Hazy Research (Stanford)
lab · United StatesScores
1 product on the map — 1 open.
Openness
5 high confidence- license
- MIT(OSI)
- source
- public
- core features withheld
- no
The LICENSE file contains the MIT text. The repository is public and the README describes no paid tier, enterprise edition or license-gated build, so the core is ungated.
- https://raw.githubusercontent.com/HazyResearch/ThunderKittens/main/LICENSE recorded 2026-09-30
LICENSE file is the verbatim MIT text, opening with "MIT License" and granting "Permission is hereby granted, free of charge, to any person obtaining a copy" of the software.
- https://raw.githubusercontent.com/HazyResearch/ThunderKittens/main/README.md recorded 2026-09-30
README closes with "This project is licensed under the terms of the MIT license." and states "ThunderKittens itself is a header-only library" that needs no installation. Searched the body for enterprise, license key, paid, proprietary and closed: none.
Adoption
2 low confidenceThunderKittens is a header library built from source with no package-registry presence, so GitHub stars are the only adoption signal available.
- https://api.github.com/repos/HazyResearch/ThunderKittens recorded 2026-08-18
Repo metadata - stargazers_count = 3,634 - for HazyResearch/ThunderKittens.
Capability
4 medium confidenceThunderKittens is a header-only tile library that also ships self-contained GEMM, attention and linear-attention kernels for Hopper and Blackwell GPUs. FastVideo vendors it as a submodule for its own kernels, which places it one step below Apache TVM.
- https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/.gitmodules recorded 2026-09-30
FastVideo .gitmodules declares a submodule "fastvideo-kernel/include/tk" at "path = fastvideo-kernel/include/tk" with "url = https://github.com/HazyResearch/ThunderKittens.git".
- https://raw.githubusercontent.com/HazyResearch/ThunderKittens/main/README.md recorded 2026-09-30
README says "We've provided a number of ThunderKittens kernels in the `kernels/` folder, which can be easily called from your PyTorch code." and lists attention "forwards / prefill and backwards kernels" with "causal and non-causal variants".
- https://raw.githubusercontent.com/HazyResearch/ThunderKittens/main/README.md recorded 2026-09-30
README says "ThunderKittens is mainly built and tested for Hopper and Blackwell GPUs." and that the 2.0 release "brings full support for Blackwell GPUs along with MXFP8 and NVFP4 precision".