How to Run CodeLlama Locally for Private Code Completion

Running CodeLlama locally gives you powerful code completion capabilities while keeping all your code completely private. This guide walks through the complete setup process, from choosing your hardware to integrating the model with your development environment.

Why Run CodeLlama Locally

When you use cloud-based code completion tools, your code travels to external servers for processing. For proprietary projects, regulated industries, or any work under NDA, this creates compliance concerns. Running CodeLlama locally processes everything on your machine—no data ever leaves your environment.

CodeLlama comes in several sizes: 7B, 13B, and 34B parameters. The smaller models run well on consumer hardware, while the larger model requires more strong GPU resources but provides better completion quality.

Hardware Requirements

For an usable local code completion experience, aim for these minimum specifications:

7B model: 8GB+ RAM, integrated graphics (Apple Silicon works well) or any modern GPU with 6GB VRAM
13B model: 16GB RAM, GPU with 8GB+ VRAM recommended
34B model: 32GB+ RAM, GPU with 16GB+ VRAM required

Apple Silicon Macs handle the 7B and 13B models surprisingly well using Metal acceleration. NVIDIA GPUs on Linux or WSL2 offer the most flexibility for all model sizes.

Prerequisites

Before you begin, make sure you have the following ready:

A computer running macOS, Linux, or Windows
Terminal or command-line access
Administrator or sudo privileges (for system-level changes)
A stable internet connection for downloading tools

Step 1: Install Ollama

Ollama is the easiest way to run CodeLlama locally. It handles model downloading, inference, and provides a simple API.

macOS Installation

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

Linux Installation

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

Windows Installation

Download the installer from ollama.com or use WSL2 with the Linux installation method.

Verify the installation:

ollama --version

Step 2: Downloading the CodeLlama Model

Pull the model that matches your hardware capabilities:

# For 7B model (smallest, fastest)
ollama pull codellama:7b

# For 13B model (balanced)
ollama pull codellama:13b

# For 34B model (best quality, requires powerful GPU)
ollama pull codellama:34b

The 7B model downloads approximately 4GB, while the 13B model requires around 8GB. Initial download time depends on your internet connection.

Step 3: Test CodeLlama in the Terminal

Once installed, test the model directly:

ollama run codellama:7b

Type a code-related query to verify functionality:

>>> Write a Python function that calculates factorial

The model should respond with a working implementation. Press Ctrl+D or type /exit to quit.

Step 4: Integrate with Your Code Editor

For real-time code completion in your IDE, you have several integration options.

Option 1: VS Code with Ollama Extension

Install the Ollama extension for VS Code
Restart VS Code
The extension automatically connects to your local Ollama instance
Start typing code—the extension provides inline completions

Option 2: Continue Extension

Continue is a VS Code extension specifically designed for local code completion:

Install Continue from the VS Code marketplace
Configure it to use your local Ollama endpoint
Adjust the model in settings to codellama:13b or your preferred size

Option 3: LM Studio

LM Studio provides a GUI alternative:

Download from lmstudio.ai
Search for CodeLlama and click Download
Select the Chat tab to use it as a coding assistant
The app also provides an OpenAI-compatible local server

Step 5: Configure Code Completion Settings

Fine-tune your setup for optimal results.

Adjusting Context Window

CodeLlama supports a context window of up to 16,000 tokens. In Ollama, you can adjust this:

ollama run codellama:13b --verbose

This shows detailed output useful for debugging.

Setting Temperature and Parameters

For code completion, lower temperature values produce more predictable results:

# Create a modified model with lower temperature
ollama create codellama-code -f ./Modelfile

Create a Modelfile with:

FROM codellama:13b
PARAMETER temperature 0.2
PARAMETER top_p 0.9

Then run:

ollama run codellama-code

Performance Optimization

Get the most out of your local setup with these optimizations.

GPU Acceleration

Ollama automatically uses GPU acceleration when available. On macOS, Metal provides significant speedups. On Linux with NVIDIA, ensure CUDA drivers are installed:

nvidia-smi

This confirms your GPU is recognized. Ollama automatically routes inference through CUDA.

CPU-Only Mode

If GPU memory is limited, force CPU inference:

OLLAMA_HOST=0.0.0.0:11434 ollama serve

Then adjust your client settings to use CPU-only mode when needed.

Memory Management

Monitor memory usage during inference:

# On macOS
top -l 1 | grep -i ollama

# On Linux
free -h

If you experience slowdowns, try a smaller model or close other applications.

Troubleshooting Common Issues

Model Not Starting

If the model fails to load, ensure sufficient system memory:

# Check available memory
vm_stat | grep Pages

Close browser tabs and other memory-intensive applications before running CodeLlama.

Slow Completion Speed

Slow speeds typically indicate CPU-only mode on a system with GPU available. Reinstall or update Ollama to ensure GPU acceleration is enabled. For Apple Silicon, verify Metal is active in system preferences.

Connection Refused Errors

Ollama runs on port 11434 by default. Check if it’s running:

curl http://localhost:11434/api/generate -d '{
  "model": "codellama:7b",
  "prompt": "test"
}'

If this fails, restart the Ollama service:

pkill ollama
ollama serve

Step 6: Security Benefits

Running locally provides security advantages cloud services cannot match. Your code never traverses networks, eliminating interception risks. There are no third-party data retention policies to review. Compliance becomes simpler since data processing stays within your infrastructure.

This setup particularly suits healthcare developers handling HIPAA data, financial teams managing PCI requirements, and anyone working under strict NDAs.

Next Steps

With CodeLlama running locally, explore fine-tuning options for specialized domains. You can also experiment with different model sizes based on your current task—use 7B for quick autocomplete and switch to 34B for complex code generation.

Built by theluckystrike — More at zovo.one