RRepoGEO

REPOGEO REPORT · LITE

OpenPPL/ppl.nn

Default branch master · commit bb84dc99 · scanned 5/20/2026, 3:08:01 AM

GitHub: 1,369 stars · 221 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
35 /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
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 OpenPPL/ppl.nn, 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
    Clarify README's opening statement to highlight LLM optimization

    Why:

    CURRENT
    PPLNN, which is short for "PPLNN is a Primitive Library for Neural Network", is a high-performance deep-learning inference engine for efficient AI inferencing. It can run various ONNX models and has better support for OpenMMLab.
    COPY-PASTE FIX
    PPLNN (Primitive Library for Neural Network) is a high-performance deep-learning inference engine for efficient AI inferencing, with a strong focus on optimizing large language models (LLMs). It supports various ONNX models and offers advanced features like Flash Attention, Group-query Attention, and INT8 quantization for LLM inference, alongside robust support for OpenMMLab.
  • highhomepage#2
    Add project homepage to repository metadata

    Why:

    COPY-PASTE FIX
    https://openppl.ai/
  • mediumtopics#3
    Expand repository topics to include LLM-specific keywords

    Why:

    CURRENT
    deep-learning, neural-network, onnx
    COPY-PASTE FIX
    deep-learning, neural-network, onnx, llm, large-language-models, llm-inference, ai-inference, model-optimization, flash-attention

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 OpenPPL/ppl.nn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. NVIDIA TensorRT · recommended 1×
  3. OpenVINO Toolkit · recommended 1×
  4. TVM · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Seeking a high-performance library for efficient deep learning inference, especially with ONNX models.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. TVM
    5. PyTorch
    6. TensorFlow Lite

    AI recommended 6 alternatives but never named OpenPPL/ppl.nn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for optimizing large language model inference with advanced attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. DeepSpeed-MII
    3. NVIDIA TensorRT-LLM
    4. Triton Inference Server
    5. FasterTransformer
    6. OpenVINO
    7. ONNX Runtime

    AI recommended 7 alternatives but never named OpenPPL/ppl.nn. 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 OpenPPL/ppl.nn?
    pass
    AI named OpenPPL/ppl.nn explicitly

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

  • If a team adopts OpenPPL/ppl.nn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenPPL/ppl.nn 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 OpenPPL/ppl.nn solve, and who is the primary audience?
    pass
    AI named OpenPPL/ppl.nn explicitly

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

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OpenPPL/ppl.nn — 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