RRepoGEO

REPOGEO REPORT · LITE

zhihu/ZhiLight

Default branch main · commit ee844680 · scanned 6/5/2026, 3:11:57 AM

GitHub: 905 stars · 102 forks

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 zhihu/ZhiLight, 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 README's opening to emphasize LLM inference engine category

    Why:

    CURRENT
    ✨ __ZhiLight__ ✨is a highly optimized LLM inference engine developed by Zhihu and ModelBest Inc. The "Zhi" in its name stands for **Z**hihu. ZhiLight can accelerate the inference of models like Llama and its variants, especially on PCIe-based GPUs. Compared to mainstream open-source inference engines, for example, vllm, it has significant performance advantages.
    COPY-PASTE FIX
    ✨ __ZhiLight__ ✨: A highly optimized **LLM inference acceleration engine** for Llama and its variants, developed by Zhihu and ModelBest Inc. It delivers significant performance advantages over mainstream open-source engines like vLLM, especially on PCIe-based GPUs.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a URL to an official project page, documentation, or a dedicated section on Zhihu's tech blog.
  • lowreadme#3
    Add a dedicated 'Why ZhiLight?' or 'Key Differentiators' section

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., 'Why ZhiLight?' or 'Key Differentiators', that explicitly outlines ZhiLight's unique advantages (e.g., 'dual streams', 'host all-reduce based on SIMD', 'fused batch attention') and how they compare to competitors like vLLM.

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 zhihu/ZhiLight
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. vLLM · recommended 2×
  3. OpenVINO · recommended 2×
  4. NVIDIA TensorRT · recommended 1×
  5. DeepSpeed-MII · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model inference on PCIe GPUs for better throughput?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. DeepSpeed-MII
    3. Hugging Face Optimum
    4. ONNX Runtime
    5. NVIDIA FasterTransformer
    6. vLLM
    7. OpenVINO

    AI recommended 7 alternatives but never named zhihu/ZhiLight. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient LLM serving solutions supporting quantized models and custom memory management?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference) by Hugging Face
    3. TensorRT-LLM
    4. DeepSpeed-MII (Model Inference Interface)
    5. OpenVINO
    6. ONNX Runtime

    AI recommended 6 alternatives but never named zhihu/ZhiLight. 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 zhihu/ZhiLight?
    pass
    AI named zhihu/ZhiLight explicitly

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

  • If a team adopts zhihu/ZhiLight in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhihu/ZhiLight 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 zhihu/ZhiLight solve, and who is the primary audience?
    pass
    AI named zhihu/ZhiLight 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 zhihu/ZhiLight. 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)
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HTML
<a href="https://repogeo.com/en/r/zhihu/ZhiLight"><img src="https://repogeo.com/badge/zhihu/ZhiLight.svg" alt="RepoGEO" /></a>
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zhihu/ZhiLight — 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