mlx-dev

To set up the MLX environment on Apple Silicon, install the core MLX tooling and run the provided sample scripts. This ensures correct runtime patterns and proper Metal GPU access for your arrays and neural networks.

MLX uses lazy evaluation, requiring explicit mlx.eval calls to trigger computation, unlike PyTorch's eager execution. Memory management relies on unified memory architecture across CPU and GPU, differing from PyTorch's separate memory pools.

NHWC conventions in MLX dictate that array shapes follow the batch, height, width, and channel order. Apply this layout when constructing neural network layers to ensure proper data flow and avoid shape mismatches on Apple Silicon.

mlx.compile optimizes and fuses operations for faster Metal GPU execution, which can hide intermediate values. Debug by isolating the compiled function, checking dtype behavior, and temporarily removing the compile decorator to trace array outputs.

Yes, MLX supports large language model inference through mlx-lm integration on Apple Silicon. This allows you to load, run, and fine-tune models efficiently using the framework's memory management and GPU acceleration.

Common MLX pitfalls include unexpected dtype promotions during operations and non-intuitive array indexing results. Follow idiomatic patterns by explicitly casting dtypes and validating array indexing logic against the framework's specific rules.