REPOGEO REPORT · LITE

neuralmagic/deepsparse

Default branch main · commit eed77977 · scanned 6/30/2026, 6:11:56 PM

GitHub: 3,159 stars · 191 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 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 neuralmagic/deepsparse, 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

Add a concise statement about DeepSparse's historical significance to the README

Why:

CURRENT

<h4>Sparsity-aware deep learning inference runtime for CPUs</h4>

## 🚨 2025 End of Life Announcement: DeepSparse, SparseML, SparseZoo, and Sparsify

COPY-PASTE FIX

<h4>Sparsity-aware deep learning inference runtime for CPUs</h4>
  <p>DeepSparse was a pioneering project that enabled high-performance inference for sparse neural networks on commodity CPUs, significantly advancing efficient AI deployment before its deprecation.</p>

## 🚨 2025 End of Life Announcement: DeepSparse, SparseML, SparseZoo, and Sparsify

mediumtopics#2

Add 'deprecated' to the repository topics

Why:

CURRENT

computer-vision, cpus, deepsparse, inference, llm-inference, machinelearning, nlp, object-detection, onnx, performance, pretrained-models, pruning, quantization, sparsification

COPY-PASTE FIX

computer-vision, cpus, deepsparse, deprecated, inference, llm-inference, machinelearning, nlp, object-detection, onnx, performance, pretrained-models, pruning, quantization, sparsification

lowreadme#3

Clarify the project's license in the README

Why:

COPY-PASTE FIX

## License
This project is licensed under [specify license(s) as found in the LICENSE file].

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 neuralmagic/deepsparse

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×
ONNX Runtime · recommended 2×
TensorFlow Lite · recommended 2×
PyTorch · recommended 2×
oneDNN · recommended 1×

CATEGORY QUERY
What tools optimize deep learning inference performance on standard CPUs?
you: not recommended
AI recommended (in order):
1. OpenVINO Toolkit
2. ONNX Runtime
3. oneDNN
4. TensorFlow Lite
5. PyTorch
6. TVM
AI recommended 6 alternatives but never named neuralmagic/deepsparse. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to achieve faster neural network inference by applying model sparsification?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. TensorFlow Model Optimization Toolkit
4. TensorFlow Lite
5. OpenVINO Toolkit
6. Model Optimizer
7. Post-Training Optimization Tool (POT)
8. NVIDIA TensorRT
9. ONNX Runtime
10. Neural Network Compression Framework (NNCF) by Intel
11. DeepSparse (by Neural Magic)
AI recommended 11 alternatives but never named neuralmagic/deepsparse. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 neuralmagic/deepsparse?
pass
AI named neuralmagic/deepsparse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts neuralmagic/deepsparse in production, what risks or prerequisites should they evaluate first?
pass
AI named neuralmagic/deepsparse 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 neuralmagic/deepsparse solve, and who is the primary audience?
pass
AI named neuralmagic/deepsparse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of neuralmagic/deepsparse. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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

HTML

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

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neuralmagic/deepsparse — 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