bitsandbytes

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/bitsandbytes-foundation/bitsandbytes/blob/main/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim MIT text

• https://api.github.com/repos/bitsandbytes-foundation/bitsandbytes recorded 2026-08-18

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

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

  README describes k-bit quantization for PyTorch through 8-bit block-wise optimizers, the LLM.int8() mixed-precision inference path with separate outlier handling, and 4-bit NF4 quantization for QLoRA, with no paid tier, enterprise edition or license-key-gated build beside it.

5,708,239 PyPI downloads of `bitsandbytes` in the trailing 30 days, which lands in the 1M-10M band of the software usage scale, level 4.

• https://pypistats.org/api/packages/bitsandbytes/recent recorded 2026-08-18

  last_month downloads = 5,708,239 for bitsandbytes

Banded on the category feature matrix as one optimization family, applied broadly. Placed two bands below the apache-tvm 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/bitsandbytes-foundation/bitsandbytes/blob/main/README.md recorded 2026-08-18

  README still documents k-bit quantization for PyTorch through 8-bit block-wise optimizers, the LLM.int8() mixed-precision inference path with separate outlier handling, and 4-bit NF4 quantization for QLoRA.