REPOGEO REPORT · LITE

openvinotoolkit/nncf

Default branch develop · commit a5ec2116 · scanned 6/27/2026, 4:52:08 PM

GitHub: 1,176 stars · 297 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface openvinotoolkit/nncf, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README intro to clarify NNCF's unique role and core differentiator

Why:

CURRENT

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO&trade; with a minimal accuracy drop.

COPY-PASTE FIX

NNCF is the dedicated Neural Network Compression Framework designed specifically to enhance OpenVINO™ inference by providing a comprehensive suite of post-training and training-time algorithms for optimizing neural networks with minimal accuracy drop. Unlike general-purpose quantization tools or broader runtimes, NNCF offers a unified, hardware-aware approach to neural network compression, deeply integrated with OpenVINO™ for optimal performance on Intel hardware.

mediumcomparison#2

Add a dedicated comparison section (or link) in the README

Why:

COPY-PASTE FIX

Add a new section to the README, perhaps titled 'NNCF vs. Alternatives' or 'Why NNCF?', that explicitly outlines NNCF's unique advantages and positioning compared to common alternatives like PyTorch Quantization, TensorFlow Lite, and NVIDIA TensorRT. Focus on aspects like its deep OpenVINO integration, comprehensive suite of compression algorithms, and hardware-aware optimizations.

lowabout#3
Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIX
```
https://docs.openvino.ai/nncf
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface openvinotoolkit/nncf

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenVINO Toolkit

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenVINO Toolkit · recommended 2×
NVIDIA TensorRT · recommended 2×
ONNX Runtime · recommended 2×
TensorFlow Lite · recommended 2×
PyTorch Quantization · recommended 1×

CATEGORY QUERY
Need a framework for post-training neural network compression to speed up AI inference.
you: not recommended
AI recommended (in order):
1. OpenVINO Toolkit
2. NVIDIA TensorRT
3. ONNX Runtime
4. PyTorch Quantization
5. TensorFlow Lite
6. Neural Network Compression Framework (NNCF)
AI recommended 6 alternatives but never named openvinotoolkit/nncf. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to reduce deep learning model size using quantization and pruning techniques?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite
2. PyTorch Mobile
3. PyTorch Quantization Toolkit
4. ONNX Runtime
5. NVIDIA TensorRT
6. OpenVINO Toolkit
7. Distiller
AI recommended 7 alternatives but never named openvinotoolkit/nncf. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of openvinotoolkit/nncf?
pass
AI did not name openvinotoolkit/nncf — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts openvinotoolkit/nncf in production, what risks or prerequisites should they evaluate first?
pass
AI named openvinotoolkit/nncf explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo openvinotoolkit/nncf solve, and who is the primary audience?
pass
AI named openvinotoolkit/nncf explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/openvinotoolkit/nncf.svg)](https://repogeo.com/en/r/openvinotoolkit/nncf)

HTML

<a href="https://repogeo.com/en/r/openvinotoolkit/nncf"><img src="https://repogeo.com/badge/openvinotoolkit/nncf.svg" alt="RepoGEO" /></a>

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openvinotoolkit/nncf — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite