RRepoGEO

REPOGEO REPORT · LITE

city96/ComfyUI-GGUF

Default branch main · commit 6ea2651e · scanned 5/11/2026, 11:33:25 PM

GitHub: 3,613 stars · 299 forks

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 city96/ComfyUI-GGUF, 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
  • highreadme#1
    Strengthen the README's opening statement to highlight core benefit

    Why:

    CURRENT
    # ComfyUI-GGUF
GGUF Quantization support for native ComfyUI models
    COPY-PASTE FIX
    # ComfyUI-GGUF: GGUF Quantization for ComfyUI (Low VRAM Diffusion/Transformer Inference)

This custom node pack brings GGUF quantization support directly to native ComfyUI models, enabling efficient, low-VRAM inference for transformer and diffusion models like Flux on consumer GPUs.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/city96/ComfyUI-GGUF

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 city96/ComfyUI-GGUF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 2×
  2. Hugging Face Optimum · recommended 2×
  3. GGML/GGUF · recommended 1×
  4. llama.cpp · recommended 1×
  5. AWQ · recommended 1×
  • CATEGORY QUERY
    How can I run large AI models on GPUs with very limited VRAM effectively?
    you: not recommended
    AI recommended (in order):
    1. GGML/GGUF
    2. llama.cpp
    3. AWQ
    4. GPTQ
    5. bitsandbytes
    6. Hugging Face Accelerate
    7. DeepSpeed
    8. ZeRO-Offload
    9. Hugging Face Optimum
    10. FlashAttention
    11. FlashAttention-2
    12. LoRA
    13. QLoRA
    14. FlexGen

    AI recommended 14 alternatives but never named city96/ComfyUI-GGUF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help quantize transformer and diffusion models for significant memory reduction?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. bitsandbytes
    3. NVIDIA TensorRT
    4. ONNX Runtime
    5. Intel OpenVINO Toolkit
    6. PyTorch Quantization APIs
    7. TVM

    AI recommended 7 alternatives but never named city96/ComfyUI-GGUF. 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 city96/ComfyUI-GGUF?
    pass
    AI did not name city96/ComfyUI-GGUF — 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 city96/ComfyUI-GGUF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named city96/ComfyUI-GGUF 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 city96/ComfyUI-GGUF solve, and who is the primary audience?
    pass
    AI named city96/ComfyUI-GGUF explicitly

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

Embed your GEO score

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MARKDOWN (README)
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city96/ComfyUI-GGUF — 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