Google Developers

Understand the Gemma 4 model family

Published 2026-09-21 · Duration 2:21

Summary

The Gemma 4 family is a set of multimodal, open-source large language models available in five sizes across four architectures. The models range from efficient, dense, on-device options (E2B, E4B) utilizing Per-Layer Embeddings (PLE), to advanced architectures like the encoder-free 12B model, the Mixture-of-Experts (MoE) 26B model, and the highly capable 31B dense model. Each size is optimized for specific use cases, including dedicated vision and audio processing.

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Key takeaways

  1. E2B and E4B Models

    These smaller, dense models are optimized for on-device usage and utilize Per-Layer Embeddings (PLE), which are lookup tables for processing queries. They process audio and images using dedicated encoders.

  2. 12B Model Architecture

    This model is suitable for high-end laptops and employs an encoder-free method, removing dedicated encoders (e.g., audio encoder) and directly projecting audio to the LLM.

  3. 26B Model (MoE)

    This Mixture-of-Experts (MoE) model uses 26 billion parameters but only activates four billion at any given time ('A' for active). It features a larger vision encoder, making it ideal for difficult vision tasks.

  4. 31B Model

    This is the most capable dense model in the Gemma 4 family. It utilizes a larger vision encoder and is presented as the top-tier model.

Technical details

  • Model Architecture & Efficiency 0s

    The Gemma 4 family includes five sizes across four architectures. Smaller models (E2B, E4B) are dense and use Per-Layer Embeddings (PLE). The 12B model uses an encoder-free method, projecting modalities directly to the LLM.

  • Mixture-of-Experts (MoE) 0s

    The 26B model is an MoE architecture, meaning it activates only a subset of its parameters (4B) despite having 26B total parameters.

Mentioned resources

  • Gemma 4 Resources (Informational)

Channel & topics

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