All Tutorials
Practical, no-fluff guides written for developers and curious tinkerers who want to run local AI without the headaches.
New to Gemma 4?
Start with a hardware check, then pick your setup path.
A detailed look at Gemma 4's benchmark results — from Arena AI rankings to MMLU, AIME, LiveCodeBench, and vision scores. See how Google's open models stack up against competitors with 600B+ parameters.
A complete guide to Gemma 4's technical architecture, core features, and practical use cases — from on-device mobile assistants to desktop workstation AI. Covers multimodal input, function calling, thinking mode, and deployment across phones, IoT, laptops, and desktops.
Build the ultimate budget-friendly local AI assistant: Gemma 4 running through OpenClaw on a Mac Mini. Step-by-step setup guide covering Ollama integration, OpenClaw configuration, multi-channel access, and cost comparison with cloud APIs.
A step-by-step guide to running Google's Gemma 4 as your local AI coding assistant in OpenCode on Mac Mini or Mac Studio. Covers model selection by memory, Ollama setup, OpenCode configuration, and optimization tips for daily use.
A fair, data-driven comparison of Google's Gemma 4 and Alibaba's Qwen 3.5 — covering benchmark scores, Arena AI rankings, hardware requirements, and practical differences for models you can run on laptops and desktop workstations.
A step-by-step guide to running Google's Gemma 4 locally using LM Studio — the desktop app with a built-in chat UI, model browser, and OpenAI-compatible API. No command line required.
Learn how to run Gemma 4 locally using Ollama — from the lightweight E2B edge model to the full 31B dense powerhouse. Covers setup, model selection, multimodal image input, API usage, and performance tips.
A clear breakdown of GPU memory, system RAM, and storage needed to run Gemma 4 on a laptop, desktop, or workstation — E4B, 26B MoE, and 31B dense — based on Google's official specs. Covers quantization, Apple Silicon (including Mac Mini), and NVIDIA GPU matching.
