# pytorch/QNNPACK

Quantized Neural Network PACKage - mobile-optimized implementation of quantized neural network operators

Repository: https://github.com/pytorch/QNNPACK
Canonical: https://ross.abutalabs.com/products/qnnpack
Homepage: https://code.fb.com/ml-applications/qnnpack/
Language: C
License: NOASSERTION
License Family: other
Archived: true
Last push: 2019-08-28T20:40:59+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2872, "days_push": 2562, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1551, forks 223 (observed 2026-08-28T04:05:02.302953+00:00)

## What it is
QNNPACK is a mobile-optimized C library of high-performance kernels for 8-bit quantized neural network operators such as convolution, pooling, and fully connected layers. It serves as a low-level performance primitive integrated into PyTorch (via Caffe2) rather than being used directly by researchers.

## Use cases
- run quantized neural network inference on mobile phones
- speed up depthwise convolutions in MobileNet-style models on ARM devices
- cross-compile quantized operator kernels for Android and iOS
- integrate fast 8-bit inference primitives into a deep learning framework
- perform real-time image classification on low-power mobile hardware

## When to choose
- you need fast quantized 8-bit inference on ARM mobile CPUs
- you are building or optimizing a deep learning runtime for Android or iOS
- you need low-level NEON-optimized kernels for convolutions and pooling

## When to avoid
- you want a high-level framework or model training tool
- you target x86 servers or GPU inference
- you need a maintained library with recent updates and broad operator coverage

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-inference
- domain: machine-learning, mobile-development, computer-vision, deep-learning
- platform: cross-platform, cpp
- tags: quantization, inference-kernels, arm-neon, mobile-ai, 8-bit-operators, android, ios

## Member repositories
- pytorch/QNNPACK (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.302953+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 2026-08-30T04:30:17.015860+00:00, confidence not recorded.
  - readme: https://github.com/pytorch/QNNPACK (fetched 2026-08-28T04:05:02.302953+00:00, sha d8168b627a20)
  - homepage: https://code.fb.com/ml-applications/qnnpack/ (fetched 2026-08-29T11:30:48.712750+00:00, sha b1f2977fcc4a)
- Data as of 2026-08-30T08:39:29.467469+00:00.