RRepoGEO

REPOGEO REPORT · LITE

nunchaku-ai/nunchaku

Default branch main · commit 8f418405 · scanned 5/25/2026, 1:57:19 AM

GitHub: 3,854 stars · 254 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 nunchaku-ai/nunchaku, 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
  • highabout#1
    Clarify the repository's 'About' description

    Why:

    CURRENT
    [ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
    COPY-PASTE FIX
    Nunchaku: A high-performance inference engine for 4-bit neural networks, based on SVDQuant (ICLR2025 Spotlight).
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Nunchaku differentiates itself from other quantization libraries and inference engines by [explain 1-2 key differentiators, e.g., specific optimization techniques, model support, or performance gains compared to AutoGPTQ, bitsandbytes, or TensorRT].
  • lowreadme#3
    Reorder README sections for better initial focus

    Why:

    CURRENT
    The 'News' section is currently placed immediately after the initial project description and community links.
    COPY-PASTE FIX
    Move the 'News' section to appear after the main 'What is Nunchaku' or 'Features' sections, ensuring the project's core purpose is presented first.

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 nunchaku-ai/nunchaku
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. bitsandbytes · recommended 1×
  3. Hugging Face `transformers` · recommended 1×
  4. AutoGPTQ · recommended 1×
  5. AWQ · recommended 1×
  • CATEGORY QUERY
    Looking for libraries to quantize generative AI models to 4-bit for faster deployment.
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. Hugging Face `transformers`
    3. AutoGPTQ
    4. AWQ
    5. ONNX Runtime
    6. TensorRT-LLM

    AI recommended 6 alternatives but never named nunchaku-ai/nunchaku. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which high-performance inference engines are optimized for running 4-bit neural networks?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. Qualcomm AI Engine Direct (QNN)
    3. Intel OpenVINO Toolkit
    4. Arm NN
    5. ONNX Runtime
    6. Edge TPU Runtime

    AI recommended 6 alternatives but never named nunchaku-ai/nunchaku. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 nunchaku-ai/nunchaku?
    pass
    AI named nunchaku-ai/nunchaku explicitly

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

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

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

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nunchaku-ai/nunchaku — 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