WWDC 2025 - Get started with MLX for Apple silicon

At WWDC 2025, Apple unveiled MLX - an open-source array framework specifically engineered for Apple Silicon. For iOS developers venturing into machine learning, MLX represents a paradigm shift that leverages the unique architecture of Apple devices to deliver unprecedented performance. What Makes MLX Different Purpose-Built for Apple Silicon Unified Memory Architecture: Unlike traditional GPU setups with separate memory pools, Apple Silicon shares memory between CPU and GPU Device Flexibility: Runs seamlessly across Mac, iPhone, iPad, and Vision Pro Native Performance: Optimized specifically for Apple's hardware ecosystem Framework Positioning NumPy Compatibility: Drop-in replacement for most numerical computations PyTorch Similarity: Familiar API for developers transitioning from other ML frameworks Swift Integration: Full-featured Swift API alongside Python support Core Architecture Principles Unified Memory Programming Model Traditional ML frameworks follow a "computation follows data" approach - arrays live in specific memory locations (CPU or GPU). MLX revolutionizes this: # Traditional approach: data location determines compute location # MLX approach: specify device per operation c = mx.add(a, b, stream=mx.gpu) # GPU computation d = mx.multiply(a, b, stream=mx.cpu) # CPU computation Key Benefits: Zero-copy operations between CPU and GPU Automatic dependency management Parallel execution capabilities Lazy Evaluation Engine MLX builds computation graphs without immediate execution: Graph Construction: Operations create nodes instead of computing results On-Demand Execution: Computation happens only when results are needed Optimization Opportunities: Framework can optimize entire graphs before execution Resource Efficiency: Pay only for computations actually used Function Transformations Elevates MLX from array framework to powerful ML tool: # Automatic differentiation def sin_function(x): return mx.sin(x) gradient_fn = mx.grad(sin_function) second_derivative = mx.grad(mx.grad(sin_function)) Transformation Categories: Automatic Differentiation: mx.grad for computing derivatives Compute Optimization: mx.compile for kernel fusion Neural Network Development MLX.nn Module Structure Base Class: nn.Module - foundation for all layers and models Standard Layers: Pre-built components like nn.Linear Custom Layers: Inherit from nn.Module for specialized implementations Utilities: Loss functions in nn.losses, initialization in nn.init PyTorch Migration Path MLX intentionally mirrors PyTorch patterns: # MLX Implementation class MLP(nn.Module): def init(self, dim, h_dim): super().init() self.linear1 = nn.Linear(dim, h_dim) self.linear2 = nn.Linear(h_dim, dim) def call(self, x): # Note: call vs forward x = nn.relu(self.linear1(x)) return self.linear2(x) Migration Differences: Use call instead of forward Activation functions as standalone functions: nn.relu(x) vs x.relu() Performance Optimization Strategies Compilation for Speed Transform multi-kernel operations into single fused kernels: @mx.compile def optimized_gelu(x): return x * (1 + mx.erf(x / math.sqrt(2))) / 2 Compilation Benefits: Reduced memory bandwidth usage Lower kernel launch overhead Improved GPU utilization MLX.fast Package Highly optimized implementations of common ML operations: Transformer Components: Positional encodings, normalization layers Attention Mechanisms: Scale dot product attention with configurable masking Specialized Operations: RMS norm, layer normalization RMS Norm Example: # Replace complex implementation with single optimized operation result = mx.fast.rms_norm(x, weight, eps=1e-5) Custom Metal Kernels For specialized operations not covered by existing implementations: source = """ uint elem = thread_position_in_grid.x; out[elem] = metal::exp(inp[elem]); """ kernel = mx.fast.metal_kernel( name="myexp", input_names=["inp"], output_names=["out"], source=source ) Memory and Precision Management Quantization Strategies Reduce model size and increase inference speed: Precision Reduction: 32-bit → 16-bit → 4-bit quantization Flexible Configuration: Configurable bits per element and group sizes Model-Level Quantization: nn.quantize() for entire models Quantization Workflow: # Quantize weights quantized_weight, scales, biases = mx.quantize(weight, bits=4, group_size=32) # Perform quantized operations result = mx.quantized_matmul(x, quantized_weight, scales=scales, biases=biases, bits=4, group_size=32) Large Model Deployment Memory Efficiency: Fit larger models in device memory Inference Speed: Sig

Jun 28, 2025 - 01:40

WWDC 2025 - Get started with MLX for Apple silicon

At WWDC 2025, Apple unveiled MLX - an open-source array framework specifically engineered for Apple Silicon. For iOS developers venturing into machine learning, MLX represents a paradigm shift that leverages the unique architecture of Apple devices to deliver unprecedented performance.

What Makes MLX Different

Purpose-Built for Apple Silicon

Unified Memory Architecture: Unlike traditional GPU setups with separate memory pools, Apple Silicon shares memory between CPU and GPU
Device Flexibility: Runs seamlessly across Mac, iPhone, iPad, and Vision Pro
Native Performance: Optimized specifically for Apple's hardware ecosystem

Framework Positioning

NumPy Compatibility: Drop-in replacement for most numerical computations
PyTorch Similarity: Familiar API for developers transitioning from other ML frameworks
Swift Integration: Full-featured Swift API alongside Python support

Core Architecture Principles

Unified Memory Programming Model

Traditional ML frameworks follow a "computation follows data" approach - arrays live in specific memory locations (CPU or GPU). MLX revolutionizes this:

# Traditional approach: data location determines compute location
# MLX approach: specify device per operation
c = mx.add(a, b, stream=mx.gpu)  # GPU computation
d = mx.multiply(a, b, stream=mx.cpu)  # CPU computation

Key Benefits:

Zero-copy operations between CPU and GPU
Automatic dependency management
Parallel execution capabilities

Lazy Evaluation Engine

MLX builds computation graphs without immediate execution:

Graph Construction: Operations create nodes instead of computing results
On-Demand Execution: Computation happens only when results are needed
Optimization Opportunities: Framework can optimize entire graphs before execution
Resource Efficiency: Pay only for computations actually used

Function Transformations

Elevates MLX from array framework to powerful ML tool:

# Automatic differentiation
def sin_function(x):
    return mx.sin(x)

gradient_fn = mx.grad(sin_function)
second_derivative = mx.grad(mx.grad(sin_function))

Transformation Categories:

Automatic Differentiation: mx.grad for computing derivatives
Compute Optimization: mx.compile for kernel fusion

Neural Network Development

MLX.nn Module Structure

Base Class: nn.Module - foundation for all layers and models
Standard Layers: Pre-built components like nn.Linear
Custom Layers: Inherit from nn.Module for specialized implementations
Utilities: Loss functions in nn.losses, initialization in nn.init

PyTorch Migration Path

MLX intentionally mirrors PyTorch patterns:

# MLX Implementation
class MLP(nn.Module):
    def __init__(self, dim, h_dim):
        super().__init__()
        self.linear1 = nn.Linear(dim, h_dim)
        self.linear2 = nn.Linear(h_dim, dim)

    def __call__(self, x):  # Note: __call__ vs forward
        x = nn.relu(self.linear1(x))
        return self.linear2(x)

Migration Differences:

Use __call__ instead of forward
Activation functions as standalone functions: nn.relu(x) vs x.relu()

Performance Optimization Strategies

Compilation for Speed

Transform multi-kernel operations into single fused kernels:

@mx.compile
def optimized_gelu(x):
    return x * (1 + mx.erf(x / math.sqrt(2))) / 2

Compilation Benefits:

Reduced memory bandwidth usage
Lower kernel launch overhead
Improved GPU utilization

MLX.fast Package

Highly optimized implementations of common ML operations:

Transformer Components: Positional encodings, normalization layers
Attention Mechanisms: Scale dot product attention with configurable masking
Specialized Operations: RMS norm, layer normalization

RMS Norm Example:

# Replace complex implementation with single optimized operation
result = mx.fast.rms_norm(x, weight, eps=1e-5)

Custom Metal Kernels

For specialized operations not covered by existing implementations:

source = """
    uint elem = thread_position_in_grid.x;
    out[elem] = metal::exp(inp[elem]);
"""
kernel = mx.fast.metal_kernel(
    name="myexp",
    input_names=["inp"],
    output_names=["out"],
    source=source
)

Memory and Precision Management

Quantization Strategies

Reduce model size and increase inference speed:

Precision Reduction: 32-bit → 16-bit → 4-bit quantization
Flexible Configuration: Configurable bits per element and group sizes
Model-Level Quantization: nn.quantize() for entire models

Quantization Workflow:

# Quantize weights
quantized_weight, scales, biases = mx.quantize(weight, bits=4, group_size=32)

# Perform quantized operations
result = mx.quantized_matmul(x, quantized_weight, scales=scales, biases=biases, 
                           bits=4, group_size=32)

Large Model Deployment

Memory Efficiency: Fit larger models in device memory
Inference Speed: Significantly faster token generation for LLMs
Quality Preservation: Minimal accuracy loss with proper quantization settings

Distributed Computing

Multi-Device Scaling

MLX supports computation across multiple machines:

Communication Primitives: mx.distributed.all_sum() for cross-device operations
Network Flexibility: Ethernet or Thunderbolt connectivity
Simple Launcher: mlx.launch command for multi-machine deployment

Use Cases:

Large models exceeding single-device memory
Distributed fine-tuning across multiple Macs
Parallel evaluation on large datasets

Swift Integration for iOS

Native iOS Development

MLX Swift provides full ML capabilities for iOS applications:

Platform Coverage: macOS, iOS, iPadOS, visionOS
Xcode Integration: Standard Swift package manager support
API Consistency: Intentionally similar to Python API

Swift vs Python API Comparison

// Swift
let a = MLXArray([1, 2, 3])
let b = MLXArray([4, 5, 6])
let c = a + b

Implementation Considerations:

Same core features available in both languages
Choose Python for prototyping, Swift for production iOS apps
Seamless transition between development environments

Getting Started Recommendations

Installation and Setup

Python: pip3 install mlx
Swift: Add MLX Swift package to Xcode project
Examples: Extensive example repositories for both languages

Learning Resources

Official Documentation: Comprehensive guides and API references
Community Models: Active Hugging Face organization with latest models
Example Projects: Language models, image generation, speech recognition

Development Strategy

Start with Python: Rapid prototyping and experimentation
Leverage Examples: Build upon existing implementations
Optimize Incrementally: Apply compilation and quantization as needed
Deploy with Swift: Integrate into production iOS applications

Strategic Implications for iOS Development

Competitive Advantages

On-Device Intelligence: Reduce cloud dependency and latency
Privacy Preservation: Keep sensitive data on device
Performance Optimization: Leverage Apple Silicon's unique architecture
Cost Efficiency: Eliminate inference costs for deployed models