# openvinotoolkit/nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

Repository: https://github.com/openvinotoolkit/nncf
Canonical: https://ross.abutalabs.com/products/nncf
Language: Python
License: Apache-2.0
License Family: permissive
Topics: quantization, pruning, sparsity, quantization-aware-training, mixed-precision-training, compression, semantic-segmentation, object-detection, classification, nlp, bert, transformers, pytorch, tensorflow, onnx, openvino, deep-learning, genai, llm
Last push: 2026-09-02T07:50:10+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 84, longevity 100
- inputs: {"age_days": 2303, "days_push": 0, "days_rel": 28, "gap_med": 63, "n_releases_24m": 12}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1197, forks 302 (observed 2026-09-03T02:15:15.856455+00:00)

## What it is
NNCF is Intel's Neural Network Compression Framework, a Python library providing post-training and training-time compression algorithms (quantization, pruning, sparsity) for optimizing neural network inference with OpenVINO. It supports models from PyTorch, TorchFX, ONNX, and OpenVINO, including LLM weight compression.

## Use cases
- quantize a model for faster OpenVINO inference
- compress LLM weights to run on CPU
- apply post-training quantization with minimal accuracy drop
- prune or sparsify a PyTorch model
- optimize object detection or segmentation models for deployment
- quantize ONNX models

## When to choose
- you deploy models with OpenVINO and want smaller/faster inference
- you need quantization-aware training or post-training quantization for PyTorch/ONNX models
- you want to compress LLMs for Intel hardware

## When to avoid
- you target GPU inference with TensorRT or other non-OpenVINO runtimes
- you need compression for frameworks like JAX or Keras
- you want a general-purpose training framework rather than compression

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-inference
- domain: deep-learning, machine-learning, large-language-models, developer-tools
- platform: python, windows
- tags: quantization, pruning, sparsity, model-compression, openvino, pytorch, onnx, post-training-quantization, inference-optimization, linux, macos, gpu

## Member repositories
- openvinotoolkit/nncf (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:15.856455+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:22:31.343107+00:00, confidence not recorded.
  - readme: https://github.com/openvinotoolkit/nncf (fetched 2026-09-03T02:15:15.856455+00:00, sha 9e0ea79dc367)
  - registry_pypi: https://pypi.org/pypi/nncf/json (fetched 2026-08-29T12:30:02.520314+00:00, sha 9e012c4fbd47)
- Data as of 2026-08-30T08:39:29.467469+00:00.