Run a Coding Agent 100% Offline with Ollama: The Full Setup Guide

There are three kinds of engineers who need a fully local AI coding agent: those whose employers forbid sending code to cloud APIs, those who work offline (flights, field sites, unreliable connections), and those who simply refuse to pay per-token for something their own hardware can do. This guide covers all three — how Gonu Worker runs entirely on local Ollama models, what hardware you need, and which models are worth your VRAM.

Why Local Changes the Privacy Conversation

With cloud AI tools, “is my code private?” is answered with policies and promises. With local models, it’s answered with physics: the model runs on your machine, the weights are on your disk, and nothing leaves. For engineers at banks, healthcare companies, or defence contractors, this is often the difference between “not allowed to use AI” and “fully approved.” Gonu Worker treats Ollama as a first-class provider — the same agent that works with Anthropic or OpenAI switches to local models with one dropdown.

Setup in Three Steps

Step 1: Install Gonu Worker (free download — no account needed). Step 2: Open Settings → Local Models. Gonu auto-detects an existing Ollama install, or installs it for you, and lets you pull models from inside the app. Step 3:Pick your model in the model picker and start working. That’s the whole setup — no config files, no CLI flags.

Which Models Are Worth Running

Qwen3 14B is the current sweet spot for coding on consumer hardware — strong multi-file reasoning and tool use on a machine with 16GB+ RAM (or a GPU with 10GB+ VRAM). DeepSeek-R1 distills are the pick when you want visible chain-of-thought reasoning for tricky debugging, at the cost of slower responses. Gemma 3 smaller variants run comfortably on modest laptops and handle quick edits, explanations, and commit messages — a solid choice when RAM is tight. A practical pattern: run a small model for everyday inline work and pull a bigger one for gnarly refactors.

Honest expectation-setting: local models in this class are not Claude or GPT-tier at complex architecture decisions. They are genuinely good at the 80% of daily agent work — edits, tests, explanations, refactors, shell tasks — and they cost nothing per token. Gonu’s multi-model consensusalso lets you cross-check a local model’s answer against a cloud model only when it matters, keeping the sensitive bulk of your work local.

What Works Offline

With a local model selected, the coding agent, file explorer, Git panel, shell execution, screen capture analysis, and sub-agents all run without internet. Meeting features naturally need a network (the meeting is online), and Gonu Music/Video generation is cloud-based. But the core loop — point the agent at a repo, give it a task, review its diffs — is fully functional on a plane.

The Cost Math

A heavy AI coding user can easily burn through significant API credit monthly. With Ollama the marginal cost is zero — you pay only electricity. Combined with Gonu’s free plan (50K daily tokens for cloud models when you want them) and Pro at ₹499/month, the total cost of a serious agentic setup drops far below dollar-priced alternatives. See how the full stack compares in our Gonu Worker vs Cursor vs Copilot breakdown, or start with the AI coding agent guide.