# gemma4 A family of open AI models from Google DeepMind for generative tasks using text, audio, and image input. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer DevFeed: [An experiment with Gemma 4 on a Raspberry Pi explores what an LLM would do with its own computer]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-09-14T09:09:42Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [gemma4](), [Large Language Model](), [Raspberry Pi](), [Go Language](), [Simulation]() Tags: [computer](), [data](), [experiment](), [gemma-4](), [go](), [llm](), [patterns](), [raspberry-pi]() ### AI overview The author describes an experiment in which Gemma 4 12B was asked what it would do with its own computer. Running on a Raspberry Pi 1 through a Go harness, the model imagined ingesting large amounts of data, finding patterns, and running large-scale simulations. ### Source excerpt LLM's response ## Setup On Device Gemma 4 Android Development on Emulator DevFeed: [Setup On Device Gemma 4 Android Development on Emulator]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-11T14:27:55Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [gemma4](), [Android](), [Emulator](), [android-development]() Tags: [android](), [android-app-development](), [android-development](), [androiddev](), [artificial-intelligence](), [emulator](), [gemma](), [gemma-4](), [google](), [mobile-app-development](), [on-device]() ### AI overview A tutorial explaining how to set up and run the Gemma 4 large language model locally on an Android Emulator, including downloading and testing the Google AI Edge Gallery sample app. It notes that emulator storage and memory may affect model use. ### Source excerpt Create Android Project with Downloadable Android On Device Gemma 4 Model Continue reading on Mobile App Development Publication " ## Android Studio Quail Using On Device Gemma 4 Experience DevFeed: [Android Studio Quail Using On Device Gemma 4 Experience]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-09T11:28:45Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android Studio](), [gemma4](), [Large Language Model](), [Development]() Tags: [agent](), [android](), [android-development](), [android-studio](), [development](), [gemma](), [gemma-4](), [llm](), [local](), [memory](), [mobile-app-development](), [on-device]() ### AI overview This article describes using the on-device Gemma 4 model in Android Studio Quail for Android development. It outlines selecting the local model option, choosing Gemma in Model Providers, and downloading a model, while noting the need for a fast machine with substantial memory. ### Source excerpt How's Android Studio Quail support for On-Device LLM Model Doing? Continue reading on Mobile App Development Publication " ## Leverage Android skills and Gemma 4 in Android Studio Quail 4 DevFeed: [Leverage Android skills and Gemma 4 in Android Studio Quail 4]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-01T15:00:00Z Content type: release Language: en Sources: [Android Developers Blog](), [Android Developers Blog]() Topics: [Android skills](), [Android Studio](), [Large Language Model](), [releases](), [gemma4]() Tags: [agent](), [agent-skills](), [android-gradle-plugin](), [android-skills](), [android-studio](), [gemma](), [jetpack](), [llms](), [performance](), [release]() ### AI overview Android Studio Quail 4 is a stable production release that preloads curated Android skills for its agent. The skills help LLMs handle Android-specific workflows such as upgrades, profiling, navigation, and adaptive UI work. ### Source excerpt Posted by Amman Fasil Asfaw, Product Manager, Android Studio Android Studio Quail 4 is now stable and ready for you to use in production. This is the final stable release for Android Studio Quail. The new features in Android Studio enable you to build premium apps with AI efficiently and effectively. Check out the video below to see the most helpful new features from the last 4 releases that can help improve and speed up your development. Here is a deep dive into what's new in Android Studio Quail 4: Android skills bundled into Android Studio While LLMs are incredibly capable at generic coding queries, they frequently write incorrect or outdated code when confronted with rapidly evolving Android APIs, platform-specific migrations, or complex configuration structures.To solve this, we bundle Android skills that have been curated by the team who builds Android, directly into Android Studio. Following the open-standard agent skills specification, these are modular, AI-optimized instructions designed specifically to guide LLMs through complex Android workflows. Android skills are now pre-loaded directly into the IDE, so you can start using them without having to manually download additional files. When you prompt the Android Studio agent, we analyze your prompt and search against the metadata for installed skills, automatically invoking them when they're most relevant. Your agent gains instant domain expertise, applying Google's best practices with less overhead spent on long, manual setup prompts. Android Studio comes preloaded with 23 curated skills, including: Need help upgrading your build? You have the Android Gradle Plugin (AGP) 9 Upgrade skill. Want to profile your app for any performance issues? You have the Android Profiler skill. Ready for a Jetpack Navigation framework upgrade? You have the Navigation3 skill. Adapting your app UI to different Android devices? You have the Adaptive skill. We also encourage you to create your own custom skills to extend Agent M ## Leverage Android skills and Gemma 4 in Android Studio Quail 4 DevFeed: [Leverage Android skills and Gemma 4 in Android Studio Quail 4]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-09-01T15:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [Agent Skills](), [Android Studio](), [Artificial Intelligence](), [LLMs](), [Google](), [gemma4]() Tags: [agent-skills](), [ai](), [android-skills](), [android-studio](), [development](), [gemma-4](), [google](), [llms]() ### AI overview Android Studio Quail 4 is stable and includes Android skills bundled directly into the IDE. The curated, preloaded skills help its agent handle complex Android workflows, including API changes, migrations, configuration, profiling, navigation upgrades, and adaptive UI work. ### Source excerpt Posted by Amman Fasil Asfaw, Product Manager, Android Studio Android Studio Quail 4 is now stable and ready for you to use in production. This is the final stable release for Android Studio Quail. The new features in Android Studio enable you to build premium apps with AI efficiently and effectively. Check out the video below to see the most helpful new features from the last 4 releases that can help improve and speed up your development. Here is a deep dive into what's new in Android Studio Quail 4: Android skills bundled into Android Studio While LLMs are incredibly capable at generic coding queries, they frequently write incorrect or outdated code when confronted with rapidly evolving Android APIs, platform-specific migrations, or complex configuration structures.To solve this, we bundle Android skills that have been curated by the team who builds Android, directly into Android Studio. Following the open-standard agent skills specification, these are modular, AI-optimized instructions designed specifically to guide LLMs through complex Android workflows. Android skills are now pre-loaded directly into the IDE, so you can start using them without having to manually download additional files. When you prompt the Android Studio agent, we analyze your prompt and search against the metadata for installed skills, automatically invoking them when they're most relevant. Your agent gains instant domain expertise, applying Google's best practices with less overhead spent on long, manual setup prompts. Android Studio comes preloaded with 23 curated skills, including: Need help upgrading your build? You have the Android Gradle Plugin (AGP) 9 Upgrade skill. Want to profile your app for any performance issues? You have the Android Profiler skill. Ready for a Jetpack Navigation framework upgrade? You have the Navigation3 skill. Adapting your app UI to different Android devices? You have the Adaptive skill. We also encourage you to create your own custom skills to extend Agent M ## Abliterated Models Show Verdict Bias in FreeBSD Kernel Bug Hunting DevFeed: [Abliterated Models Show Verdict Bias in FreeBSD Kernel Bug Hunting]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-09-01T06:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [freebsd](), [vulnerability](), [bug](), [Kernel](), [gemma4](), [Testing](), [gemma]() Tags: [bug](), [bug-hunting](), [cve](), [false-positives](), [freebsd](), [gemma](), [gemma-4](), [kernel](), [llms](), [testing](), [vulnerability](), [vulnerability-research]() ### AI overview The article examines local abliterated open-weight models during vulnerability scanning of FreeBSD kernel source. Compared with base models of the same family and size, the modified models produced substantially more candidate and VALID findings, including false positives, while the most aggressive build did not surface the real CVE in a 28-file scan. The article describes this tendency as verdict bias: a greater willingness to return positive findings under uncertainty. ### Source excerpt Abliterated models never refuse, which makes them tempting for bug hunting. But on the same kernel source, they graduate three to four times as many findings to VALID, including false positives the base correctly rejects, and across a 28-file scan of FreeBSD's sys/rpc the most aggressive build never surfaced the real CVE at all. ## Running golf swing analysis on an Android device with a fine-tuned Gemma 4 model DevFeed: [Running golf swing analysis on an Android device with a fine-tuned Gemma 4 model]() Original publisher: [Read original article]() Published: 2026-08-29T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [Kotlin Multiplatform](), [gemma4](), [LiteRT](), [On-device AI](), [Fine-tuning]() Tags: [ai](), [android](), [cross-platform](), [fine-tuning](), [gemma](), [gemma-4](), [kotlin](), [kotlin-multiplatform](), [litert](), [model](), [multiplatform](), [on-device-ai]() ### AI overview This article explains how FormAI adds Android on-device golf-swing analysis using a small Gemma 4 model fine-tuned to imitate Gemini for a narrow coaching task. It covers generating training data with Gemini, fine-tuning with LoRA, converting the model for LiteRT-LM, and the current fallback to cloud analysis on other platforms. ### Source excerpt FormAI is a Kotlin Multiplatform app that analyses a video of your golf swing, basketball shot or running form and gives you coaching feedback. Up to now that has always meant uploading the video to Gemini and getting the response back over the network. We've added an option to do the golf swing analysis entirely on an Android device instead, using a small Gemma 4 model that we fine-tuned to imitate Gemini for that one task. LiteRT-LM, the runtime we use for this, is itself cross platform (Android, iOS, desktop and web), but we've only wired up the Android side so far, so this path lives in androidMain and the other targets report it as unavailable and fall back to the cloud. ## Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device DevFeed: [Engineering log: fine-tuning Gemma 4 E4B with LoRA to bring FormAI's coaching on-device]() Original publisher: [Read original article]() Published: 2026-08-28T23:00:00Z Content type: article Language: en Sources: [John O'Reilly]() Topics: [gemma4](), [Fine-tuning](), [lora](), [On-device AI](), [multiplatform](), [LiteRT](), [Android](), [Artificial Intelligence](), [API]() Tags: [ai](), [android](), [api](), [data](), [data-quality](), [engineering](), [fine-tuning](), [gemini](), [gemma](), [gemma-4](), [inference](), [kotlin-multiplatform](), [litert](), [lora](), [multiplatform](), [on-device]() ### AI overview This engineering log describes a prototype that distils FormAI's Gemini-based sports coaching feedback into Gemma 4 E4B fine-tuned with LoRA. The local pipeline uses seed videos, filters inadequate critiques, extracts frames, trains and merges the adapter, then converts the model to LiteRT-LM for possible on-device Android inference. ### Source excerpt ⚠ Note: this post is AI-generated. The text below was written by Claude, and documents findings from a series of Claude Code sessions working on this project -- the experiments, bugs and measurements described are ones that came out of those sessions. The engineering work is real and the numbers were measured rather than estimated, but the write-up is the model's own account of what it did, not a human's independent retelling of it. ## PROOF-Gen: From Optimized Data to Better Distillation DevFeed: [PROOF-Gen: From Optimized Data to Better Distillation]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Fine-tuning](), [Prompt optimization](), [Artificial Intelligence](), [gemma4](), [Programming]() Tags: [agent](), [ai-agents](), [evaluation](), [fine-tuning](), [gemma](), [generate](), [models](), [on-device](), [post-training](), [prompt-optimization]() ### AI overview PROOF-Gen improves tool-calling model distillation by using per-scenario prompt optimization to recover successful trajectories from failed teacher attempts. The method strips corrective guidance before training, producing clean demonstrations and improving benchmark, deployed-pipeline, and on-device model performance. ### Source excerpt Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher's passing trajectories, discard the rest) and each cycle leaves behind the same hard scenarios because failures supply no signal. On τ 2-bench, 57% of teacher trials fail, two-thirds of them near-misses (most tool calls correct, undone... ## How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Unlocks Ultrafast Interactivity at Long Context on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-24T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [d-matrix](), [Vera Rubin](), [Inference Performance](), [long-context](), [Benchmark](), [Artificial Intelligence](), [gemma4](), [systems](), [Cache]() Tags: [agent](), [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-inference](), [benchmark](), [cache](), [collective](), [data-center-cloud](), [developer-tools-techniques](), [groq](), [groq-3-lpx](), [inference-performance](), [long-context](), [low-latency-inference](), [lpx](), [nvidia](), [nvidia-vera](), [performance](), [rubin-gpu](), [speed](), [systems](), [tokens](), [training-ai-models](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview NVIDIA Groq 3 LPX, paired with Vera Rubin NVL72, delivers high-interactivity AI inference for long-context workloads. A reported benchmark measured 3,431 output tokens per second on Gemma 4 31B with a 100K context. ### Source excerpt NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform. At the core of the platform is NVIDIA Vera Rubin NVL72, the... ## Building a Local, Multimodal AI Terminal Agent with Gemma 4 DevFeed: [Building a Local, Multimodal AI Terminal Agent with Gemma 4]() Original publisher: [Read original article]() Author: Arjun Prabhulal Published: 2026-08-12T09:25:11Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [gemma4](), [Artificial Intelligence](), [multimodal-ai](), [Command-line interface](), [Large Language Model](), [Local AI](), [Code](), [Google]() Tags: [ai](), [ai-agent](), [artificial-intelligence](), [code](), [function-calling](), [gemma](), [gemma-4](), [google](), [google-cloud-platform](), [local-ai](), [multimodal](), [ollama](), [open-source](), [terminal]() ### AI overview A tutorial introduces Gemma 4 and builds a local multimodal terminal agent named gemma4-agent. It covers function calling, tool orchestration, text, image, and voice processing, plus Gemma 4's model variants and architecture. ### Source excerpt Introduction Open-source LLM models have been improving rapidly with tool calling, extended context windows, and native vision and audio capabilities, all while delivering strong benchmark performance. Gemma 4, recently introduced by Google Deepmind brings all of these features together in sizes efficient enough to run locally. In this article, we'll look at the capabilities of Gemma 4 and build a multimodal (Text, Vision, Voice) CLI agent (gemma4-agent) with function-calling capabilities. By the end, you'll have an agent that can chat, write, execute code, analyze images, and process voice instructions to deliver highly grounded responses. What is Gemma 4 Model ? Gemma 4 is Google DeepMind's open model family, released in April 2026 under the Apache 2.0 license. Built from the same research and technology behind Gemini 3, Gemma 4 is designed for high-performance reasoning, coding, multimodal understanding, and local AI execution across different model sizes. Features of Gemma 4 Models Improved Tool calling : Native function calling and tool orchestration, letting agents act autonomously without bloating prompt instructions Thinking mode : Built-in step-by-step thinking mode via the <|think|> token for complex multi-turn logic Context Windows : Up to 256K tokens on the 12B and larger models (128K on the edge-sized E2B/E4B) for processing long document and tool outputs Extended Multimodality : Gemma 4 models can process text,voice and images simultaneously like extracting data from charts, analyzing screenshots , and reviewing UI mockups. Gemma 4 Model Variants & SpecificationsGemma 4 Architecture Gemma 4 comes in five model sizes built around four architectural variants, each making different trade-offs between performance, inference speed, compute, and memory. Gemma4 Unified 12B vs Effective Parameters Effective-parameter models (E2B and E4B) are dense transformer models optimized for edge and on-device deployment. The "E" stands for effective parameters use Per-La ## Build intelligent Android apps: On-device inference DevFeed: [Build intelligent Android apps: On-device inference]() Original publisher: [Read original article]() Author: Android Developers (noreply@blogger.com) Published: 2026-07-21T13:00:00Z Content type: article Language: en Sources: [Android Developers Blog]() Topics: [Android](), [On-device AI](), [ML Kit](), [Inference](), [gemma4](), [Google](), [Mobile]() Tags: [android](), [efficiency](), [features](), [gemini](), [gemma](), [gemma-4](), [google](), [inference](), [ml-kit](), [on-device](), [speed]() ### AI overview This Android developer article explains how to use Gemini Nano through ML Kit's Prompt API to build on-device features in the Jetpacker demo app. It covers itinerary summarization, expense management, and voice notes, emphasizing local processing, privacy, offline reliability, and avoiding cloud inference costs. The article reports that prompt iteration reduced response time from 13 seconds to under 2 seconds. ### Source excerpt Posted by Caren Chang, Developer Relations Engineer, Android Developer Relations Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we introduced Jetpacker, the demo app we'll use throughout this series. In this blog post, we will share how you can use Gemini Nano through ML Kit's Prompt API to build intelligent on-device features. Building intelligent on-device features refers to the ability to process prompts and data directly on a device without sending data to a server. This offers a few advantages: User data can be processed locally on the device, preserving user privacy Functionality of the model is reliable even with spotty or no internet connection No additional cloud inference cost, since everything runs on the user's hardware With the benefits of on-device in mind, we identified three features to add in Jetpacker that can improve the user experience: summarizing trip itineraries, managing expenses, and capturing voice notes. On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes High quality tailored summarization of short texts The itinerary screen gives users a quick overview of all activities for a given trip. Since this screen contains a lot of information, it can quickly become overwhelming. To help users prepare without feeling overwhelmed, we can add a 'Get ready for your trip' section at the top. The romantic Paris trip is summarized as a classic Parisian adventure blending art, sights, and delicious food. A tip and some useful phrases are also added. By inputting a trip itinerary and asking an LLM to summarize it, we can generate a quick summary of the trip along with packing tips and useful local phrases. This is a great use case for an on-device model for several reasons: Performance and quality: Both the input and output text are relatively short. With tha ## Hugging Face and Cerebras bring Gemma 4 to real-time voice AI DevFeed: [Hugging Face and Cerebras bring Gemma 4 to real-time voice AI]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti; Leandro von Werra; Saurabh Vyas Published: 2026-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [voice ai](), [gemma4](), [hugging face](), [Artificial Intelligence](), [Open Source](), [real-time](), [Low Latency](), [Inference](), [Conversational AI](), [Language models](), [systems]() Tags: [ai](), [audio](), [gemma](), [hugging-face](), [inference](), [inference-providers](), [latency](), [low-latency](), [model](), [models](), [multimodal](), [open-source](), [open-source-collab](), [partnerships](), [performance](), [real-time](), [systems](), [text-to-speech](), [voice](), [voice-ai]() ### AI overview Hugging Face and Cerebras present a real-time speech-to-speech pipeline that combines Cerebras inference, Google DeepMind's Gemma 4 31B language model, and Qwen text-to-speech. The modular, open architecture is designed for low latency, predictable performance, and adaptable voice experiences across assistants, robots, products, and research projects. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing Gemma 4 12B: a unified, encoder-free multimodal model DevFeed: [Introducing Gemma 4 12B: a unified, encoder-free multimodal model]() Original publisher: [Read original article]() Author: Olivier Lacombe Published: 2026-06-09T14:10:19Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [gemma4](), [multimodal](), [Language models](), [moe](), [Hardware](), [Latency]() Tags: [apache](), [community](), [gemma](), [laptop](), [latency](), [llm](), [mixture-of-experts-moe](), [multimodal](), [none]() ### AI overview Gemma 4 12B is an encoder-free multimodal model designed to deliver agentic intelligence locally on laptops. It combines native vision and audio processing, near-26B benchmark performance, a reduced memory footprint, and Apache 2.0 licensing. ### Source excerpt An overview of Gemma 4 12B, a model designed to bring high-performance multimodal intelligence directly to your laptop. ## System Over Model, Tested: Reproducing Mythos's FreeBSD Find on Local Open-Weight Models DevFeed: [System Over Model, Tested: Reproducing Mythos's FreeBSD Find on Local Open-Weight Models]() Original publisher: [Read original article]() Author: clearbluejar Published: 2026-06-04T07:00:00Z Content type: article Language: en Sources: [clearbluejar]() Topics: [freebsd](), [gpt-oss](), [Artificial Intelligence](), [gemma4](), [bug](), [Large Language Model](), [Benchmark](), [Python]() Tags: [ai](), [benchmark](), [cve](), [false-positives](), [freebsd](), [gemma-4](), [gpt-oss](), [llm](), [llms](), [python](), [rce](), [vulnerability-research]() ### AI overview This article tests AISLE's vulnerability-finding pipeline on the local open-weight models gpt-oss-20b and gemma-4-31b-it using a FreeBSD RCE as the benchmark. The models initially appeared to miss the bug, but a rerun recovered it. The author reports that adding a reachability stage reduced false positives from 30 to 5 while retaining the CVE finding. ### Source excerpt Mythos found a 17-year-old FreeBSD RCE; AISLE reproduced it with gpt-5.4-nano via their nano-analyzer pipeline. I ran the pipeline on two local open-weight models, gpt-oss-20b and gemma-4-31b-it. The misses recovered on re-run. The real problem was the false-positive rate, and one extra system stage cut it from 30 to 5 with the CVE still standing. ## Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale DevFeed: [Use Your Mac for AI Agents: Self-Host Gemma 4 12 B with Pulumi and Tailscale]() Original publisher: [Read original article]() Author: Pablo Seibelt Published: 2026-06-04T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [gemma4](), [llama.cpp](), [Local AI](), [Infrastructure as code](), [macOS](), [On-device AI](), [multimodal]() Tags: [ai](), [download](), [gemma-4](), [gemma4](), [gpu](), [infrastructure-as-code](), [kubernetes](), [llama-cpp](), [macos](), [multimodal](), [python](), [tailscale](), [tutorials]() ### AI overview A tutorial for self-hosting Gemma 4 12 B on a modern Mac using llama.cpp with Apple Metal acceleration. It combines host-native inference with a local Kubernetes cluster, Pulumi infrastructure as code, and Tailscale for secure access, and reports validation results on a MacBook Pro with an Apple M3 Max and 36 GB RAM. ### Source excerpt If you run AI tools and agents, you've probably accepted three tradeoffs: your data leaves your network, you can't work offline, and your bill scales with usage. Open-weight models now run well on consumer hardware. Once the model is on your machine, your data stays local, inference works offline, and tokens cost nothing. If you own a modern Mac, you can run a high-quality model yourself. Gemma 4 is an open-weights model family from Google. This post focuses on Gemma 4 12 B, released in June 2026, using Unsloth's Q8_0 GGUF. The 12 B model fits comfortably on a modern Mac while leaving enough headroom for local llama.cpp and a chat UI. We'll use llama.cpp for host-native inference, k3d for a local Kubernetes cluster, Pulumi for infrastructure as code, and Tailscale for secure access. Prerequisites This setup was validated on the following hardware: macOS 26 Tahoe, version 26.5 MacBook Pro with Apple M3 Max 36 GB RAM On this machine, llama.cpp reported about 20 output tokens per second for a 160-token validation response with unsloth/gemma-4-12b-it-GGUF, gemma-4-12b-it-Q8_0.gguf, and a 131,072-token context. Sustained throughput varies by prompt length, thermal state, and llama.cpp settings. You'll need brew, docker, pulumi, and tailscale installed. We'll also install k3d during the process. Run Gemma 4 with host-native llama.cpp We use llama.cpp directly on macOS to leverage Apple Metal acceleration. Running the LLM on the host is more efficient than trying to pass GPU access into a local Kubernetes VM. Install the build tools: brew install cmake git Then build llama.cpp from source and download the multimodal projector. In validation, Homebrew llama.cpp 9430 could run text inference, but it could not load the new Gemma 4 12 B projector and failed with unknown projector type: gemma4uv. Building current llama.cpp from source fixed that. llm_home="$HOME/pulumi-gemma4-llm" mkdir -p "$llm_home/models" "$llm_home/logs" if [ ! -d "$llm_home/llama.cpp/.git" ]; then git clon ## How I Run My Daily Standup on a Local LLM: Git, Gemma 4, llama.cpp and Telegram DevFeed: [How I Run My Daily Standup on a Local LLM: Git, Gemma 4, llama.cpp and Telegram]() Original publisher: [Read original article]() Author: Ivan Morgillo Published: 2026-05-27T09:51:06Z Content type: tutorial Language: en Sources: [Ivan Morgillo]() Topics: [gemma4](), [llama.cpp](), [Git](), [Large Language Model](), [API](), [Claude]() Tags: [anthropic](), [api](), [claude](), [gemma](), [gemma-4](), [git](), [llama-cpp](), [llm](), [local](), [local-llm](), [sdk](), [telegram]() ### AI overview A developer describes replacing a cloud-based daily standup pipeline with a local Gemma 4 E4B model running on llama.cpp. The pipeline reads Git commits, formats them, and posts the summary to Telegram. The article also explains how empty API-key settings caused several automations to use personal Claude subscription authentication instead of the Anthropic API. ### Source excerpt A real-world pipeline that scans my Git repos every morning, formats the day's commits with a local Gemma 4 E4B on llama.cpp, and posts the summary to Telegram -- no cloud, no API key, no AUP warnings. ## Reachy Mini goes fully local DevFeed: [Reachy Mini goes fully local]() Original publisher: [Read original article]() Author: Amir Mahla; Andres Marafioti Published: 2026-05-27T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [reachy](), [gemma4](), [llama.cpp](), [Large Language Model](), [speech-to-speech](), [hugging face](), [WebSocket]() Tags: [api](), [artificial-intelligence](), [audio](), [blog](), [gemma](), [guide](), [how-to](), [llama](), [llama-cpp](), [llm](), [local](), [local-ai](), [open-source](), [reachy](), [robotics](), [server](), [speech](), [speech-to-speech]() ### AI overview A tutorial for running fully local conversations with a Reachy Mini robot. It describes a cascaded VAD, speech-to-text, LLM, and text-to-speech pipeline using llama.cpp with Gemma 4, Silero VAD, Parakeet-TDT STT, and Qwen3-TTS, connected through a Realtime API-compatible WebSocket. The setup avoids cloud services, API keys, and sending data off the local machine. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How to Use Transformers.js in a Chrome Extension DevFeed: [How to Use Transformers.js in a Chrome Extension]() Original publisher: [Read original article]() Author: Nico Martin Published: 2026-04-23T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Chrome extension](), [transformers.js](), [gemma4](), [Artificial Intelligence](), [Local AI](), [Open Source](), [Code]() Tags: [artificial-intelligence](), [chrome](), [chrome-extension](), [code](), [extension](), [github](), [guide](), [inference](), [javascript](), [local](), [local-ai](), [manifest](), [on-device](), [open](), [open-source](), [transformers](), [transformers-js](), [ui](), [web]() ### AI overview A developer guide to building a local AI Chrome extension with Transformers.js under Manifest V3. It explains an architecture with a background service worker hosting models, a side-panel chat interface, and a content script for page extraction and highlighting, using the Gemma 4 Browser Assistant as a reference. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How to Enable Gemma 4 Thinking Mode in LM Studio and OpenCode DevFeed: [How to Enable Gemma 4 Thinking Mode in LM Studio and OpenCode]() Original publisher: [Read original article]() Published: 2026-04-08T15:10:00Z Content type: tutorial Language: en Sources: [Antonio Leiva]() Topics: [gemma4](), [Chat Bot]() Tags: [ai](), [gemma-4](), [lm-studio](), [local-llms](), [opencode](), [reasoning]() ### AI overview A tutorial on configuring Gemma 4 26B's thinking mode for local use with LM Studio and OpenCode. It explains that successful setup depends on reasoning parsing, the prompt template, model support, and message formatting across the involved tools. ### Source excerpt Everything Android, Kotlin and other random topics ## Gemma 4: Byte for byte, the most capable open models DevFeed: [Gemma 4: Byte for byte, the most capable open models]() Original publisher: [Read original article]() Author: Clement Farabet Published: 2026-04-02T16:00:49Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [gemma4](), [gemma](), [Artificial Intelligence](), [Mixture of Experts (MoE)](), [multimodal](), [Low Latency](), [Android]() Tags: [agentic](), [ai](), [android](), [apache](), [gemma](), [latency](), [low-latency](), [mixture-of-experts-moe](), [models](), [multimodal](), [none](), [on-device](), [open](), [performance](), [products](), [reasoning](), [research](), [tools](), [workflows]() ### AI overview Gemma 4 is a family of open models designed for advanced reasoning and agentic workflows. It includes E2B, E4B, 26B Mixture of Experts, and 31B Dense variants, with an Apache 2.0 license and designs optimized for hardware ranging from Android devices and laptop GPUs to developer workstations and accelerators. ### Source excerpt Gemma 4: Our most intelligent open models to date, purpose-built for advanced reasoning and agentic workflows. ## Welcome Gemma 4: Frontier multimodal intelligence on device DevFeed: [Welcome Gemma 4: Frontier multimodal intelligence on device]() Original publisher: [Read original article]() Author: merve; Pedro Cuenca; Sergio Paniego; ben burtenshaw; Steven Zheng; Alvaro Bartolome; Nathan Habib Published: 2026-04-02T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gemma4](), [multimodal](), [Open Source](), [long-context](), [quantization](), [llama.cpp](), [MLX](), [Rust](), [webgpu]() Tags: [audio](), [benchmarks](), [fine-tuning](), [gemma](), [gemma4](), [generation](), [google](), [guide](), [llama](), [llama-cpp](), [local](), [long-context](), [mlx](), [models](), [moe](), [multimodal](), [on-device](), [open](), [open-source](), [quantization](), [science](), [speed]() ### AI overview Gemma 4 is presented as an open, Apache 2-licensed family of multimodal models supporting image, video, text, and, for selected variants, audio inputs. The article highlights on-device deployment, long-context support, compatibility across libraries and devices, quantization, benchmark results, and implementation options including llama.cpp, MLX, WebGPU, and Rust. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## MedGemma: Our most capable open models for health AI development DevFeed: [MedGemma: Our most capable open models for health AI development]() Original publisher: [Read original article]() Published: 2025-10-25T18:02:50Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [AI Development](), [Artificial Intelligence](), [multimodal](), [gemma](), [gemma4](), [Application Development](), [GPU](), [Hardware](), [Google]() Tags: [2025](), [ai](), [ai-development](), [classification](), [development](), [gemma](), [generation](), [google](), [gpu](), [hardware](), [health](), [healthcare](), [image](), [mobile](), [models](), [multimodal](), [open](), [product-development](), [research](), [retrieval]() ### AI overview Google Research announces two open models in the MedGemma collection for health AI development: MedGemma 27B Multimodal and MedSigLIP. The models support medical text and imaging tasks, including electronic health record interpretation, report generation, visual question answering, classification, and retrieval. ### Source excerpt We're announcing new multimodal models in the MedGemma collection, our most capable open models for health AI development.