Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks

Gemma 4 is Google DeepMind's open-weight model family (Apache 2.0) spanning four variants: E2B and E4B for on-device/edge use, a 26B A4B Mixture-of-Experts model, and a 31B dense model. Key architectural innovations include interleaved local/global attention, Grouped Query Attention (GQA), K=V caching, pruned RoPE (p-RoPE), and a ViT-based vision encoder with 2D RoPE and soft token budgets. The E2B/E4B models uniquely use Per-Layer Embeddings stored in flash memory and include an audio encoder for speech recognition and translation. The 26B A4B MoE achieves 26B-parameter knowledge capacity at roughly 4B inference cost via sparse expert routing with 8 of 128 experts active per token. Hardware requirements range from ~3 GB VRAM (E2B at 4-bit) to ~60 GB (31B at BF16). All models support thinking mode (chain-of-thought), function calling, native structured output, and multimodal inputs. Deployment options include Hugging Face Transformers, llama.cpp, MLX, and cloud via Vertex AI or Gemini API. Fine-tuning is supported via QLoRA/TRL.

#llm

#multimodal

#gemma

#mixture-of-experts

Jun 22•30m read time•From pyimagesearch.com

Table of contents

Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks Gemma 4 Model Family Overview: E2B, E4B, 31B, and MoE 26B A4B Gemma 4 Capabilities: Reasoning, Multimodal AI, and Thinking Mode Gemma 4 Architecture Overview: Shared Transformer Design Principles Gemma 4 Architecture Variants: Dense vs MoE vs On-Device Models Gemma 4 Hardware Requirements: GPU VRAM and Inference Cost Breakdown Gemma 4 Benchmarks: LMArena Elo Scores and Multimodal Performance Results How to Run Gemma 4: Transformers, llama.cpp, MLX, and Cloud Deployment Options Fine-Tuning Gemma 4: LoRA, QLoRA, and TRL Training Pipeline Guide Gemma 4 Prompt Formatting: Chat Templates and Multimodal Input Structure Which Gemma 4 Model to Use: E2B vs E4B vs 26B MoE vs 31B Comparison Summary

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