MLX

Overview

MLX is an open-source, NumPy-compatible array framework developed by Apple's machine learning research team, purpose-built for Apple silicon hardware. It provides a Python API that closely follows NumPy, along with a fully featured C++ API mirroring the Python interface. Unlike traditional frameworks, MLX adopts a unified memory model, where arrays reside in shared memory and can be operated on by the CPU or GPU without costly data copies. This design enables seamless execution across supported devices while retaining a simple and familiar developer experience.

The framework is built around several core principles: lazy computation, composable function transformations, and multi-device support. Computations are evaluated lazily, meaning arrays are only materialized when necessary, which allows for graph-level optimizations. MLX supports automatic differentiation, automatic vectorization, and compilation through composable transforms such as grad , vmap , and compile . It also offers a rich set of primitives, including operations for Linear Algebra, Fast Fourier Transforms, random number generation, quantization, and specialized fast kernels like scaled dot-product attention and rotary position embeddings.

MLX is designed for researchers and developers who need an efficient, flexible framework for building machine learning models on Apple hardware. It includes a comprehensive neural network library with common layers, loss functions, optimizers, schedulers, and distributed communication primitives. The documentation provides practical examples ranging from linear regression and multi-layer perceptrons to large-scale LLM inference, data parallelism, and tensor parallelism, showcasing MLX's capability to handle both experimental research and production-scale workloads.

Core capabilities

NumPy-compatible Python API

Follows NumPy conventions closely for ease of adoption, with additional array, data type, and operation modules.

Unified memory model

Arrays live in shared memory, enabling operations on CPU and GPU without data copies, simplifying multi-device execution.

Lazy evaluation

Arrays are materialized only when needed, enabling dynamic computation graph optimization and reduced overhead.

Composable function transforms

Supports automatic differentiation (grad), vectorization (vmap), Jacobian-vector products (jvp/vjp), and compilation (compile) that can be composed flexibly.

Multi-device support

Operations can run on any supported device type, currently CPU and GPU, with a device abstraction for future extensibility.

Comprehensive neural network library

Includes layers (e.g., Linear, Transformer, LSTM, MLP), loss functions, initializers, and quantized variants for model building.

Optimizers and schedulers

Provides common optimizers like Adam, AdamW, SGD, Lion, Adafactor, and learning-rate schedules for training workflows.

Distributed communication

Offers primitives for data and tensor parallelism such as all_reduce, all_gather, send/recv, and sharded linear layers for scaling models.

Export and interoperability

Supports saving/loading arrays in multiple formats (safetensors, GGUF, NumPy-compatible) and conversion to/from other frameworks.

C++ API

A fully-featured C++ interface mirrors the Python API, allowing low-level control and integration into performance-critical applications.

Use cases

Training and fine-tuning large language models (LLM inference examples provided)

Implementing data parallelism and tensor parallelism for distributed training

Rapid prototyping of neural network architectures using NumPy-like syntax

Running transformer-based models with efficient attention and KV cache implementations

Building custom ML systems with lazy evaluation and composable gradients

Handling scientific computing and numerical linear algebra tasks on CPU/GPU

Integrating ML models into C++ applications with the native MLX C++ API