Run Claude Code Free Locally via Ollama & Gemma 4
Use Ollama to serve Google's open-source Gemma 4 E2B model locally as a free, private engine for Anthropic's Claude Code CLI—no API keys, subscriptions, or data leaving your machine.
Swap Cloud Engine for Local Gemma 4 in Claude Code
Ollama acts like Docker for AI models: download and run them locally with one command, exposing an API at localhost:11434. Pair it with Google's Gemma 4 family—Apache 2.0 licensed for commercial use, fine-tuning, and products. The E2B variant (7.2GB download, runs on 8GB RAM) delivers 128K context window and multimodal image handling, ranking #3 on Arena AI leaderboard among open models (beats DeepSeek, Qwen). Claude Code, Anthropic's CLI for reading codebases, writing code, running terminal commands, and managing files, normally hits paid cloud APIs. Redirect it to Ollama's local endpoint for identical features (file reading, tool calling) but zero cost, full privacy, offline operation, no rate limits, and no vendor lock-in. Tradeoffs: responses take 30 seconds to 1+ minute (vs. cloud seconds), smaller effective context limits multi-file reasoning across 10+ files, and lacks frontier capabilities like Opus/Sonnet for complex debugging.
Exact Setup for Zero-Cost Coding Assistant
Download Ollama from ollama.com for Mac/Windows/Linux and install via installer or terminal. Pull Gemma 4 E2B: ollama run gemma2:2b (downloads ~7.2GB; use larger E4B/26B/31B on beefier hardware). Test with a prompt like "capital of the United States?" to confirm (expect visible thinking steps). Critical step: set context window to 65,536 tokens (ollama context_length 65536) after quitting Ollama app—Claude Code crashes or fails without it for file reading/planning. In your project directory (e.g., Cursor/VS Code terminal), run ollama launch claude, select gemma2:2b. No Anthropic API key needed; auto-configures local endpoint. Switch models anytime (model gemma2:27b). Example: "Break down Claude.md" reads and summarizes files, though slower on complex tasks.
Best Use Cases and Hybrid Strategy
Ideal for learning, side projects, quick prototyping, simple/medium tasks (functions, features, scaffolding)—saves token costs without watching bills. Avoid for production debugging over huge codebases; revert to paid API there. Hybrid approach maximizes value: local for daily/low-stakes work, cloud for heavy lifts. Runs on phones too (no internet), making it viable for mobile prototyping. If errors arise (e.g., port in use), query local models or cloud LLMs for fixes—no excuses with AI helpers available.