Gemma 4 Complete Guide: Architecture, Models, and Deployment in 2026
This article provides a comprehensive overview of the new Gemma 4 language model released by Google DeepMind, including its four model variants, architectural details, and deployment options across cloud, local, and mobile platforms.
Why it matters
The release of Gemma 4 under a permissive license and its efficient model variants make it a significant development in the field of large language models, with potential applications across a wide range of industries.
Key Points
- 1Gemma 4 ships in four model sizes with different architectures and target use cases
- 2The 26B A4B model uses a Mixture-of-Experts (MoE) design for efficient inference
- 3The E2B and E4B edge models leverage Per-Layer Embeddings (PLE) for low-memory deployment
- 4All Gemma 4 models use a hybrid attention mechanism with local and global layers
Details
Gemma 4 was released by Google DeepMind in April 2026 under the Apache 2.0 license, a significant shift from previous versions. The model family includes four variants with different parameter counts, architectures, and target deployment platforms. The 26B A4B model uses a Mixture-of-Experts (MoE) design, where only 3.8B parameters activate per token, reducing the VRAM requirements compared to a standard dense model. The E2B and E4B edge models leverage Per-Layer Embeddings (PLE) to enable sub-2GB RAM deployment on mobile devices. All Gemma 4 models use a hybrid attention mechanism with alternating local sliding-window and global full-context attention layers. The larger 26B A4B and 31B models support longer context windows of up to 256K tokens, as well as multimodal capabilities like image, video, and function calling inputs.
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