutoring without the need for an internet connection.
To overcome mobile hardware constraints, HubX deployed the 4-bit quantized version of the model released by Google. This version handles tasks like grammar explanations and progress monitoring across multiple languages. By leveraging Gemma 4’s native audio input capabilities, the app supports direct speech-to-speech learning, reducing costs while ensuring user privacy by processing all vocal and text data entirely on-device.
