RRepoGEO

REPOGEO REPORT · LITE

neuralmagic/deepsparse

Default branch main · commit eed77977 · scanned 5/19/2026, 9:37:01 AM

GitHub: 3,162 stars · 192 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
    Update README H1/H4 to reflect deprecation and successor

    Why:

    CURRENT
    <h1 style="display: flex; align-items: center;" ><span>&nbsp;&nbsp;DeepSparse</span></h1><h4>Sparsity-aware deep learning inference runtime for CPUs</h4>
    COPY-PASTE FIX
    <h1 style="display: flex; align-items: center;" ><span>&nbsp;&nbsp;DeepSparse (DEPRECATED)</span></h1><h4>Sparsity-aware deep learning inference runtime for CPUs. Development ceased June 2025. Successor projects are available at ai.redhat.com.</h4>
  • highabout#2
    Update repository description to reflect deprecation

    Why:

    CURRENT
    Sparsity-aware deep learning inference runtime for CPUs
    COPY-PASTE FIX
    DEPRECATED: Sparsity-aware deep learning inference runtime for CPUs. Development ceased June 2025. Successor projects are available at ai.redhat.com.
  • mediumreadme#3
    Add explicit license clarification to README

    Why:

    COPY-PASTE FIX
    ## License
This project is provided under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on the applicable license(s).

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
0%
Of all named tools, what % are you?
Top rival
microsoft/onnxruntime
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/onnxruntime · recommended 1×
  2. openvinotoolkit/openvino · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. apache/tvm · recommended 1×
  • CATEGORY QUERY
    Seeking an efficient runtime for deep learning model predictions on commodity CPUs.
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime (microsoft/onnxruntime)
    2. OpenVINO Toolkit (openvinotoolkit/openvino)
    3. TensorFlow Lite (tensorflow/tensorflow)
    4. PyTorch Mobile / LibTorch (pytorch/pytorch)
    5. Apache TVM (apache/tvm)
    6. NCNN (Tencent/ncnn)
    7. MNN (alibaba/MNN)

    AI recommended 7 alternatives but never named neuralmagic/deepsparse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to accelerate large language model inference using sparsification on CPU?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO
    2. ONNX Runtime
    3. Hugging Face Optimum
    4. PyTorch
    5. TensorFlow Lite

    AI recommended 5 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?

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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