Making 27B Models Practical: Quantization Strategies for Local Inference on Apple Silicon

As cloud LLM API costs rise, the ability to run capable 27B models locally on standard MacBooks becomes increasingly valuable. This guide demonstrates how quantization techniques and Apple's MLX framework can deliver practical inference performance on constrained 16GB hardware, enabling engineers to test code, prompts, and security workflows without cloud dependency. The trade-off model presented—accepting slower speeds and shorter contexts in exchange for privacy and cost savings—reflects a broader shift in how developers approach local AI development.