--- title: Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and... description: 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... canonical: https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks | daily.dev og:description: 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... og:url: https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz og:image: https://api.daily.dev/og/posts/lUrZ1EcwZ.png og:image:alt: Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks og:image:width: 1200 og:image:height: 630 og:locale: 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: ## Questions this post answers ### What is the difference between Gemma 4 31B and Gemma 4 26B A4B? Gemma 4 31B is a dense transformer where all parameters are used on every inference pass, while the 26B A4B is a Mixture-of-Experts model with 26 billion total parameters but only 4 billion active per token, achieved by routing to 8 of 128 experts plus one always-active shared expert. This gives the 26B A4B roughly 4B-model inference speed with much larger knowledge capacity. _Compare architecture trade-offs on daily.dev before deciding which Gemma 4 variant fits your inference budget._ ### How much VRAM does Gemma 4 31B need to run at 4-bit quantization? The 31B dense model needs roughly 17 GB of VRAM at 4-bit quantization, making a single RTX 4090 or A10G viable, compared to about 60 GB at full 16-bit precision requiring two A100 80GB GPUs or a single H100. The 26B A4B MoE model drops to 15.6 GB at 4-bit despite a 48 GB full-precision footprint. _Track hardware requirements like these on daily.dev when planning local LLM deployments._ ### What is p-RoPE and why does Gemma 4 use it? p-RoPE (pruned RoPE) applies rotational positional encoding only to the first p fraction of embedding pairs and zeroes out the rest, preventing low-frequency rotations from accumulating noise into semantic content over very long sequences. For example, with p = 0.25, only the top 25% of high-frequency pairs get positional rotation, leaving the rest for pure semantic meaning, which matters most in global attention layers spanning tens of thousands of tokens. _Follow deep architecture explainers like this on daily.dev to keep up with long-context model design._ ## Similar posts on daily.dev - [Welcome Gemma 4: Frontier multimodal intelligence on device](https://daily.dev/posts/welcome-gemma-4-frontier-multimodal-intelligence-on-device-mkjiapoth) · Hugging Face · 8 upvotes · 3 comments - [Google’s Gemma 4 shines on local systems – both big and small](https://daily.dev/posts/google-s-gemma-4-shines-on-local-systems-both-big-and-small-qpert58dk) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#multimodal](https://daily.dev/tags/multimodal), [#mixture-of-experts](https://daily.dev/tags/mixture-of-experts), [#gemma](https://daily.dev/tags/gemma) [View this post on daily.dev](https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"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","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz"},"datePublished":"2026-06-22T12:46:06.421Z","dateModified":"2026-10-15T02:22:14.976Z","description":"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...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/eb898468cca9c928c305c22670991750?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/eb898468cca9c928c305c22670991750?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"PyImageSearch","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"PyImageSearch","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/33ddc48b99ee47eb9283ef1fdcff3095","url":"https://daily.dev/sources/pyimagesearch"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,llm,multimodal,mixture-of-experts,gemma","timeRequired":"PT30M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"PyImageSearch","item":"https://daily.dev/sources/pyimagesearch"},{"@type":"ListItem","position":3,"name":"Google DeepMind’s Gemma 4: MoE, Efficiency Tricks, and Benchmarks"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/google-deepmind-s-gemma-4-moe-efficiency-tricks-and-benchmarks-lurz1ecwz#faq","mainEntity":[{"@type":"Question","name":"What is the difference between Gemma 4 31B and Gemma 4 26B A4B?","acceptedAnswer":{"@type":"Answer","text":"Gemma 4 31B is a dense transformer where all parameters are used on every inference pass, while the 26B A4B is a Mixture-of-Experts model with 26 billion total parameters but only 4 billion active per token, achieved by routing to 8 of 128 experts plus one always-active shared expert. This gives the 26B A4B roughly 4B-model inference speed with much larger knowledge capacity. Compare architecture trade-offs on daily.dev before deciding which Gemma 4 variant fits your inference budget."}},{"@type":"Question","name":"How much VRAM does Gemma 4 31B need to run at 4-bit quantization?","acceptedAnswer":{"@type":"Answer","text":"The 31B dense model needs roughly 17 GB of VRAM at 4-bit quantization, making a single RTX 4090 or A10G viable, compared to about 60 GB at full 16-bit precision requiring two A100 80GB GPUs or a single H100. The 26B A4B MoE model drops to 15.6 GB at 4-bit despite a 48 GB full-precision footprint. Track hardware requirements like these on daily.dev when planning local LLM deployments."}},{"@type":"Question","name":"What is p-RoPE and why does Gemma 4 use it?","acceptedAnswer":{"@type":"Answer","text":"p-RoPE (pruned RoPE) applies rotational positional encoding only to the first p fraction of embedding pairs and zeroes out the rest, preventing low-frequency rotations from accumulating noise into semantic content over very long sequences. For example, with p = 0.25, only the top 25% of high-frequency pairs get positional rotation, leaving the rest for pure semantic meaning, which matters most in global attention layers spanning tens of thousands of tokens. Follow deep architecture explainers like this on daily.dev to keep up with long-context model design."}}]} ```