Run Claude Code Free with Local Ollama + Gemma 4
Replace Anthropic's paid Claude API with Google's free Gemma 4 E2B model running locally via Ollama in Claude Code CLI—no API keys, zero costs, full privacy, works offline.
Local Architecture Powers Free Claude Code
Claude Code CLI acts as a car with swappable engines: normally powered by Anthropic's paid cloud API (Claude Opus/Sonnet), but you can plug in Ollama's local server running open-source models like Google's Gemma 4 E2B. Ollama downloads and serves models (e.g., Gemma, Llama, Qwen, Mistral) on localhost:11434, mimicking OpenAI-compatible APIs. Gemma 4 E2B (7.2GB, runs on 8GB RAM, 128K context window, multimodal for images) uses Gemini 3 research under Apache 2.0 license—full commercial use, no restrictions. Its 31B dense variant ranks #3 on Arena AI leaderboard, beating DeepSeek and Qwen. Swap keeps Claude Code's file reading, tool calling, terminal commands, and codebase management, but routes requests locally instead of cloud. Gains: zero cost, data privacy (nothing leaves your machine), no rate limits, offline use, no vendor lock-in.
Essential Setup Delivers Production-Ready Local AI Dev
Download Ollama from ollama.com (Mac/Windows/Linux). Pull Gemma 4 E2B: ollama run gemma2e2b (downloads ~7.2GB). Test in terminal: chat confirms thinking process and responses (e.g., "capital of US is Washington, D.C."). Critical: Set context window to 65,536 tokens (ollama context-length 65536)—default is too small for Claude Code to read files, plan, and code effectively; skipping causes crashes or garbage. In Cursor/VS Code/any IDE terminal (or standalone): ollama launch claude-code, select gemma2e2b. No Anthropic API key needed—auto-configures. Switch models anytime: /model gemma2e2b or larger like 26B. Example: "Break down Claude.md file"—reads, analyzes locally. Handles simple/medium tasks like functions, features, scaffolding.
Speed and Complexity Tradeoffs Guide Smart Hybrid Use
Local E2B lags cloud (30s-5min per complex response vs. seconds), especially on laptops; hardware dictates speed. Excels for learning, side projects, prototyping where token costs matter—keeps API bills at zero. Struggles with multi-file debugging across 10+ files (smaller effective context vs. Claude Opus 3.5 Sonnet 3.5), lacking tool choice, prompt caching, URL images. Hybrid wins: local for daily/low-stakes coding, paid API for production-scale codebases. Troubleshoot installs/errors by prompting local models (Claude/ChatGPT). Runs on phones too (no net/airplane mode).