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MLX provides a complete C++ API for high-performance machine learning on Apple Silicon. The C++ API gives you direct access to MLX’s core operations and lets you build efficient ML applications without Python overhead.

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Why Use the C++ API?

The C++ API is ideal when you need:
  • Maximum Performance: Direct C++ access eliminates Python overhead
  • Standalone Applications: Build native macOS applications without Python dependencies
  • Custom Extensions: Create custom operations and Metal kernels
  • Integration: Embed MLX into existing C++ projects

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Core Components

The MLX C++ API is organized into several key namespaces: 
#include "mlx/mlx.h"

namespace mx = mlx::core;

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Array Operations

The mlx::core namespace contains all core array operations:
  • Array Creation: zeros(), ones(), arange(), linspace()
  • Array Manipulation: reshape(), transpose(), concatenate(), split()
  • Mathematical Operations: add(), multiply(), matmul(), exp(), log()
  • Reductions: sum(), mean(), max(), min()
  • Comparison: equal(), greater(), less()

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Additional Modules

  • mlx::core::random: Random number generation
  • mlx::core::fft: Fast Fourier transforms
  • mlx::core::linalg: Linear algebra operations
  • mlx::core::fast: Fast custom Metal kernels

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Basic Example

Here’s a simple example showing array creation and operations: 
#include <iostream>
#include "mlx/mlx.h"

namespace mx = mlx::core;

int main() {
  // Create arrays
  auto x = mx::array({1.0f, 2.0f, 3.0f, 4.0f}, {2, 2});
  auto y = mx::ones({2, 2});
  
  // Perform operations
  auto z = x + y;
  
  // Evaluate (MLX is lazy by default)
  mx::eval(z);
  
  // Print result
  std::cout << z << std::endl;
  
  return 0;
}

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Key Concepts

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Lazy Evaluation

MLX uses lazy evaluation by default. Operations build a computation graph without executing immediately: 
auto z = x + y;  // Graph node created, no computation yet
mx::eval(z);     // Now the computation runs
Some operations implicitly evaluate arrays:
  • Accessing values with .item<T>()
  • Printing arrays with std::cout

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Streams and Devices

MLX supports multiple compute devices and streams for parallelism: 
// Run on GPU (default)
auto x = mx::ones({100, 100});

// Run on CPU
auto y = mx::ones({100, 100}, mx::cpu);

// Explicit stream
auto s = mx::Stream(mx::Device::gpu);
auto z = mx::ones({100, 100}, s);

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Data Types

MLX supports various data types: 
auto x = mx::array(1, mx::int32);      // 32-bit integer
auto y = mx::array(1.0f, mx::float32); // 32-bit float
auto z = mx::array(1.0, mx::float16);  // 16-bit float
Available types: float32, float16, bfloat16, int32, int64, int16, int8, uint32, uint64, uint16, uint8, bool_, complex64

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Automatic Differentiation

MLX provides composable function transformations including automatic differentiation: 
auto fn = [](mx::array x) { 
  return mx::square(x); 
};

// Get gradient function
auto grad_fn = mx::grad(fn);

// Compute derivative
auto x = mx::array(1.5);
auto dfdx = grad_fn(x);  // Returns 2 * x = 3.0

// Higher-order derivatives
auto d2fdx2 = mx::grad(mx::grad(fn))(x);  // Returns 2.0

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Next Steps

Operations Reference

Complete reference for C++ operations

Usage Guide

How to use MLX in C++ projects

Building Extensions

Create custom C++ extensions

Metal Kernels

Write custom Metal GPU kernels