Lightweight Local LLM Comparison on Mac mini with Ollama
Published:Jan 2, 2026 16:47
•1 min read
•Zenn LLM
Analysis
The article details a comparison of lightweight local language models (LLMs) running on a Mac mini with 16GB of RAM using Ollama. The motivation stems from previous experiences with heavier models causing excessive swapping. The focus is on identifying text-based LLMs (2B-3B parameters) that can run efficiently without swapping, allowing for practical use.
Key Takeaways
- •Focus on identifying lightweight LLMs (2B-3B parameters) for efficient operation on a 16GB Mac mini.
- •Addresses the issue of swapping encountered with larger models.
- •Serves as a preliminary step before evaluating image analysis models.
Reference
“The initial conclusion was that Llama 3.2 Vision (11B) was impractical on a 16GB Mac mini due to swapping. The article then pivots to testing lighter text-based models (2B-3B) before proceeding with image analysis.”