Running Claude Code Locally for Free with Ollama

Introduction

Claude Code, developed by Anthropic, has quickly gained popularity among developers for its advanced reasoning, contextual awareness, and strong coding capabilities. It assists with:

• Code generation and refactoring
• Debugging and error analysis
• Documentation writing
• Multi-language translation
• Architecture reasoning

Its strength lies in producing structured, safe, and contextually coherent code suggestions. As a result, many developers consider it one of the most capable AI coding assistants available today.

However, Claude Code is primarily cloud-based and usage often involves subscription tiers or usage-based billing. For developers working on large projects, frequent experimentation, or limited budgets, recurring API costs can become a constraint.

This article explains how to run Claude Code locally using Ollama - allowing you to leverage powerful models (such as Qwen3-Coder-32B) or lighter models for lower-spec machines - without incurring cloud costs.

Why Run Claude Code with Ollama?

Running Claude Code with Ollama provides:

• Zero cloud usage fees
• Full local data privacy
• Flexibility to choose models
• Offline capability
• Control over performance and hardware usage

However, there are trade-offs, which we will also cover.

System Requirements Considerations

Before proceeding, understand the hardware implications.

High-End Models (e.g., Qwen3-Coder-32B)

• Require high VRAM GPUs (16GB+ recommended)
• Significant RAM usage
• Best suited for dedicated AI workstations

Medium Models (7B-14B)

• Moderate GPU or strong CPU systems
• Balanced performance and resource usage

Lightweight Models (1B-4B)

• Suitable for laptops
• Can run on CPU-only systems
• Lower reasoning depth but efficient

Your hardware determines which model tier is practical.

Installation Guide

We will cover:

1. Installing Claude Code
2. Installing Ollama
3. Pulling models
4. Connecting Claude Code to Ollama
5. Running selected models

All instructions apply to macOS, Linux, and Windows.

Step 1: Install Claude Code (Official Method)

Always use the official installation source.

Visit: https://claude.ai

Or use the official installer scripts below.

macOS / Linux / WSL

    curl -fsSL https://claude.ai/install.sh | bash

This installs the Claude CLI and sets up the claude command in your terminal.

Verify installation:

    claude --version

Windows (PowerShell)

Open PowerShell and run:

    irm https://claude.ai/install.ps1 | iex

If execution policy blocks the script:

    Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Then re-run the install command.

Verify:

    claude --version

Step 2: Install Ollama

Navigate to the official website:

https://ollama.com

Download the installer for your operating system and follow the instructions.

Ollama provides native installers for:

• macOS
• Linux
• Windows

After installation, confirm:

    ollama --version

Ollama runs a local model server at:

http://localhost:11434

Step 3: Pull a Model with Ollama

Once installed, pull your desired model.

High-End Model Example

    ollama pull qwen3-coder:32b

Medium Model Example

    ollama pull qwen3-coder:7b

Lightweight Model Example

    ollama pull qwen2.5-coder:3b

To see available models:

    ollama list

Step 4: Start Ollama Server

If not already running:

    ollama serve

This launches the local API server.

Step 5: Configure Claude Code to Use Ollama

Claude Code communicates with model providers using an Anthropic-compatible API.

To redirect it to Ollama, configure environment variables.

macOS / Linux / WSL

    export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""
export ANTHROPIC_BASE_URL=http://localhost:11434

Windows (PowerShell)

    $env:ANTHROPIC_AUTH_TOKEN="ollama"
$env:ANTHROPIC_API_KEY=""
$env:ANTHROPIC_BASE_URL="http://localhost:11434"

These settings instruct Claude Code to use the local Ollama server instead of Anthropic's cloud.

Step 6: Run Claude Code with a Selected Model

Now start Claude using your local model:

    claude --model qwen3-coder:32b

Or for a lighter system:

    claude --model qwen3-coder:7b

Claude Code will now route requests to Ollama running locally.

Performance Recommendations

For High-End Systems

• Use 32B models
• Ensure GPU acceleration is enabled
• Monitor memory usage

For Mid-Range Systems

• Use 7B-14B models
• Consider quantized versions if available

For Low-End Systems

• Use 3B or smaller models
• Prefer CPU-friendly quantized builds

Disadvantages of Running Claude Code with Ollama

While this setup avoids pricing issues, it introduces trade-offs:

1. Hardware Dependency

Large models require powerful machines.

2. Feature Differences

Some cloud-specific optimizations and safety layers may not fully replicate locally.

3. Setup Complexity

Environment configuration requires manual setup.

4. Model Quality Variation

Open-weight models may not exactly match Claude's proprietary model performance.

Pricing Perspective

Cloud-based Claude Code usage may include:

• Subscription tiers
• Token-based billing
• Usage caps

For heavy users, this can scale significantly over time.

Running locally with Ollama:

• Eliminates token billing
• Provides unlimited local inference
• Shifts cost to hardware and electricity

For developers prioritizing cost control and privacy, local execution is highly attractive.

Final Thoughts

Running Claude Code with Ollama allows developers to:

• Use powerful coding models without cloud fees
• Maintain data privacy
• Choose models based on hardware capability
• Scale from lightweight laptop setups to high-performance AI workstations

While this approach requires initial configuration and hardware awareness, it provides long-term flexibility and cost efficiency.

For technical teams, researchers, and independent developers, combining Claude Code with Ollama creates a powerful local AI development environment that is both practical and financially sustainable.