RRepoGEO

REPOGEO REPORT · LITE

dphnAI/aphrodite-engine

Default branch main · commit 14e8de14 · scanned 5/23/2026, 8:01:57 AM

GitHub: 1,738 stars · 197 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 dphnAI/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

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

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening sentence to explicitly state LLM focus

    Why:

    CURRENT
    Aphrodite is an inference engine that optimizes the serving of HuggingFace-compatible models at scale.
    COPY-PASTE FIX
    Aphrodite is a high-performance inference engine for large language models (LLMs), optimizing the serving of HuggingFace-compatible models at scale.
  • mediumtopics#2
    Add more specific LLM-related topics

    Why:

    CURRENT
    api-rest, cuda, inference-engine, inferentia, intel, lora, machine-learning, rocm, speculative-decoding, tpu
    COPY-PASTE FIX
    api-rest, cuda, inference-engine, inferentia, intel, lora, machine-learning, rocm, speculative-decoding, tpu, llm-inference, large-language-models, model-serving, huggingface, quantization
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project website, documentation, or the main PygmalionAI site) to the 'Homepage' field in the repository settings.

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 dphnAI/aphrodite-engine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Triton Inference Server
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Triton Inference Server · recommended 2×
  2. vLLM · recommended 2×
  3. TensorRT-LLM · recommended 2×
  4. TGI (Text Generation Inference) by Hugging Face · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    How to efficiently serve large language models for many concurrent users with high throughput?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. vLLM
    3. TGI (Text Generation Inference) by Hugging Face
    4. OpenVINO
    5. Ray Serve
    6. DeepSpeed-MII (Model Inference Interface)
    7. TensorRT-LLM

    AI recommended 7 alternatives but never named dphnAI/aphrodite-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best inference engines for LLMs supporting quantization and distributed serving?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. NVIDIA Triton Inference Server
    3. TensorRT-LLM
    4. OpenVINO (Intel)
    5. DeepSpeed-MII (Microsoft)
    6. TGI (Text Generation Inference by Hugging Face)

    AI recommended 6 alternatives but never named dphnAI/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 dphnAI/aphrodite-engine?
    pass
    AI named dphnAI/aphrodite-engine explicitly

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

  • If a team adopts dphnAI/aphrodite-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dphnAI/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 dphnAI/aphrodite-engine solve, and who is the primary audience?
    pass
    AI named dphnAI/aphrodite-engine 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 dphnAI/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.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/dphnAI/aphrodite-engine.svg)](https://repogeo.com/en/r/dphnAI/aphrodite-engine)
HTML
<a href="https://repogeo.com/en/r/dphnAI/aphrodite-engine"><img src="https://repogeo.com/badge/dphnAI/aphrodite-engine.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

dphnAI/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