RRepoGEO

REPOGEO REPORT · LITE

noonghunna/club-3090

Default branch master · commit 21d01bda · scanned 6/6/2026, 10:27:08 PM

GitHub: 1,280 stars · 66 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 noonghunna/club-3090, 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
  • hightopics#1
    Add specific topics to clarify the repository's domain

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, large-language-models, rtx-3090, rtx-4090, rtx-5090, vllm, llama-cpp, ik-llama, gpu-inference, cuda, deep-learning, machine-learning, ai-inference, local-llm
  • mediumreadme#2
    Reinforce the core purpose in the README's opening sentence

    Why:

    CURRENT
    **Recipes for serving LLMs locally on RTX 3090s.** Multi-engine (vLLM, llama.cpp, ik_llama), multi-model, model-agnostic by design.
    COPY-PASTE FIX
    **This repository provides community recipes for serving Large Language Models (LLMs) locally on RTX 3090s.** It's multi-engine (vLLM, llama.cpp, ik_llama), multi-model, and model-agnostic by design.
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://github.com/noonghunna/club-3090

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 noonghunna/club-3090
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. llama.cpp · recommended 2×
  3. TGI · recommended 1×
  4. ExLlamaV2 · recommended 1×
  5. DeepSpeed-MII · recommended 1×
  • CATEGORY QUERY
    How can I efficiently serve large language models on my local RTX 3090 GPU?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. llama.cpp
    4. ExLlamaV2
    5. DeepSpeed-MII
    6. TensorRT-LLM
    7. Hugging Face Transformers
    8. bitsandbytes

    AI recommended 8 alternatives but never named noonghunna/club-3090. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are optimized configurations for running multiple LLM inference engines on consumer NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Triton Inference Server
    3. Hugging Face `transformers`
    4. llama.cpp
    5. OpenVINO
    6. accelerate
    7. Docker
    8. NGINX
    9. HAProxy

    AI recommended 9 alternatives but never named noonghunna/club-3090. 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 noonghunna/club-3090?
    pass
    AI named noonghunna/club-3090 explicitly

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

  • If a team adopts noonghunna/club-3090 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named noonghunna/club-3090 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 noonghunna/club-3090 solve, and who is the primary audience?
    pass
    AI did not name noonghunna/club-3090 — 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?

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noonghunna/club-3090 — 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