--- title: Gemma 4 runs locally on a 16GB laptop, costs nothing,... description: Google's Gemma 4 family offers two notable local AI models: the E4B (4B parameters with MoE architecture giving it ~8B knowledge capacity) and the new 12B.... canonical: https://daily.dev/posts/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Gemma 4 runs locally on a 16GB laptop, costs nothing, and handles text, vision, and audio | daily.dev og:description: Google's Gemma 4 family offers two notable local AI models: the E4B (4B parameters with MoE architecture giving it ~8B knowledge capacity) and the new 12B.... og:url: https://daily.dev/posts/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k og:image: https://api.daily.dev/og/posts/0BTFstO2K.png og:image:alt: Gemma 4 runs locally on a 16GB laptop, costs nothing, and handles text, vision, and audio 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. # Gemma 4 runs locally on a 16GB laptop, costs nothing, and handles text, vision, and audio **[Collections](https://daily.dev/sources/collections)** · 5 min read · 1 upvotes · 0 comments ## Summary Google's Gemma 4 family offers two notable local AI models: the E4B (4B parameters with MoE architecture giving it ~8B knowledge capacity) and the new 12B. Both run on 16GB laptops with no cloud dependency or API costs. The 12B is the first mid-sized Gemma model with native audio input, using an encoder-free multimodal design that routes vision and audio directly through the LLM backbone. Both models are Apache 2.0 licensed and run via Ollama, LM Studio, llama.cpp, MLX, or vLLM. The family has crossed 150 million downloads. ## Content Google's Gemma 4 family has quietly become one of the more practical options for running AI locally. Across a 16GB Windows laptop, an 8GB MacBook Air, and even a browser tab, people are getting real work done with these models — no API keys, no usage limits, no cloud dependency. Here's a breakdown of what's actually possible and how. ## The model lineup Gemma 4 comes in three main sizes worth knowing about: - **E4B** — a mixture-of-experts model that behaves like a 4B model in memory while drawing on roughly twice the stored knowledge. Fits comfortably in 16GB RAM. - **12B** — a mid-sized multimodal model that handles text, images, and audio through a single encoder-free backbone. Targets 16GB VRAM laptops and benchmarks close to the 26B model at under half the memory cost. - **26B (26B-A4B)** — the largest in the family, also MoE-based, activating only 4B parameters per forward pass. All three are released under Apache 2.0. The family has crossed 150 million downloads. ## Running E4B on a 16GB Windows laptop The E4B variant runs fine on a mid-range PC — tested on a Ryzen 5 5600G with 16GB RAM and no dedicated GPU, using LM Studio. No internet connection required after the initial download. A few things worth knowing before you start: **QAT vs PTQ quantization.** Gemma 4 offers QAT (quantization-aware training) versions, which are generally better than standard PTQ quantized models at the same bit width. Pick QAT if available. **Disable thinking mode.** By default, Gemma 4 runs an internal reasoning chain before responding. On slower hardware this adds noticeable latency. Turning it off roughly doubles generation speed with minimal quality loss for everyday tasks. **12B vs E4B tradeoff.** The 12B model is more accurate on harder tasks. The E4B runs about twice as fast. For most practical use — translation, summarization, code generation, image recognition — E4B holds up well. Practical demos that work well on this setup: handwriting transcription using the vision input, multilingual translation, document summarization, and Python code generation. The native vision capability is genuinely useful — you can photograph a handwritten note and get clean text back without any external OCR tool. For a fully private knowledge base, pairing E4B with Ollama and Obsidian works well. Everything stays local. ## Running 26B in 2GB RAM on Apple Silicon This one is more surprising. TurboFieldfare is an open-source Swift + Metal runtime that runs the full Gemma 4 26B model in approximately 2GB of RAM on any M-series Mac, including 8GB models. The trick: instead of loading the full 14.3GB model into memory, it keeps only the 1.35GB shared core and FP16 KV cache resident and streams the MoE expert weights from SSD on demand. The model uses 4-bit MLX affine weights with an 8-bit router, and custom Metal kernels handle quantized GEMV, attention, MoE routing, normalization, RoPE, and sampling. Benchmarks: - 8GB M2 MacBook Air: 5.1–6.3 tok/s - M5 Pro: 31–35 tok/s The project ships as a native SwiftUI Mac app, a CLI, a streaming model installer, and an OpenAI-compatible loopback server. The loopback server means you can point existing tools at it without modification. One catch: it requires macOS 26, Metal 4, and Swift 6.2 — so you need to be on the latest betas. ## Running in the browser with WebGPU If you don't want to install anything at all, Gemma 4 also runs entirely in the browser using Transformers.js, WebGPU, and ONNX Runtime. No Python, no Node.js, no backend. The setup loads a quantized (q4f16) ONNX version of the model, handles image and audio inputs via URL or file upload, streams generated tokens in real time, and caches model weights locally for faster subsequent loads. WebGPU support is required — Chrome on a reasonably modern machine works. The full implementation fits in a single HTML/JS file. It's a good option for demos or situations where you want zero installation friction. ## What the 12B adds The 12B model sits between E4B and 26B and is worth calling out separately because of its audio support. It's the first mid-sized Gemma model to accept native audio input, processed directly through the LLM backbone rather than a separate encoder. It also supports Multi-Token Prediction drafters for lower latency. For developers who need multimodal capabilities beyond images — transcription, audio understanding — and want something that fits on a consumer laptop, 12B is the practical choice. It runs via Ollama, LM Studio, llama.cpp, MLX, or vLLM. ## The bigger picture What's changed with Gemma 4 isn't any single capability — it's that the tradeoffs have shifted enough to make local AI genuinely usable across a wide range of hardware. An 8GB MacBook Air running a 26B model from SSD, a no-GPU laptop handling vision tasks, a browser tab doing multimodal inference — none of these required specialized hardware or cloud credits. The iteration speed changes when there's no cost per query. You stop rationing prompts. ## Similar posts on daily.dev - [Google's free Gemma 4 model runs on hardware you probably already own](https://daily.dev/posts/google-s-free-gemma-4-model-runs-on-hardware-you-probably-already-own-u5po7zub9) · XDA Developers · 6 upvotes · 0 comments --- Tags: [#local-ai](https://daily.dev/tags/local-ai), [#multimodal](https://daily.dev/tags/multimodal), [#ollama](https://daily.dev/tags/ollama), [#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/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k) ```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":"Gemma 4 runs locally on a 16GB laptop, costs nothing, and handles text, vision, and audio","url":"https://daily.dev/posts/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k"},"datePublished":"2026-07-24T15:46:38.851Z","dateModified":"2026-07-29T15:43:28.835Z","description":"Google's Gemma 4 family offers two notable local AI models: the E4B (4B parameters with MoE architecture giving it ~8B knowledge capacity) and the new 12B....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ff1fc5f43c6b48dab9fb47b7063b2ca4?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ff1fc5f43c6b48dab9fb47b7063b2ca4?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Collections","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":"Collections","logo":"https://media.daily.dev/image/upload/s--fk_6ycEi--/f_auto,q_auto/v1780996001/logos/collections?_a=BAMAMiWQ0","url":"https://daily.dev/sources/collections"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/gemma-4-runs-locally-on-a-16gb-laptop-costs-nothing-and-handles-text-vision-and-audio-0btfsto2k","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"local-ai,multimodal,ollama,mixture-of-experts,gemma","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Collections","item":"https://daily.dev/sources/collections"},{"@type":"ListItem","position":3,"name":"Gemma 4 runs locally on a 16GB laptop, costs nothing, and handles text, vision, and audio"}]} ```