RRepoGEO

REPOGEO REPORT · LITE

aphrodite-engine/aphrodite-engine

Default branch main · commit 14e8de14 · scanned 6/17/2026, 3:41:52 PM

GitHub: 1,767 stars · 200 forks

AI VISIBILITY SCORE
22 /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
1 / 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 aphrodite-engine/aphrodite-engine, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant project or organization homepage URL to the repository's "About" section.
  • mediumcomparison#2
    Add a "Why Aphrodite?" or "Comparison" section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., "Why Aphrodite?" or "Comparison to Alternatives," explicitly outlining its unique advantages over competitors like vLLM, Triton Inference Server, and TensorRT-LLM.

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 aphrodite-engine/aphrodite-engine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. TensorRT-LLM · recommended 2×
  3. OpenVINO · recommended 2×
  4. Triton Inference Server · recommended 1×
  5. FasterTransformer · recommended 1×
  • CATEGORY QUERY
    What are the best LLM inference engines for high-throughput, low-latency serving at scale?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Triton Inference Server
    3. FasterTransformer
    4. TensorRT-LLM
    5. OpenVINO
    6. DeepSpeed-MII
    7. LightLLM
    8. llama.cpp

    AI recommended 8 alternatives but never named aphrodite-engine/aphrodite-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently deploy and serve large language models with quantization and distributed inference?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. vLLM
    3. Hugging Face TGI (Text Generation Inference)
    4. DeepSpeed-MII (Model Inference Interface)
    5. TensorRT-LLM
    6. OpenVINO
    7. ONNX Runtime

    AI recommended 7 alternatives but never named aphrodite-engine/aphrodite-engine. 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 aphrodite-engine/aphrodite-engine?
    pass
    AI did not name aphrodite-engine/aphrodite-engine — 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 aphrodite-engine/aphrodite-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aphrodite-engine/aphrodite-engine 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 aphrodite-engine/aphrodite-engine solve, and who is the primary audience?
    pass
    AI did not name aphrodite-engine/aphrodite-engine — 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?

Embed your GEO score

Drop this badge into the README of aphrodite-engine/aphrodite-engine. 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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aphrodite-engine/aphrodite-engine — 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