According to Decrypt, Apple has introduced MLX, an open-source framework designed for machine learning on its M-series CPUs. This move aims to address compatibility and performance issues related to Apple's unique architecture and software. MLX offers a user-friendly design, drawing inspiration from popular frameworks like PyTorch, Jax, and ArrayFire, and promises a more streamlined process for training and deploying AI learning models on Apple devices.
A key feature of MLX is its unified memory model, where arrays exist in shared memory, allowing operations across supported device types without requiring data duplication. This is essential for developers seeking flexibility in their AI projects. However, AI development on Apple Silicon has faced challenges due to its closed ecosystem and lack of compatibility with many open-source development projects and their widely used infrastructure.
MLX differs from CoreML, which focuses on converting pre-existing machine learning models and optimizing them for Apple devices. Instead, MLX is about creating and executing machine learning models directly and efficiently on Apple's hardware, providing tools for innovation and development within the Apple ecosystem. MLX has shown promising results in benchmark tests and compatibility with tools like Stable Diffusion and OpenAI's Whisper. This development marks a significant milestone for Apple's ecosystem, opening up new possibilities for AI and machine learning research on Apple devices.