MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, provided by the Apple Machine Learning Research team. Its Python API closely resembles NumPy, though some exceptions exist. MLX also boasts a complete C++ API that closely adheres to the Python API. Key differences between MLX and NumPy include composable function transforms, lazy computation, and multi-device support. MLX draws inspiration from frameworks like PyTorch, Jax, and ArrayFire. Unlike these frameworks, MLX utilizes a unified memory model. Arrays in MLX reside in shared memory, enabling operations to be performed on any supported device type (CPU, GPU, etc.) without data copying.