ThunderKittens

license

MIT(OSI)

source

public

core-gated

ungated

MIT license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.

• https://github.com/HazyResearch/ThunderKittens/blob/main/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim MIT text

• https://api.github.com/repos/HazyResearch/ThunderKittens recorded 2026-08-18

  Repo metadata - license spdx_id MIT, private false, archived false, default branch main - for HazyResearch/ThunderKittens.

• https://github.com/HazyResearch/ThunderKittens/blob/main/README.md recorded 2026-08-18

  README describes a CUDA-embedded framework of tile primitives for writing fast deep-learning kernels, designed so authors can drop back to raw CUDA where the abstraction runs out, with no paid tier, enterprise edition or license-key-gated build beside it.

3,634 GitHub stars, which lands in the 1K-10K stars band of the stars scale, level 2. A header library built from source with no package registry presence, so the stars scale applies with its cap of 3.

• https://api.github.com/repos/HazyResearch/ThunderKittens recorded 2026-08-18

  Repo metadata - stargazers_count = 3,634 - for HazyResearch/ThunderKittens.

Banded on the category feature matrix as narrow kernel set or single-pass utility. Placed two bands below the tensorrt anchor, on a matrix that bands on how much of the model-to-hardware transformation pipeline a product performs, over how many inputs and targets.

• https://github.com/HazyResearch/ThunderKittens/blob/main/README.md recorded 2026-08-18

  README still documents a CUDA-embedded framework of tile primitives for writing fast deep-learning kernels, designed so authors can drop back to raw CUDA where the abstraction runs out.