Intel Neural Compressor

license

Apache-2.0(OSI)

source

public

core-gated

ungated

Apache-2.0 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/intel/neural-compressor/blob/main/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim Apache-2.0 text

• https://api.github.com/repos/intel/neural-compressor recorded 2026-08-18

  Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch main - for intel/neural-compressor.

• https://github.com/intel/neural-compressor/blob/main/README.md recorded 2026-08-18

  README describes static and dynamic quantization, SmoothQuant, weight-only quantization, quantization-aware training, mixed precision and pruning across PyTorch, TensorFlow and ONNX Runtime, down to INT4 and the MX and NVFP4 formats, with no paid tier, enterprise edition or license-key-gated build beside it.

33,576 PyPI downloads of `neural-compressor` in the trailing 30 days, which lands in the 10K-100K band of the software usage scale, level 2.

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

  last_month downloads = 33,576 for neural-compressor

Banded on the category feature matrix as multi-technique optimization toolchain. Placed one band 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/intel/neural-compressor/blob/main/README.md recorded 2026-08-18

  README still documents static and dynamic quantization, SmoothQuant, weight-only quantization, quantization-aware training, mixed precision and pruning across PyTorch, TensorFlow and ONNX Runtime, down to INT4 and the MX and NVFP4 formats.