SoftwareFramework
tinygrad
Project record
tinygrad is a small end-to-end deep learning framework with a tensor library and autograd, an IR and compiler that fuse and lower kernels, a JIT, and modules for neural networks, optimizers, and datasets. Its API is similar to PyTorch's, and it supports both training and inference across several accelerator backends.17
- Website: Website (external site: tinygrad.org)
- Repository: Repository (external site: github.com)
- Documentation: Documentation (external site: docs.tinygrad.org)
- License: LICENSE (external site: github.com)
- Release notes: Reviewed release (external site: github.com)
Last reviewedEntry updated Documented release
Availability and license
Component reuse rights
A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.
- weights
- Unknown — no complete fact-level rights review
- code
- Reviewed qualifying license recorded — check scope and conditions
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
Items for a framework under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | Public repository in the tinygrad GitHub organization.1 |
| DocumentationIs user documentation published? | Public | Official documentation includes a quickstart, tutorials, API reference, runtime documentation, and developer notes.3 |
| InstallationAre installation instructions or packages publicly available? | Public | The recommended installation is from source (git clone, then pip install -e .); installing directly from the GitHub master branch with pip is also documented.13 |
| Supported platformsAre supported operating systems or hardware documented? | Public | The runtime documentation lists NV (NVIDIA Ampere, Ada, and Blackwell GPUs), AMD (CDNA3, CDNA4, RDNA3, and RDNA4 GPUs), QCOM (6xx-series GPUs), METAL (M1 or newer Macs), CUDA, OpenCL 2.0, CPU via clang or LLVM, and WebGPU via Dawn.4 |
| Release statusAre versioned releases published? | Public | Reviewed release tinygrad 0.14.0, published August 24, 2026. The documentation says tinygrad is not yet 1.0. This entry describes the project rather than one release.63 |
What it is useful for
Run and use notes
Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.
Organization context
U.S. eligibility
Project eligibility rests on documented governing or maintaining entities, not on contributors.
Sources
Email a correction (opens your email app; no account on USASI needed)Report a correction (opens GitHub)Edit this entry (opens GitHub)
This listing is not an endorsement, a safety assessment, or a federal approval.