MLX
Product information, use cases, and access for MLX.
Pricing information
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What is MLX?
MLX is an array framework for machine learning designed for Apple silicon. It gives developers a foundation for expressing model and numerical workloads, with a Python API that closely follows NumPy and a full C++ API with a similar overall shape. This makes it a practical fit for people who want familiar array-oriented interfaces while developing machine-learning software on supported Apple hardware.
Its programming model combines composable transformations for automatic differentiation, automatic vectorization, and computation-graph optimization with lazy evaluation. Arrays are materialized when they are needed, and operations can run on supported CPU or GPU devices while the arrays remain in shared unified memory. Developers can use these building blocks for experiments, model training, and inference-oriented projects, including the kinds of workflows represented by the official examples for neural networks and language-model inference.
Key features of MLX
NumPy-Like Interfaces
The Python API closely follows NumPy, while the framework also provides a fully featured C++ API with a similar structure. This suits developers who want familiar array-oriented interfaces for numerical and machine-learning code.
Composable Function Transformations
MLX provides composable transformations for automatic differentiation, automatic vectorization, and computation-graph optimization. These primitives help developers structure reusable transformations around numerical functions and machine-learning models.
Lazy Array Computation
Computations in MLX are lazy, so arrays are materialized when their values are needed rather than at every intermediate step. This model is useful for composing sequences of array operations while keeping evaluation explicit at the point where results are required.
Unified Memory and Multiple Devices
MLX uses a unified memory model in which arrays live in shared memory. Operations can run on supported device types without copying data between them, and the documentation currently identifies CPU and GPU as supported device types.