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tinygrad

tinygrad is a deep learning stack for neural network training, combining a tensor library, compiler, just-in-time compilation, and training tools.

Place
#11 of 35
Score
6.4
tinygrad's website

What tinygrad is

tinygrad is a deep learning stack for neural network training, combining a tensor library, compiler, just-in-time compilation, and training tools. It supports forward and backward passes with automatic differentiation. Tensor operations are lazy and run when a tensor is realized. The library includes neural network classes, optimizers, and tools for loading and saving state. TinyJit can capture and replay kernels in a decorated function, and tensors can be sharded across multiple GPUs.

Listed accelerators include OpenCL, CPU, Metal, CUDA, AMD, NV, Qualcomm, and WebGPU. The project says tinygrad is used in openpilot to run its driving model on a Snapdragon 845 GPU. It supports Python and local training, and the GitHub repository identifies its license as MIT. Installation is documented through pip, though the project recommends installing from source. The documentation says tinygrad is not yet version 1.0, while noting the API has been stable for a while. Its FAQ says it is not faster than PyTorch for most use cases. Development discussion is directed to GitHub and Discord.

Good

  • Supports forward and backward autodiff
  • TinyJit can capture and replay kernels
  • Tensors can be sharded across multiple GPUs
  • MIT-licensed and free

Watch out

  • Not yet version 1.0
  • Not faster than PyTorch for most use cases
  • Project recommends installation from source

Who it suits

tinygrad suits developers working on deep learning and neural network training who want a Python stack with autodiff, JIT, and multiple listed accelerators. It may be less suited to users whose main requirement is outperforming PyTorch in most use cases.

Verdict

tinygrad provides a free Python stack for neural network training, with lazy tensors, JIT, and multiple accelerator options. The project is not yet version 1.0, and its FAQ cautions that it is not faster than PyTorch for most use cases.

Plans and prices

From tinygrad's own pricing page, checked 7 Oct 2026.

All about pricing
PlanPriceWhat it gives you
tinygradFreeOpen source library · installation from source or pip

The facts

Each one read from tinygrad's own pages, with the date it was checked.

What it does

Accelerators
Listed accelerators include OpenCL, CPU, Metal, CUDA, AMD, NV, Qualcomm, and WebGPU.checked 7 Oct 2026
Autodiff
The project supports forward and backward passes with autodiff.checked 7 Oct 2026
Community support
The project directs development discussion to GitHub and Discord.checked 7 Oct 2026
Installation
The project recommends installing from source and also documents installation with pip.checked 7 Oct 2026
Integration
The project says tinygrad is used in openpilot to run its driving model on a Snapdragon 845 GPU.checked 7 Oct 2026
Intended users
The project describes tinygrad as a framework for deep learning and neural network training.checked 7 Oct 2026
JIT
tinygrad provides TinyJit to capture and replay kernels in a decorated function.checked 7 Oct 2026
Lazy execution
Tensor operations are lazy and run when the tensor is realized.checked 7 Oct 2026
License
The GitHub repository identifies the project license as MIT.checked 7 Oct 2026
Maturity
The documentation says tinygrad is not yet version 1.0, while noting its API has been stable for a while.checked 7 Oct 2026
Multi GPU
The documentation says tensors can be sharded across multiple GPUs.checked 7 Oct 2026
Neural networks
The library includes neural network classes, optimizers, and state load/save management.checked 7 Oct 2026
Performance caveat
The project FAQ says tinygrad is not yet faster than PyTorch for most use cases.checked 7 Oct 2026
Purpose
tinygrad is an end-to-end deep learning stack with a tensor library, compiler, JIT, and tools for training.checked 7 Oct 2026

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