MLX

What is MLX?

MLX is a machine learning framework from Apple machine learning research, designed around arrays and tuned for Apple silicon. Its Python API follows NumPy closely, and there are also C++, C and Swift APIs, so the same ideas carry across languages. Higher-level packages for neural networks and optimizers follow PyTorch conventions, which makes it easier to assemble larger models. Composable function transformations cover automatic differentiation, automatic vectorization and graph optimization, and the framework computes lazily, and builds graphs dynamically so changing input shapes does not force slow recompiles. Arrays live in unified shared memory, which means operations can run on the CPU or GPU without copying data between them. The project repository links a separate examples collection covering language model training, LLaMA text generation with LoRA fine-tuning, Stable Diffusion image generation and Whisper speech recognition. MLX is free to install from PyPI, with a CUDA backend and a CPU-only Linux package also available.

Use cases

• Train neural networks on a Mac
• Run LLM inference on Apple silicon
• Fine-tune models with LoRA
• Generate images with Stable Diffusion
• Transcribe speech with Whisper
• Prototype ML research ideas

Who it's for

• ML researchers
• Python developers
• Swift developers
• Mac-based AI engineers

Why use MLX

• The NumPy-like core and PyTorch-like neural network packages are familiar if you already use those libraries.
• Unified memory removes the need to move data between CPU and GPU.
• Dynamic graphs and lazy evaluation keep debugging simple and avoid slow recompilation when shapes change.
• Python, C++, C and Swift bindings let you use the same framework across different codebases.
• It is free to install with pip, and a separate examples repository gives working starting points.

MLX features

• Python API modeled on NumPy
• C++, C and Swift APIs that mirror the Python one
• mlx.nn and mlx.optimizers packages with PyTorch-style APIs
• Composable transforms for autodiff, vectorization and graph optimization
• Lazy computation that materializes arrays only when needed
• Dynamic graph construction without recompiles on shape changes
• Unified memory shared by CPU and GPU
• CUDA backend and CPU-only package for Linux

How to get started with MLX

1. Install MLX with pip install mlx on macOS, or use the cuda or cpu extras on Linux.
2. Read the quick start guide to learn arrays and function transformations.
3. Build a model using the mlx.nn and mlx.optimizers packages.
4. Run examples from the MLX examples repository, such as text generation or image generation.
5. Adapt the code to your own research or application.

MLX limitations

• Built mainly for Apple silicon, with Linux CUDA and CPU builds as add-ons
• Currently runs operations on the CPU and GPU only

MLX pricing

MLX is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

MLX alternatives

The main MLX alternatives listed on Debuly are TensorFlow, llama.cpp, FlexAI, LlamaEdge, Crusoe Cloud and DeepInfra. The table compares pricing and platforms; order is A–Z.

1. 

   Crusoe Cloud

   AI cloud with on-demand GPU instances, managed Kubernetes and Slurm, and managed inference and fine-tuning for open models.

   Developer platformsCoding
   PaidVisit Crusoe Cloud (official site)
2. 

   DeepInfra

   AI inference cloud with an OpenAI-compatible API for 100+ open models, plus private GPU deployments, GPU rental and hosted agents.

   Developer platformsCodingAutomation and agents
   PaidVisit DeepInfra (official site)
3. 

   FlexAI

   OpenAI-compatible inference API, dedicated GPU endpoints and fine-tuning for open models, billed per token or per GPU-hour.

   Developer platformsCoding
   PaidVisit FlexAI (official site)
4. 

   llama.cpp

   Open-source engine and desktop app for running LLMs locally, with a built-in OpenAI-compatible API server and web chat UI.

   Developer platformsCodingChat assistants
   Open sourceVisit llama.cpp (official site)
5. 

   LlamaEdge

   Small Rust and WebAssembly runtime that serves local LLMs through an OpenAI-compatible API on CPUs, GPUs and edge devices.

   Developer platformsCodingAutomation and agents
   Open sourceVisit LlamaEdge (official site)
6. 

   TensorFlow

   Open-source machine learning framework with Keras, TensorFlow.js, LiteRT and TFX for building and deploying models.

   Developer platformsCoding
   Open sourceVisit TensorFlow (official site)

MLX FAQ

Sources & verification

Facts on this page come from the official sources below, each with the date we read it. Written by Debuly; the vendor did not review this page.

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