RRepoGEO

REPOGEO REPORT · LITE

intel/intel-extension-for-pytorch

Default branch main · commit 6d3ba89c · scanned 7/1/2026, 8:21:41 PM

GitHub: 2,014 stars · 318 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 intel/intel-extension-for-pytorch, 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
  • highabout#1
    Update repository description to reflect archived status

    Why:

    CURRENT
    A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
    COPY-PASTE FIX
    ARCHIVED: This project is no longer actively developed or supported. It was a Python package for extending PyTorch to optimize performance on Intel platforms. Users should now use PyTorch directly.
  • hightopics#2
    Add 'archived' and 'legacy' topics

    Why:

    CURRENT
    deep-learning, intel, machine-learning, neural-network, pytorch, quantization
    COPY-PASTE FIX
    deep-learning, intel, machine-learning, neural-network, pytorch, quantization, archived, legacy
  • mediumhomepage#3
    Add homepage link to PyTorch.org

    Why:

    COPY-PASTE FIX
    https://pytorch.org/

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 intel/intel-extension-for-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Intel Extension for PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Intel Extension for PyTorch · recommended 1×
  2. PyTorch `torch.compile` · recommended 1×
  3. Intel OpenVINO Toolkit · recommended 1×
  4. Intel oneDNN · recommended 1×
  5. TorchScript · recommended 1×
  • CATEGORY QUERY
    How to optimize PyTorch deep learning models for better performance on Intel CPUs and GPUs?
    you: not recommended
    AI recommended (in order):
    1. Intel Extension for PyTorch
    2. PyTorch `torch.compile`
    3. Intel OpenVINO Toolkit
    4. Intel oneDNN
    5. TorchScript
    6. `torch.quantization` module
    7. OpenVINO's Post-Training Optimization Tool
    8. `torch.nn.DataParallel`
    9. `torch.distributed.DistributedDataParallel`

    AI recommended 9 alternatives but never named intel/intel-extension-for-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for PyTorch extensions to accelerate neural network inference and quantization on Intel platforms.
    you: not recommended
    AI recommended (in order):
    1. Intel Extension for PyTorch (IPEX)
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. torch.compile
    5. Intel Neural Compressor (INC)
    6. oneDNN

    AI recommended 6 alternatives but never named intel/intel-extension-for-pytorch. 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 intel/intel-extension-for-pytorch?
    pass
    AI did not name intel/intel-extension-for-pytorch — 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 intel/intel-extension-for-pytorch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named intel/intel-extension-for-pytorch 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 intel/intel-extension-for-pytorch solve, and who is the primary audience?
    pass
    AI named intel/intel-extension-for-pytorch explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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intel/intel-extension-for-pytorch — 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