Ever wanted the power of AI coding assistants like Claude Code or Cursor, but without sending your code to external servers? In March 2026, running a fully functional AI coding assistant locally has never been easier or more practical. This comprehensive guide walks you through setting up Ollama with Continue.dev for a free, private, and powerful local AI development environment.

Why Local AI Coding Matters in 2026

The AI coding assistant landscape has exploded, with tools like Claude Code, Cursor, and GitHub Copilot dominating the market. But there's a growing concern among developers: you're essentially uploading your code to third-party servers when using these cloud-based solutions.

For developers working on:

• Proprietary commercial projects with strict NDAs
• Sensitive codebase requiring data localization
• Personal projects where privacy matters
• Learning environments without internet dependencies

Local AI coding provides a compelling alternative. According to a January 2026 survey by Developer Economics, 34% of developers now express concern about code privacy, up from 18% in 2024.

What You'll Need

Before we dive in, here's what you'll need:

• Mac, Linux, or Windows PC with at least 16GB RAM (32GB recommended)
• Modern GPU (optional but recommended for faster inference)
• VS Code or JetBrains IDE as your editor
• Ollama - the open-source runtime for running AI models locally
• Continue.dev - VS Code extension for AI-assisted coding

Step 1: Installing Ollama

Ollama has become the de facto standard for running large language models locally. As of March 2026, it supports over 100 models including Llama 3.3, Mistral, CodeLlama, and DeepSeek models.

Installation on macOS

# Open Terminal and run the installation command
curl -fsSL https://ollama.com/install.sh | sh

Installation on Linux

# For Ubuntu/Debian
curl -fsSL https://ollama.com/install.sh | sh

Or install manually
sudo apt update
sudo apt install ollama

Installation on Windows

Windows users can either use WSL2 (Windows Subsystem for Linux) for the best experience, or download the Windows preview version directly from ollama.com.

Verifying Installation

After installation, verify Ollama is working:

ollama --version
Should output: ollama version 0.5.6 or later (March 2026)

Test with a simple model
ollama run llama3.3 "Hello, world!"

Step 2: Choosing the Right Model for Coding

Not all AI models are created equal for coding tasks. Here's a breakdown of the best models as of March 2026:

Recommended Models for Coding

Downloading Your Model

# For best overall coding performance (recommended)
ollama pull deepseek-coder2:16b

For faster performance on limited hardware
ollama pull codellama:7b

For the best quality (requires 32GB+ RAM)
ollama pull qwen2.5-coder:14b

The DeepSeek Coder 2 model released in February 2026 has quickly become the community favorite for local coding, offering GPT-4 level code generation at a fraction of the cost.

Step 3: Setting Up Continue.dev in VS Code

Continue.dev is a free, open-source VS Code extension that brings AI assistance to your editor using local or remote models.

Installation

1. Open VS Code
2. Go to Extensions (Cmd/Ctrl + Shift + X)
3. Search for "Continue"
4. Click Install

Configuration

After installation, you'll need to configure Continue to use your local Ollama instance:

1. Click the Continue icon in your VS Code sidebar
2. Click the gear icon to access settings
3. Select "Add Ollama" as your provider
4. Choose your downloaded model (deepseek-coder2:16b recommended)

Your config.json should look something like:

{
  "models": [
    {
      "model": "deepseek-coder2:16b",
      "provider": "ollama",
      "title": "Local DeepSeek"
    }
  ],
  "context": {
    "maximumTokens": 4096
  }
}

Step 4: Using Your Local AI Coding Assistant

Now comes the fun part - using your local AI coding assistant effectively!

Basic Interactions

• Cmd+L (Mac) / Ctrl+L (Windows/Linux): Open chat panel
• Cmd+I (Mac) / Ctrl+I (Windows/Linux): Highlight code and press for inline editing
• Tab: Accept AI code completions

Practical Examples

Example 1: Explaining Code

Highlight any code in your editor and ask: "Explain this function"

# Ask your AI to explain this
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

The local model will explain: This is a recursive function that calculates the nth Fibonacci number. It has O(2^n) time complexity due to repeated calculations...

Example 2: Writing New Code

Ask: "Write a Python function that reads a CSV file and returns a dictionary"

import csv

def csvtodict(filename):
    result = {}
    with open(filename, 'r') as f:
        reader = csv.DictReader(f)
        for row in reader:
            result[row['id']] = row
    return result

Example 3: Debugging

Paste error messages and ask: "Debug this error"

The local AI can analyze stack traces, suggest fixes, and even write corrected code - all without your code leaving your machine.

Performance Optimization Tips

GPU Acceleration

If you have an NVIDIA GPU, enable CUDA for significantly faster inference:

# Set environment variable before running Ollama
export OLLAMAGPULAYERS=999
ollama serve

Quantization

For faster performance on limited hardware, use quantized models:

# 4-bit quantized models (faster, less accurate)
ollama pull codellama:7b-q40

8-bit quantized models (balanced)
ollama pull codellama:7b-q80

Memory Management

If you're running multiple applications, close unused apps to free RAM for your AI model. The 16B models work best with at least 16GB system RAM available.

Comparing Local vs Cloud AI Coding

Here's a practical comparison to help you decide:

Troubleshooting Common Issues

Issue: Model Won't Download

# Check your Ollama version
ollama --version

Pull with explicit version
ollama pull deepseek-coder2:16b --verbose

Issue: Slow Performance

• Ensure you have sufficient RAM
• Use quantized models (q40, q80)
• Enable GPU acceleration if available
• Close unnecessary applications

Issue: Continue Not Recognizing Ollama

# Make sure Ollama is running
ollama serve

Check if Ollama is accessible
curl http://localhost:11434/api/tags

Advanced: Using Ollama with Other IDEs

JetBrains (IntelliJ, PyCharm, WebStorm)

Use the "Continue" plugin for JetBrains or the "Ollama" plugin:

1. Go to Settings > Plugins
2. Search for "Continue" or "Ollama"
3. Configure to connect to localhost:11434

Neovim

For Neovim users, the "codeium" and "copilot.lua" plugins can work with local Ollama models via custom configuration.

The Future of Local AI Coding

The local AI coding movement is gaining momentum. With models like DeepSeek Coder 2 (released February 2026) achieving near GPT-4 performance, and Ollama's infrastructure maturing, we're seeing a shift toward privacy-conscious development.

Major developments to watch in 2026:

• Smaller, more capable models optimized for consumer hardware
• Better integration with popular IDEs and editors
• Improved inference speeds making real-time coding assistance viable locally
• Enterprise adoption of local AI for sensitive projects

Conclusion

Setting up local AI coding with Ollama is one of the most practical upgrades you can make to your development workflow in 2026. Whether you're concerned about code privacy, want to save on subscription costs, or simply want a reliable offline coding assistant, Ollama + Continue.dev delivers.

The setup takes less than 30 minutes, works on hardware you likely already own, and provides genuine value for everyday coding tasks. Give it a try - your code (and your privacy) will thank you.

Ready to get started? Download Ollama at ollama.com and join the local AI coding revolution!