Apple Releases Model Framework For Silicon Chips
Apple’s machine learning research team has released MLX, a framework designed for efficient and flexible machine learning on its silicon chips. MLX is inspired by frameworks like PyTorch, Jax, and ArrayFire, but differs by its unified memory model. Arrays in MLX live in shared memory, and operations can be performed on any supported device type without data copies.
Currently, the supported device types are the CPU and GPU. MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon. The Python API closely follows NumPy with a few exceptions, and MLX also has a fully featured C++ API that closely follows the Python API. Apple CEO Tim Cook recently revealed that the company is investing in AI and machine learning as fundamental technologies, and that they are integral to virtually every product they ship.
(With inputs from Shikha Singh)
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