No API costs. No rate limits. No privacy concerns. Just you, your machine, and a model that thinks at the speed of flow. A complete setup guide for local AI-powered coding.

No API costs. No rate limits. No privacy concerns. Just you, your machine, and a model that thinks at the speed of flow.

The Problem with Cloud AI for Coding

You're deep in a coding session. You're in the zone. Then your AI assistant hits a rate limit, lags for 4 seconds, or you suddenly remember you just pasted a proprietary database schema into a third-party API.

Cloud-based LLMs are incredible — but for vibe coding, that fluid, almost meditative state of rapid prototyping and iterative thinking, they're not always the right tool. Latency breaks flow. Rate limits kill momentum. Privacy is a legitimate concern for professional codebases.

The solution? Run the model locally. This guide sets up your machine as a fully self-contained AI coding environment, for free, forever.

01 — Choosing Your Runner: Why Ollama Wins

Your "runner" is the software that loads model weights and serves them via a local API. The three main contenders are Ollama, LM Studio, and llama.cpp.

For vibe coding, Ollama wins. It exposes an OpenAI-compatible API at localhost:11434, which means every IDE plugin and chat UI that supports OpenAI can point straight at your local model — zero code changes required. It installs in one command and runs silently in the background.

02 — The Brain: Best Open-Weights Coding Models

Model choice depends on your hardware. Here's the current state-of-the-art landscape for coding:

For most developers on 16–32GB unified memory (Apple Silicon) or a mid-range NVIDIA GPU, DeepSeek-Coder-V2 16B hits the sweet spot — fast enough for conversational flow, smart enough for non-trivial problems.

💡 Apple Silicon tip: Unified memory is a superpower here. A MacBook Pro M3 Max with 64GB can run a 32B model entirely in memory with impressive throughput. No discrete GPU needed.

03 — The Interface: Your Vibe Coding Cockpit

The model running in the background is just the engine. You need a cockpit. Here are the three layers:

Continue.dev (VS Code / JetBrains)

The best open-source AI coding assistant for local LLMs. Inline autocomplete, a chat sidebar, slash commands, and full Ollama support out of the box. This is your primary coding interface.

Open WebUI

A self-hosted, ChatGPT-like web interface that connects to Ollama. Perfect for longer architecture brainstorming sessions, explaining complex problems, or rubber-ducking system design — without leaving your local environment.

Aider (CLI)

A terminal-based AI pair programmer that edits your actual files and is commit-aware. Exceptional for bulk refactoring, large-scale changes across multiple files, and keeping a clean git history of AI-assisted edits.

Recommended combo: Ollama in the background → Continue.dev in VS Code for in-editor flow → Open WebUI in a browser tab for architecture chats.

04 — Step-by-Step Setup Checklist

Step 1 — Install Ollama

Visit ollama.com or run:

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows: download the installer from ollama.com

Ollama runs as a background service on port 11434.

Step 2 — Pull your first model

# Fast and lightweight (good starting point)
ollama pull qwen2.5-coder:7b

# Balanced power and speed (recommended for most setups)
ollama pull deepseek-coder-v2:16b

# Maximum capability (requires 24GB+ VRAM or unified memory)
ollama pull qwen2.5-coder:32b

Step 3 — Test the model

ollama run qwen2.5-coder:7b
# Type a prompt. If you get a response, your runner is working.

Step 4 — Install Continue.dev in VS Code

Open VS Code → Extensions (Cmd+Shift+X) → search "Continue" → Install.

Continue will auto-detect your running Ollama instance.

Step 5 — Configure Continue

Open ~/.continue/config.json and add your model:

{
  "models": [
    {
      "title": "DeepSeek Coder",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5 Coder 7B",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b",
    "apiBase": "http://localhost:11434"
  }
}

Restart VS Code and hit Cmd+L (Mac) / Ctrl+L (Windows/Linux) to open the chat.

Step 6 — Install Open WebUI (optional, requires Docker)

docker run -d \
  -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  ghcr.io/open-webui/open-webui:main

Visit http://localhost:3000 and connect it to your Ollama instance.

Step 7 — Tune for speed

# Maximize GPU offloading (set in your shell profile)
export OLLAMA_NUM_GPU_LAYERS=-1

# Enable flash attention for faster inference (supported hardware)
export OLLAMA_FLASH_ATTENTION=1

On Apple Silicon, GPU offloading is automatic — no configuration needed.

Step 8 — Start vibe coding

Open a project in VS Code. Hit Cmd+L to open Continue. Ask it anything about your codebase. Feel the flow.

05 — Pro Tips for Maximum Performance

Use quantized models. A Q4_K_M quantized 14B model often runs faster than a Q8 7B model with comparable quality. You can specify the quantization level explicitly:

ollama pull qwen2.5-coder:14b-instruct-q4_K_M

Keep context windows tight. Shorter context = faster generation. In Continue, set "contextLength": 8192 unless you genuinely need more. Feeding 128K tokens to every autocomplete request will kill your latency.

Use a dedicated model per task. A small 3B model for tab-completion, a 16B model for chat. Continue supports multiple model configs and you can switch with a keyboard shortcut — this is one of its best features.

Pre-warm your model. On first load, models take a few seconds to initialize. Send a dummy request when your machine starts up to keep the model warm in memory.

The Vibe Is Yours to Own

Once this stack is running, you have a private, unlimited, cost-free AI coding environment that runs entirely on your hardware. No subscriptions. No outages. No one reading your code.

The future of AI-assisted development isn't just in the cloud — it's sitting on your desk, ready to go offline.