Technology
Apple MLX
Apple MLX: An efficient, open-source array framework for machine learning, specifically engineered for the unified memory architecture of Apple silicon.
MLX is Apple's high-performance, open-source array framework, purpose-built for efficient machine learning on Apple silicon (M-series chips). It capitalizes on the unified memory architecture: data remains in shared memory, eliminating costly transfers between the CPU and GPU. The API is immediately familiar, closely mirroring NumPy for core operations and PyTorch for higher-level packages (e.g., `mlx.nn`). Developers use Python, C++, C, or Swift to leverage key features like composable function transformations (automatic differentiation) and dynamic graph construction. MLX enables researchers and developers to efficiently train and deploy large models locally, including LLaMA and Stable Diffusion.
What builders pair with Apple MLX
Projects using both technologies. Select a pairing to see a project.
Pairing: macOS
Big Models, Small Machines: Run Full-Precision LLMs on Low Memory
Pairing: Python
Big Models, Small Machines: Run Full-Precision LLMs on Low Memory
Pairing: Transformers
Big Models, Small Machines: Run Full-Precision LLMs on Low Memory
Recent Talks & Demos
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