5 Free AI Models You Can Run Locally (No Cloud)

Why Run AI Locally?

• Privacy — Your data stays on your machine
• Speed — No network latency
• Cost — No API fees
• Offline — Works without internet

The Models

1. Llama 3 (Meta)

• Size: 8B, 70B parameters
• Use Case: General purpose
• Install: ollama pull llama3
• GitHub: github.com/meta-llama/llama3

2. Mistral (Mistral AI)

• Size: 7B parameters
• Use Case: Fast inference
• Install: ollama pull mistral
• GitHub: github.com/mistralai/mistral-src

3. CodeLlama (Meta)

• Size: 7B, 13B, 34B parameters
• Use Case: Code generation
• Install: ollama pull codellama
• GitHub: github.com/meta-llama/codellama

4. Phi-3 (Microsoft)

• Size: 3.8B parameters
• Use Case: Small but powerful
• Install: ollama pull phi3
• GitHub: github.com/microsoft/Phi-3

5. Gemma (Google)

• Size: 2B, 7B parameters
• Use Case: Efficient inference
• Install: ollama pull gemma
• GitHub: github.com/google/gemma

How to Get Started

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

# Pull a model
ollama pull llama3

# Run the model
ollama run llama3

Comparison

Model	Size	Speed	Quality	Best For
Llama 3	8B	Fast	Good	General
Mistral	7B	Fastest	Good	Speed
CodeLlama	7B	Fast	Good	Code
Phi-3	3.8B	Fastest	Good	Small devices
Gemma	2B	Fastest	Good	Efficiency

Integration with MonkeyCode

MonkeyCode supports local models through Ollama:

1. Install MonkeyCode: monkeycode-ai.net
2. Start Ollama: ollama serve
3. Configure MonkeyCode to use local model
4. Start coding!

What's Your Favorite Local Model?

Let me know in the comments! 👇

ai #opensource #local #productivity