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MLX vs tinygrad

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

MLX

Open source · MIT

An array framework for machine learning on Apple silicon, with a NumPy-style Python API.

Full review Website
tinygrad

Open source · MIT

A small, readable deep learning library in Python that sits between PyTorch and micrograd.

Full review Website

The short answer

Choose MLX if you need…

  • Researchers experimenting with machine learning models on a Mac with Apple silicon
  • Developers who already know NumPy or PyTorch and want a familiar API
  • People who want to run or fine-tune models like LLaMA or Whisper locally using the MLX examples
  • Developers who want C++, C, or Swift bindings for Apple silicon machine learning

Choose tinygrad if you need…

  • Developers who want to read and modify how a deep learning stack works internally
  • Students learning how autograd and tensor compilers function
  • Researchers experimenting with low-level kernel code or new hardware backends
  • People who want a PyTorch-like training loop with a small codebase

Side-by-side

MLXtinygrad
TypeLibraryLibrary
PriceFree and open sourceFree and open source
LicenceMITMIT
GitHub stars29k33.8k
Setup difficultyIntermediateIntermediate
How you get itInstall with a package managerClone and build from source
Main languageC++Python
Last code update4 days ago4 days ago

What each one is

MLX

MLX is an open-source framework for building and running machine learning code on Apple silicon Macs. It provides array operations with a Python API that closely follows NumPy, plus higher-level packages for neural networks and optimizers. Its main distinguishing feature is a unified memory model, where arrays live in shared memory and can be used by the CPU or GPU without copying data.

tinygrad

tinygrad is a Python library for building and training neural networks. It provides a tensor type with automatic differentiation, a compiler that fuses operations into faster kernels, and modules for layers, optimizers and datasets. It aims to be much smaller and easier to read than PyTorch while keeping a similar API.

Best for

MLX

  • Researchers experimenting with machine learning models on a Mac with Apple silicon
  • Developers who already know NumPy or PyTorch and want a familiar API
  • People who want to run or fine-tune models like LLaMA or Whisper locally using the MLX examples
  • Developers who want C++, C, or Swift bindings for Apple silicon machine learning

tinygrad

  • Developers who want to read and modify how a deep learning stack works internally
  • Students learning how autograd and tensor compilers function
  • Researchers experimenting with low-level kernel code or new hardware backends
  • People who want a PyTorch-like training loop with a small codebase

Strengths

MLX

  • Familiar NumPy-like and PyTorch-like APIs lower the learning curve for many developers
  • Unified memory lets operations run on CPU or GPU without transferring data, per the README
  • Backed by Apple machine learning research and has examples for several well-known models

tinygrad

  • The compiler and IR are visible and hackable, according to the README
  • Its API is designed to resemble PyTorch, so familiar training loops carry over
  • Supports many backends including CPU, CUDA, Metal, OpenCL, AMD, NV and WebGPU

Not ideal for

MLX

  • Users who need to deploy on Windows or non-Apple hardware: the README lists CPU and GPU devices, and the Linux install options mention CUDA and CPU-only packages, so cross-platform support is narrower than general frameworks
  • People looking for a finished end-user application: MLX is a framework for building models, not a ready-made app

tinygrad

  • Teams needing a mature, production-proven ecosystem; the README says the library is not 1.0 yet
  • Users who need the broad set of pretrained models and tooling that PyTorch offers
  • Beginners looking for a high-level tutorial-first framework; the docs are external and the README is terse

Trade-offs

MLX

  • The README describes it as built for Apple silicon, so its main use case is limited to that hardware
  • The README is truncated and does not give detailed performance numbers or a full feature list, so some claims need checking in the docs

tinygrad

  • The README says it is not 1.0 yet, so APIs may change
  • Some features such as full vmap and pmap are not yet available, per the README comparison with JAX
  • Contribution rules are strict: large diffs, docs-only changes and AI-written PRs are closed without review

Getting started

MLX

Install on macOS

pip install mlx

Install CUDA backend on Linux

pip install mlx[cuda]
Step-by-step guide: how to use MLX

tinygrad

Clone the repository

git clone https://github.com/tinygrad/tinygrad.git

Enter the directory

cd tinygrad
Step-by-step guide: how to use tinygrad

Frequently asked questions

What is the difference between MLX and tinygrad?

MLX: An array framework for machine learning on Apple silicon, with a NumPy-style Python API. tinygrad: A small, readable deep learning library in Python that sits between PyTorch and micrograd. MLX suits researchers experimenting with machine learning models on a mac with apple silicon, while tinygrad suits developers who want to read and modify how a deep learning stack works internally.

Are MLX and tinygrad free?

MLX: Free and open source. tinygrad: Free and open source.

Is MLX or tinygrad more popular?

tinygrad has more GitHub stars (33.8k) than MLX (29k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to MLX and tinygrad?

Other options people compare them with include micrograd.

Keep comparing

micrograd vs tinygrad

Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.