NVIDIA Model Optimizer

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/NVIDIA/Model-Optimizer/blob/main/LICENSE recorded 2026-08-18

  LICENSE file is the verbatim Apache-2.0 text

• https://api.github.com/repos/NVIDIA/Model-Optimizer recorded 2026-08-18

  Repo metadata - license spdx_id Apache-2.0, private false, archived false, default branch main - for NVIDIA/Model-Optimizer.

• https://github.com/NVIDIA/Model-Optimizer/blob/main/README.md recorded 2026-08-18

  README describes a library of quantization, pruning, neural architecture search, distillation, speculative decoding and sparsity techniques taking Hugging Face, PyTorch or ONNX inputs and exporting optimized checkpoints for TensorRT-LLM, TensorRT and vLLM, with no paid tier, enterprise edition or license-key-gated build beside it.

418,452 PyPI downloads of `nvidia-modelopt` in the trailing 30 days, which lands in the 100K-1M band of the software usage scale, level 3.

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

  last_month downloads = 418,452 for nvidia-modelopt

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/NVIDIA/Model-Optimizer/blob/main/README.md recorded 2026-08-18

  README still documents a library of quantization, pruning, neural architecture search, distillation, speculative decoding and sparsity techniques taking Hugging Face, PyTorch or ONNX inputs and exporting optimized checkpoints for TensorRT-LLM, TensorRT and vLLM.