MLX Framework

Machine Learning on Apple Silicon

The Landscape

• ML frameworks optimized for NVIDIA GPUs
• Apple Silicon left behind
• Gap in the market

Enter MLX

• Open-source from Apple
• Python-first design
• Unified memory architecture

Why Apple Silicon?

• Billions of devices
• High-performance GPU
• Shared memory model
• Massive untapped potential

Core Design Principles

• Simple and intuitive API
• Familiar NumPy-like syntax
• Lazy evaluation
• Composable functions

Architecture Overview

• NumPy-like array interface
• Unified memory (CPU + GPU)
• Automatic differentiation

Key Features

• Efficient inference on-device
• Fine-tuning and training
• Memory-optimized operations
• Cross-platform compatibility

MLX vs Alternatives

• PyTorch: GPU-focused, larger overhead
• TensorFlow: Production-heavy, complex
• JAX: Functional, steep learning curve
• MLX: Apple Silicon native, lightweight

Core Concepts

• Arrays: Lazy computation model
• Operations: GPU-accelerated when beneficial
• Gradients: Full autodiff support
• Training: Lightning-fast fine-tuning

Training Pipeline: Data to Inference

Step 1: Data Preparation

• Extract and annotate domain-specific patterns
• Convert to optimized training tensors

Step 2: Model Training

• Train specialized architectures from scratch
• Metal-accelerated on Apple Silicon

Step 3: Deploy & Inference

• Package models (.npz or .safetensors)
• Load at startup, run in parallel

What You Can Do

• Train specialized models from scratch
• Inference on M-series chips in <50ms
• Parallel model execution
• Bundle models directly in apps

The Future

• On-device AI becomes standard
• Privacy-first applications
• Distributed ML at the edge

Questions?

MLX: https://github.com/ml-explore/mlx
HuggingFace: https://huggingface.co/docs/hub/en/mlx