micrograd 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.
Open source · MIT
A tiny educational autograd engine and small neural net library with a PyTorch-like API.
Open source · MIT
A small, readable deep learning library in Python that sits between PyTorch and micrograd.
The short answer
Choose micrograd if you need…
- Students learning how backpropagation works under the hood
- Developers who want to read a complete autograd implementation in a short codebase
- Educators demonstrating neural net training on small examples like binary classification
- Anyone curious about how PyTorch-style APIs might be built
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
| micrograd | tinygrad | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 18.6k | 33.8k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Jupyter Notebook | Python |
| Last code update | 3 months ago | 4 days ago |
What each one is
micrograd
micrograd is a very small Python library that performs automatic differentiation, the calculus step that lets neural networks learn. It works on single scalar values, breaking every operation into tiny adds and multiplies, and it includes a small neural network layer on top with an API similar to PyTorch. The README describes it as potentially useful for educational purposes, and it is meant to be read and understood rather than used for large-scale work.
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
micrograd
- Students learning how backpropagation works under the hood
- Developers who want to read a complete autograd implementation in a short codebase
- Educators demonstrating neural net training on small examples like binary classification
- Anyone curious about how PyTorch-style APIs might be built
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
micrograd
- Very small codebase, which the README says is about 100 lines for the engine and 50 for the nn library
- Includes runnable notebooks for training and graph visualization
- MIT licensed and easy to install with pip
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
micrograd
- Training large models, since it operates on scalar values one at a time and the README does not claim performance for big workloads
- Production machine learning pipelines, where mature frameworks such as PyTorch are the usual choice
- Users who need GPU or tensor-level computation, which the README does not mention
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
micrograd
- Scalar-only operations make it far too slow and limited for real-world model sizes
- Running the tests requires installing PyTorch separately
- The README is brief and offers little beyond the example code and notebooks
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
tinygrad
Clone the repository
git clone https://github.com/tinygrad/tinygrad.gitEnter the directory
cd tinygradFrequently asked questions
What is the difference between micrograd and tinygrad?
micrograd: A tiny educational autograd engine and small neural net library with a PyTorch-like API. tinygrad: A small, readable deep learning library in Python that sits between PyTorch and micrograd. micrograd suits students learning how backpropagation works under the hood, while tinygrad suits developers who want to read and modify how a deep learning stack works internally.
Are micrograd and tinygrad free?
micrograd: Free and open source. tinygrad: Free and open source.
Which is easier to set up, micrograd or tinygrad?
micrograd is easier to get started with (rated beginner); tinygrad is rated intermediate and usually needs more setup.
Is micrograd or tinygrad more popular?
tinygrad has more GitHub stars (33.8k) than micrograd (18.6k). Popularity isn't the same as fit — check which one matches your use case above.
Keep comparing
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.