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ArshTechPro
ArshTechPro

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WWDC 2025 - Get started with MLX for Apple silicon

#ios #swift #ai #mobile

pytorch description

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
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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))
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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)
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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
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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)
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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
)
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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)
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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
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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

  1. Start with Python: Rapid prototyping and experimentation
  2. Leverage Examples: Build upon existing implementations
  3. Optimize Incrementally: Apply compilation and quantization as needed
  4. 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

Top comments (3)

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arshtechpro profile image
ArshTechPro •

MLX - an open-source array framework specifically engineered for Apple Silicon

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nathan_tarbert profile image
Nathan Tarbert •

This is extremely impressive, especially the seamless switch between Python prototyping and Swift app deployment. I've always wanted this kind of setup for running real ML on-device

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dotallio profile image
Dotallio •

This feels like a pretty big unlock for on-device ML on Apple hardware. Have you tried migrating any PyTorch models - how smooth is the actual transition?