Package · PyPI

nvidia-modelopt

PyPI · v0.47.0 · Apache-2.0

Nvidia Model Optimizer: A unified library of SOTA model optimization techniques like quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

Install

pip install nvidia-modelopt

Adoption

Adoption
Not published by PyPI
Not published
Popularity
Not published by PyPI
Not published
Dependents
Not published by PyPI
Not published
Maintenance
0.94
DerivedRelease cadence and recency, 0 to 1
Releases per year
17
41 versions over its life

Growth by period

PyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.

7 days
Not published by PyPI
Not published
30 days
Not published by PyPI
Not published
90 days
Not published by PyPI
Not published
1 year
Not published by PyPI
Not published

Adoption history

Adoption history

PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.

Facts from the registry

Latest version
v0.47.0
41 versions
First published
APR 30, 2024
Last release
OCT 19, 2026
6d ago
Licence
Apache-2.0

Repository

In the Crest catalog

NVIDIA/Model-Optimizer

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

Stars
5.2K
Forks
719
Language
Python
Last push
23d ago
Last release
31d ago

Linked by the package’s own registry metadata.GitHub (opens in a new tab)

Dependencies latest version · v0.47.0

Runtime 15

  • PyYAML>=6.0
  • ninjaAny version
  • numpyAny version
  • nvidia-ml-py>=12
  • omegaconf>=2.3.0
  • packagingAny version
  • pulp<4.0
  • pydantic>=2.0
  • regexAny version
  • richAny version
  • safetensorsAny version
  • scipyAny version
  • setuptools>=80
  • torch>=2.8
  • tqdmAny version

Related news

No published news event mentions this package or its repository yet.