Explain new in mxl
Asked on 06/12/2025
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MLX is a new framework introduced by Apple, specifically designed to leverage the capabilities of Apple Silicon for machine learning tasks. Here are some of the key features and capabilities of MLX:
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Unified Memory Architecture: MLX takes advantage of Apple Silicon's unified memory, allowing the CPU and GPU to share the same physical memory. This eliminates the need to copy data between different memory pools, which is common in systems with discrete GPUs.
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Lazy Evaluation: MLX uses a lazy execution engine, meaning computations are only performed when necessary. This allows for optimizations and transformations on the computation graph before execution, making it more efficient.
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Function Transformations: MLX supports function transformations, which can automatically compute gradients and optimize computation graphs. This feature is particularly useful for training and improving machine learning models.
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Higher-Level Packages: MLX includes packages like MLX Net for building neural networks and MLX Optimizers for optimization algorithms. These packages can be used independently or together for seamless integration.
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Support for Large Language Models: MLX enables running and fine-tuning large language models locally on Apple devices without the need for cloud resources. This is both efficient and secure, as no data leaves the machine.
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Quantization: MLX provides routines for quantizing models to reduce memory usage and increase speed, which is particularly beneficial for large models.
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Custom Metal Kernels: For specialized functions, MLX allows the addition of custom Metal kernels, which are compiled and executed just-in-time, providing further optimization opportunities.
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Multi-language Support: MLX offers APIs in Python, Swift, C, and C++, making it accessible to developers with different language preferences.
For more detailed information, you can refer to the sessions "Get started with MLX for Apple silicon" and "Explore large language models on Apple silicon with MLX" from WWDC 2025.
Get started with MLX for Apple silicon
MLX is a flexible and efficient array framework for numerical computing and machine learning on Apple silicon. We’ll explore fundamental features including unified memory, lazy computation, and function transformations. We’ll also look at more advanced techniques for building and accelerating machine learning models across Apple’s platforms using Swift and Python APIs.
Explore large language models on Apple silicon with MLX
Discover MLX LM – designed specifically to make working with large language models simple and efficient on Apple silicon. We’ll cover how to fine-tune and run inference on state-of-the-art large language models on your Mac, and how to seamlessly integrate them into Swift-based applications and projects.
Train your machine learning and AI models on Apple GPUs
Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.