5 Free AI Models You Can Run Locally Right Now
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 par
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:
- Install MonkeyCode: monkeycode-ai.net
- Start Ollama:
ollama serve - Configure MonkeyCode to use local model
- Start coding!
What's Your Favorite Local Model?
Let me know in the comments! 👇
ai #opensource #local #productivity
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