Gemma

Gemma is a family of lightweight, state-of-the-art open-weights models developed by Google DeepMind and other teams across Google. Built upon the same technical foundations as the Gemini family, Gemma models are designed for 2026's decentralized AI landscape, offering high performance in relatively small parameter sizes (2B, 9B, and 27B). The architecture utilizes a dense decoder-only transformer setup, incorporating advanced techniques such as Multi-Query Attention (MQA), Sliding Window Attention (SWA), and Logit Soft-capping to maintain high accuracy while reducing memory footprint. In the 2026 market, Gemma serves as the primary alternative to Meta's Llama for developers requiring deep integration with Google Cloud Vertex AI or those targeting edge deployment on Android and Chrome-based environments. Its ecosystem includes specialized variants like CodeGemma for programming, PaliGemma for vision-language tasks, and RecurrentGemma for long-context efficiency. By providing open weights with a commercially permissive license, Google has positioned Gemma as a cornerstone for private RAG (Retrieval-Augmented Generation) and localized enterprise deployments where data sovereignty is paramount.