RRepoGEO

REPOGEO REPORT · LITE

ModelCloud/GPTQModel

Default branch main · commit 4f39b308 · scanned 5/9/2026, 3:22:24 PM

GitHub: 1,140 stars · 185 forks

AI VISIBILITY SCORE
33 /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
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 ModelCloud/GPTQModel, 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
    Reposition the README's opening paragraph to emphasize its role as a leading toolkit

    Why:

    CURRENT
    <p align="center">LLM model quantization (compression) toolkit with hw acceleration support for NVIDIA CUDA, AMD ROCm, Huawei Ascend NPU, Intel XPU, and Intel/AMD/Apple CPUs via HF, vLLM, and SGLang.</p>
    COPY-PASTE FIX
    GPTQModel is the leading LLM quantization (compression) toolkit, providing hardware-accelerated support for NVIDIA CUDA, AMD ROCm, Huawei Ascend NPU, Intel XPU, and Intel/AMD/Apple CPUs, seamlessly integrating with Hugging Face, vLLM, and SGLang for efficient deployment.
  • mediumtopics#2
    Expand repository topics with broader LLM optimization and hardware acceleration terms

    Why:

    CURRENT
    gptq, optimum, peft, quantization, sglang, transformers, vllm
    COPY-PASTE FIX
    gptq, optimum, peft, quantization, sglang, transformers, vllm, llm-inference, model-optimization, hardware-acceleration, deep-learning-framework
  • lowlicense#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms found in the [LICENSE file](LICENSE).

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 ModelCloud/GPTQModel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Optimum
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Optimum · recommended 1×
  2. ONNX Runtime · recommended 1×
  3. NVIDIA TensorRT · recommended 1×
  4. OpenVINO Toolkit · recommended 1×
  5. llama.cpp · recommended 1×
  • CATEGORY QUERY
    How can I quantize large language models for efficient deployment across different GPU and CPU architectures?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. ONNX Runtime
    3. NVIDIA TensorRT
    4. OpenVINO Toolkit
    5. llama.cpp
    6. PyTorch Quantization
    7. TensorFlow Lite
    8. DeepSpeed

    AI recommended 8 alternatives but never named ModelCloud/GPTQModel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide hardware-accelerated LLM compression compatible with Hugging Face, vLLM, or SGLang?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. AutoGPTQ (PanQiWei/AutoGPTQ)
    7. ExLlamaV2 (turboderp/exllamav2)

    AI recommended 7 alternatives but never named ModelCloud/GPTQModel. 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 ModelCloud/GPTQModel?
    pass
    AI named ModelCloud/GPTQModel explicitly

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

  • If a team adopts ModelCloud/GPTQModel in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ModelCloud/GPTQModel 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 ModelCloud/GPTQModel solve, and who is the primary audience?
    pass
    AI did not name ModelCloud/GPTQModel — 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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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite