Gemma 4 Architecture and Benchmark Notes

This article provides a technical analysis of the Gemma 4 31B dense model architecture, highlighting its similarities to Gemma 3 27B in terms of local-global attention, grouped-query attention with QK-Norm, and RMSNorm blocks. It notes the benchmark improvements likely stem from training data and recipe rather than architectural changes. The article also discusses a sparse MoE variant (Gemma 4 26B-A4B) and the licensing shift to Apache License 2.0, which is more permissive than the Gemma 3 license. Benchmark comparisons show Gemma 4 31B performing closer to Qwen3.5-27B than Gemma 3 27B. The content is relevant to IT/TECHNOLOGY, focusing on AI model architecture and performance evaluation.