MLX framework

What is MLX?

MLX is an array framework for machine learning research developed by Apple’s machine learning research team. It’s designed specifically for Apple Silicon and provides:

Unified memory architecture: CPU and GPU share the same memory pool
Lazy evaluation: Operations build computation graphs evaluated on-demand
Dynamic computation graphs: No recompilation needed when input shapes change
Automatic differentiation: Built-in gradient computation for training
Metal acceleration: Direct access to Apple’s GPU via Metal framework
Multi-device support: Seamless execution on CPU or GPU

MLX is to Apple Silicon what PyTorch is to CUDA GPUs - a native framework optimized for the hardware architecture.

Why MLX for Apple Silicon?

Metal GPU acceleration

MLX uses Apple’s Metal framework to access the GPU, providing:

Native performance: Direct Metal API calls without translation layers
Optimized kernels: Apple-tuned implementations of common operations
Unified shader architecture: Efficient compute shader compilation
Low latency: Minimal overhead between CPU and GPU operations

use mlx_rs::{Device, DeviceType, StreamOrDevice};

// Operations automatically use GPU by default
let device = Device::gpu();
let stream = StreamOrDevice::gpu();

let result = x.matmul(&y)?; // Executes on GPU via Metal

Performance on M3 Max (128GB):

LLM inference: 25-45 tokens/second (4B-9B parameter models)
ASR transcription: 30-50x real-time processing
Image generation: 3-5 seconds per image

Unified memory model

Unlike traditional GPU computing where data must be copied between CPU and GPU memory:

# Traditional GPU (CUDA)
x_cpu = torch.randn(1000, 1000)  # CPU memory
x_gpu = x_cpu.cuda()              # Copy to GPU memory
y_gpu = x_gpu @ x_gpu.T           # Compute on GPU
y_cpu = y_gpu.cpu()               # Copy back to CPU

MLX arrays live in unified memory accessible by both CPU and GPU:

// MLX on Apple Silicon
let x = mlx_rs::random::normal(&[1000, 1000], None, None, None)?;

// No copying needed - both CPU and GPU can access x
let y_cpu = ops::matmul(&x, &x.T()?, StreamOrDevice::cpu())?;
let y_gpu = ops::matmul(&x, &x.T()?, StreamOrDevice::gpu())?;

See Unified memory for details.

Lazy evaluation

MLX builds computation graphs without executing operations immediately:

use mlx_rs::array;

let a = array!([1, 2, 3, 4]);
let b = array!([5, 6, 7, 8]);

// These operations don't execute yet
let c = &a + &b;  // Graph: c = add(a, b)
let d = &c * 2;    // Graph: d = mul(add(a, b), 2)

// Evaluation happens here
d.eval()?;  // Executes optimized graph

Benefits:

Kernel fusion: Multiple operations combined into single GPU kernel
Memory optimization: Intermediate results avoided when possible
Dead code elimination: Unused computations never execute

See Lazy evaluation for details.

MLX architecture

Layer structure

┌──────────────────────────────────────────┐
│           Python/Rust API                │  High-level interface
├──────────────────────────────────────────┤
│           MLX C++ Core                   │  
│  • Array abstraction                     │
│  • Operation dispatch                    │  
│  • Graph optimization                    │
├──────────────────────────────────────────┤
│        Backend Implementations           │
│  ┌─────────────┐  ┌─────────────┐       │
│  │   CPU       │  │    GPU      │       │
│  │ (Accelerate)│  │  (Metal)    │       │
│  └─────────────┘  └─────────────┘       │
├──────────────────────────────────────────┤
│        Hardware Abstraction              │
│  • Unified Memory Controller             │
│  • Metal API                             │
│  • Accelerate Framework                  │
└──────────────────────────────────────────┘
                   │
                   ▼
┌──────────────────────────────────────────┐
│         Apple Silicon (M1/M2/M3/M4)      │
│  • ARM CPU cores (Performance + Efficient)│
│  • GPU cores (Metal-optimized)           │
│  • Neural Engine                         │
│  • Unified Memory (shared DRAM)          │
└──────────────────────────────────────────┘

OminiX-MLX integration

OminiX-MLX provides Rust bindings to MLX via three layers: mlx-sys (FFI layer)

Auto-generated bindings to MLX C API using bindgen
Raw pointers and C types (mlx_array, mlx_device, etc.)
Direct mapping to MLX functions with no overhead

mlx-rs (Safe Rust API)

Safe wrappers around mlx-sys with automatic memory management
Idiomatic Rust types (Array, Device, Stream)
Compile-time safety and zero-cost abstractions

Model crates (High-level)

Complete model implementations (transformers, encoders, etc.)
Weight loading and generation loops
Integration with tokenizers and audio/image processing

Core concepts

Arrays

The fundamental data structure in MLX is the n-dimensional array:

use mlx_rs::{array, Array, Dtype};

// Create arrays
let a = array!([1, 2, 3, 4]);  // Shape: [4], dtype: int32
let b = array!([[1.0, 2.0], [3.0, 4.0]]);  // Shape: [2, 2], dtype: float32

// Array properties
assert_eq!(a.shape(), &[4]);
assert_eq!(a.dtype(), Dtype::Int32);
assert_eq!(b.ndim(), 2);

Arrays are:

Immutable by default: Operations return new arrays
Lazily evaluated: Data only computed when needed
Reference counted: Automatic memory management
Device-agnostic: No explicit device placement

Devices

MLX supports CPU and GPU devices:

use mlx_rs::{Device, DeviceType};

// Create devices
let cpu = Device::cpu();  // Device(cpu, 0)
let gpu = Device::gpu();  // Device(gpu, 0)

// Check device properties
let device = Device::default();  // GPU by default
assert_eq!(device.get_type()?, DeviceType::Gpu);
assert_eq!(device.get_index()?, 0);

// Set default device
Device::set_default(&cpu);

See mlx-rs/src/device.rs:11 for implementation.

Streams

Streams control where and how operations execute:

use mlx_rs::{Stream, StreamOrDevice};

// Default streams
let gpu_stream = Stream::gpu();
let cpu_stream = Stream::cpu();

// Operations specify execution stream
let result = ops::add(&a, &b, StreamOrDevice::gpu())?;

Key properties:

Operations on the same stream execute sequentially
Operations on different streams can execute in parallel
MLX handles synchronization automatically
No explicit device-to-device transfers needed

See mlx-rs/src/stream.rs:110 for implementation.

Operations

MLX provides a comprehensive set of operations: Element-wise operations

let c = &a + &b;  // Addition
let d = &a * &b;  // Multiplication
let e = a.exp()?; // Exponential

Linear algebra

use mlx_rs::ops;

let y = x.matmul(&w)?;              // Matrix multiplication
let (q, r) = ops::qr(&a, None)?;    // QR decomposition
let inv = ops::inv(&a)?;             // Matrix inverse

Neural network layers

use mlx_rs::nn;

let linear = nn::Linear::new(128, 256)?;  // Linear layer
let output = linear.forward(&input)?;

let conv = nn::Conv2d::new(3, 64, 3)?;    // 2D convolution
let features = conv.forward(&image)?;

Reductions

let sum = ops::sum(&a, None, None)?;     // Sum all elements
let mean = ops::mean(&a, &[0], None)?;   // Mean along axis 0
let max = ops::max(&a, &[1], None)?;     // Max along axis 1

Performance features

Accelerate framework integration

For CPU operations, MLX uses Apple’s Accelerate framework:

BLAS/LAPACK: Optimized linear algebra routines
vDSP: Vector digital signal processing
SIMD vectorization: Automatic use of NEON instructions
Multi-core parallelism: Operations spread across CPU cores

Enable with the accelerate feature flag (enabled by default):

[dependencies]
mlx-rs = { version = "0.21", features = ["accelerate"] }

Metal shader compilation

MLX compiles optimized Metal shaders for GPU operations:

Operation graph construction: Build computation graph
Kernel fusion: Combine multiple ops into single shader
Metal shader generation: Emit Metal Shading Language code
Compilation: Compile to GPU binary
Execution: Dispatch to GPU compute units

// This sequence gets fused into a single Metal kernel
let x = ops::add(&a, &b, StreamOrDevice::gpu())?;
let y = ops::mul(&x, &c, StreamOrDevice::gpu())?;
let z = ops::relu(&y, StreamOrDevice::gpu())?;
z.eval()?;  // Single kernel: z = relu((a + b) * c)

Memory optimization

MLX optimizes memory usage through: In-place operations (where safe):

// May reuse a's memory if no other references exist
let result = &a + &b;

Lazy materialization:

let c = &a + &b;  // No memory allocated yet
let d = &c * 2;    // Still no allocation
d.eval()?;         // Memory allocated only for d (c may be skipped)

Automatic garbage collection:

Reference counting frees unused arrays
Graph evaluation clears intermediate results
No manual memory management required

Comparison with other frameworks

Feature	MLX	PyTorch	TensorFlow
Target platform	Apple Silicon	NVIDIA GPUs	Multi-platform
Memory model	Unified	Separate CPU/GPU	Separate CPU/GPU
Evaluation	Lazy	Eager (default)	Graph (v1) / Eager (v2)
Graph construction	Dynamic	Dynamic	Static (v1) / Dynamic (v2)
GPU API	Metal	CUDA	CUDA / ROCm
Rust bindings	mlx-rs	tch-rs	tensorflow-rust
Memory overhead	Low (unified)	High (copy overhead)	High (copy overhead)

MLX is optimized for Apple Silicon specifically. For NVIDIA GPUs, use PyTorch/TensorFlow with CUDA.

Feature flags

Control MLX backend features via Cargo:

[dependencies]
mlx-rs = { version = "0.21", features = ["metal", "accelerate"] }

Flag	Description	Default
`metal`	Enable Metal GPU acceleration	✓ On
`accelerate`	Use Accelerate framework for CPU	✓ On

Disabling features:

# CPU-only build (useful for CI without Metal)
mlx-rs = { version = "0.21", default-features = false, features = ["accelerate"] }

System requirements

Hardware:

Apple Silicon Mac (M1, M2, M3, M4, or later)
Minimum 8GB unified memory (16GB+ recommended)
macOS 14.0 (Sonoma) or later

Software:

Rust 1.82.0 or later
Xcode Command Line Tools
Metal support (included in macOS)

For best performance, use macOS 15+ which includes Metal 3 optimizations.

Limitations

Platform-specific: MLX only works on Apple Silicon Macs. It will not run on:

Intel Macs
Windows or Linux (even with ARM processors)
Cloud platforms without Apple Silicon instances

Not for training: While MLX supports automatic differentiation, OminiX-MLX focuses on inference. Training large models requires Python’s MLX for full ecosystem support. Model availability: Models must be converted to MLX format (typically via HuggingFace MLX community models).

Additional resources

MLX GitHub

Official MLX repository and documentation

Unified memory

Deep dive into Apple Silicon’s unified memory

Lazy evaluation

How lazy evaluation optimizes performance

Architecture

OminiX-MLX system architecture overview

Get Started

Core Concepts

Language Models

Vision-Language Models

Speech Recognition

Text-to-Speech

Image Generation

API Server

Advanced

What is MLX?

Why MLX for Apple Silicon?

Metal GPU acceleration

Unified memory model

Lazy evaluation

MLX architecture

Layer structure

OminiX-MLX integration

Core concepts

Arrays

Devices

Streams

Operations

Performance features

Accelerate framework integration

Metal shader compilation

Memory optimization

Comparison with other frameworks

Feature flags

System requirements

Limitations

Additional resources

MLX GitHub

Unified memory

Lazy evaluation

Architecture

Build docs developers (and LLMs) love

Get Started

Core Concepts

Language Models

Vision-Language Models

Speech Recognition

Text-to-Speech

Image Generation

API Server

Advanced

​What is MLX?

​Why MLX for Apple Silicon?

​Metal GPU acceleration

​Unified memory model

​Lazy evaluation

​MLX architecture

​Layer structure

​OminiX-MLX integration

​Core concepts

​Arrays

​Devices

​Streams

​Operations

​Performance features

​Accelerate framework integration

​Metal shader compilation

​Memory optimization

​Comparison with other frameworks

​Feature flags

​System requirements

​Limitations

​Additional resources

MLX GitHub

Unified memory

Lazy evaluation

Architecture

Build docs developers (and LLMs) love

What is MLX?

Why MLX for Apple Silicon?

Metal GPU acceleration

Unified memory model

Lazy evaluation

MLX architecture

Layer structure

OminiX-MLX integration

Core concepts

Arrays

Devices

Streams

Operations

Performance features

Accelerate framework integration

Metal shader compilation

Memory optimization

Comparison with other frameworks

Feature flags

System requirements

Limitations

Additional resources