Programming Free

MLX

Open array framework for machine learning on Apple silicon and CUDA

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PricingFree
Starting price$0
Free planYes
Free trialYes
APIYes
Open sourceYes
DeploymentSelf Hosted
Last verifiedAugust 1, 2026
Overview

Tool overview

MLX is listed under Programming AI tools.

Summary

What is MLX?

MLX is an MIT-licensed array framework for machine learning. It provides NumPy-like Python and C++ APIs, automatic differentiation, neural-network modules, distributed primitives, and optimized execution on Apple silicon, with newer CUDA support.

Best fit

Best for

Developers and researchers building machine-learning workloads on supported hardware

Audience

Who is it for?

ML researchersPython developersApple-silicon usersSystems engineers
Recommendation

Decision note

Rebuilt from the original export under the complete V411 factual-source-verification workflow. Preview only. Apply remains blocked until Failures = 0, Warnings = 0, Unmapped = 0, Missing = 0, all explicit clears are reviewed, image import is disabled or V380 accepts the logo asset, one representative WordPress edit screen is compared with the export and proposed row, and a post-Apply zero-change Preview is completed.

Capabilities

Key features

NumPy-like Python and C++ APIs

Automatic differentiation and neural-network modules

Optimized Apple-silicon execution

Distributed and newer CUDA support

Workflows

Use cases

Train models on Apple silicon

Run local inference

Build custom ML research code

Experiment with distributed workloads

Strengths

Pros

  • MIT licensed
  • Actively maintained releases
  • Designed for efficient local ML
Considerations

Limitations

Performance and numerical behavior depend on hardware, operating system, precision, and kernel support.

Developers must validate compatibility, reproducibility, memory use, and model licensing.

Cost

Pricing details

Pricing modelFree
Starting price$0
Free planYes
Free trialYes
Pricing context

Billing options

Free open sourceLocal infrastructure
Pricing context

Pricing note

MLX is free under MIT. Costs are limited to the developer's hardware, cloud compute, storage, and model-provider expenses.

View official pricing
Compatibility

Supported programming languages

  • English
Connectivity

Integrations

MLX-LM

MLX Examples

Hugging Face model weights

CUDA

MPI

Specs

Technical details

PlatformsDesktop
Multilingual supportYes
Login requiredNo
Open sourceYes
LicenseMIT
DeploymentSelf Hosted
CompanyApple Machine Learning Research
Launch year2023
Current version0.32.0
Models / versionsCore Arrays Neural Networks Distributed MLX CUDA Backend
Editions / plansPython API C++ API Apple Metal backend Linux CPU/CUDA backends
Data confidenceHigh
Last verifiedAugust 1, 2026
Documentation → Source code → Release notes → Data source →
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Answers

Frequently asked questions

MLX is an MIT-licensed array framework for machine learning. It provides NumPy-like Python and C++ APIs, automatic differentiation, neural-network modules, distributed primitives, and optimized execution on Apple silicon, with newer CUDA support.
Developers and researchers building machine-learning workloads on supported hardware
The listed pricing model for MLX is free. Pricing can change, so users should verify the latest plan details on the official website.
Yes. The current profile indicates that a free trial is available.
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