# mlx | ml-explore | Knowledge Base | Instagit

MLX: An array framework for Apple silicon

GitHub Stars: 27.1k

Repository: https://github.com/ml-explore/mlx

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## Articles

### [How Automatic Differentiation Works in MLX's Transforms: A Deep Dive into the Autograd Engine](/ml-explore/mlx/automatic-differentiation-mlx-transforms)

Explore how MLX's autograd engine powers automatic differentiation in transforms for machine learning. Understand dynamic computation graphs and gradient computation.

- Tags: deep-dive
- Published: 2026-06-18

### [How to Benchmark MLX Operations and Compare Performance Against PyTorch](/ml-explore/mlx/benchmark-mlx-operations-compare-performance)

Benchmark MLX operations and compare performance directly against PyTorch using the built-in Python benchmarking suite. Measure operator latency, warm-up runs, and forced evaluation for accurate results.

- Tags: performance
- Published: 2026-06-18

### [Differences Between MLX's C++ and Python APIs: A Complete Technical Guide](/ml-explore/mlx/mlx-cpp-vs-python-api-differences)

Explore the technical differences between MLX C++ and Python APIs. Understand language semantics memory management and compilation requirements for optimal MLX development.

- Tags: deep-dive
- Published: 2026-06-18

### [How MLX Primitives Form the Backend of All Operations](/ml-explore/mlx/mlx-primitives-backend-operations)

Discover how MLX primitives power all MLX operations. Learn about their role as low-level computational units handling execution, gradients, and batching via a unified C++ interface.

- Tags: internals
- Published: 2026-06-18

### [How MLX Computation Graph Optimization Works Internally: A Deep Dive into the Compiler Pipeline](/ml-explore/mlx/mlx-computation-graph-optimization-internal)

Discover MLX's five-stage compilation pipeline: tracing, tape construction, simplification, kernel fusion, and caching. Optimize MLX computation graphs for efficient device kernels.

- Tags: deep-dive
- Published: 2026-06-18

### [How to Generate Random Numbers Using MLX's Random Module](/ml-explore/mlx/generate-random-numbers-mlx-random-module)

Learn to generate random numbers with MLX's random module. Explore PRNG keys, distributions & GPU acceleration for CPU, CUDA, and Metal.

- Tags: how-to-guide
- Published: 2026-06-18

### [How to Use FFT Operations in MLX for Signal Processing](/ml-explore/mlx/use-fft-operations-mlx-signal-processing)

Unlock powerful signal processing with MLX FFT operations. Learn to implement complex and real-valued transforms efficiently across CPU, CUDA, and Metal backends.

- Tags: how-to-guide
- Published: 2026-06-18

### [How MLX's Memory Allocator and Buffer Management Work: A Deep Dive into the Core Subsystem](/ml-explore/mlx/mlx-memory-allocator-buffer-management)

Explore MLX's memory allocator and buffer management. Understand how it handles CPU, Metal, and CUDA memory with specialized optimization for high performance.

- Tags: deep-dive
- Published: 2026-06-18

### [How to Implement Distributed Training with MLX's Distributed Module: A Complete Guide](/ml-explore/mlx/implement-distributed-training-mlx-distributed-module)

Learn how to implement distributed training with MLX's distributed module. This guide covers tensor sharding and gradient synchronization for multi-device setups.

- Tags: how-to-guide
- Published: 2026-06-18

### [How to Write Custom Metal Kernels Using MLX's fast.h Module](/ml-explore/mlx/write-custom-metal-kernels-mlx-fast-h)

Learn to write custom Metal kernels with MLX fast.h. Explore a JIT compilation interface for direct Python access to Metal compute shaders and streamline your GPU programming.

- Tags: how-to-guide
- Published: 2026-06-18

### [How MLX's Compile Function Works and When to Use Compile Mode](/ml-explore/mlx/mlx-compile-function-compilemode-usage)

Discover how MLX's compile function optimizes Python code into efficient kernels. Learn when to use CompileMode for performance gains and memory savings in your ML projects.

- Tags: internals
- Published: 2026-06-18