--- title: "Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks" url: https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz source_url: https://pyimagesearch.com/2026/06/22/google-deepminds-gemma-4-moe-efficiency-tricks-and-benchmarks type: article source: "PyImageSearch" published: 2026-06-22T12:46:06.421Z updated: 2026-06-22T12:46:32.383Z tags: ["llm", "multimodal", "gemma", "mixture-of-experts"] reading_time: 30 upvotes: 1 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks **[PyImageSearch](https://daily.dev/sources/pyimagesearch)** · 30 min read · 1 upvotes · 0 comments ## Summary 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. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: --- Tags: [#llm](https://daily.dev/tags/llm), [#multimodal](https://daily.dev/tags/multimodal), [#gemma](https://daily.dev/tags/gemma), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts) [View this post on daily.dev](https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz)