RRepoGEO

REPOGEO REPORT · LITE

alibaba/rtp-llm

Default branch main · commit 6b89624c · scanned 5/23/2026, 11:22:05 AM

GitHub: 1,126 stars · 193 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 alibaba/rtp-llm, 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 the 'About' section to the top of the README

    Why:

    COPY-PASTE FIX
    ## About
RTP-LLM is a Large Language Model (LLM) inference acceleration engine developed by Alibaba's Foundation Model Inference Team. It is widely used within Alibaba Group, supporting LLM service across multiple business units including Taobao, Tmall, Idlefish, Cainiao, Amap, Ele.me, AE, and Lazada.

RTP-LLM is a sub-project of the havenask project.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://github.com/alibaba/rtp-llm#documentation
  • lowreadme#3
    Add a 'Diverse Hardware Support' bullet point to 'Key Features'

    Why:

    COPY-PASTE FIX
    *   **Diverse Hardware Support:** Currently supports Qwen series models and BERT embedding models on Yitian ARM CPU. Active development is underway to expand support for AMD ROCm, Intel CPU, and broader ARM CPU architectures.

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 alibaba/rtp-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO · recommended 2×
  2. vLLM · recommended 1×
  3. TGI · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. TensorRT-LLM · recommended 1×
  • CATEGORY QUERY
    What are the best open-source engines for high-performance LLM inference serving?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. DeepSpeed-MII
    4. TensorRT-LLM
    5. OpenVINO

    AI recommended 5 alternatives but never named alibaba/rtp-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I optimize large language model inference for diverse hardware platforms?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. Apache TVM
    5. Qualcomm AI Engine Direct (QNN)
    6. Apple Core ML
    7. TensorFlow Lite
    8. MediaPipe

    AI recommended 8 alternatives but never named alibaba/rtp-llm. 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 alibaba/rtp-llm?
    pass
    AI named alibaba/rtp-llm explicitly

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

  • If a team adopts alibaba/rtp-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alibaba/rtp-llm 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 alibaba/rtp-llm solve, and who is the primary audience?
    pass
    AI named alibaba/rtp-llm explicitly

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

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alibaba/rtp-llm — 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