This technical overview evaluates the performance and implementation of running Large Language Models (LLMs) locally on Apple Silicon using MLX and llama.cpp. MLX, Apple's proprietary machine learning framework, is optimized specifically for Metal and unified memory architectures, making it a high-efficiency choice for M1 through M4 chips. Users can deploy quantized models via the mlx-lm Python package; for instance, a 4-bit quantized Llama-3.2-3B-Instruct model can be executed using the mlx_lm.generate command or integrated into Python applications via the load and generate functions. In contrast, llama.cpp utilizes the ggml ecosystem to provide broad cross-platform compatibility across CPU, GPU, CUDA, and Metal, making it ideal for non-Apple environments like Linux or Windows. While both tools support similar quantized weight families, MLX generally offers lower overhead and superior performance on Mac hardware. For deployment, MLX provides an OpenAI-compatible API through mlx_lm.server , whereas llama.cpp utilizes llama-server . The guide highlights that local execution ensures data privacy and eliminates per-token API costs, noting that a 3B parameter model operates effectively on systems with 16 GB of RAM.