RRepoGEO

REPOGEO REPORT · LITE

ModelCloud/GPTQModel

Default branch main · commit 9de4b034 · scanned 6/19/2026, 12:11:53 PM

GitHub: 1,181 stars · 188 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
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 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
    Add a 'Why GPTQModel?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why GPTQModel?

GPTQModel stands out as a dedicated LLM quantization toolkit offering **optimized performance** for faster quantization and inference, coupled with **superior memory efficiency** to significantly reduce VRAM usage and increase throughput. It provides comprehensive hardware acceleration support across NVIDIA CUDA, AMD ROCm, Huawei Ascend NPU, Intel XPU, and Intel/AMD/Apple CPUs, making it a versatile solution for deploying large language models efficiently.
  • mediumlicense#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License Name(s) here, e.g., 'a custom license combining Apache-2.0 and MIT terms'] - see the [LICENSE](LICENSE) file for details.
  • lowtopics#3
    Expand repository topics to include broader application areas

    Why:

    CURRENT
    gptq, optimum, peft, quantization, sglang, transformers, vllm
    COPY-PASTE FIX
    gptq, optimum, peft, quantization, sglang, transformers, vllm, llm-optimization, inference-acceleration, multi-gpu, multi-cpu

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
openvinotoolkit/openvino
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openvinotoolkit/openvino · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. huggingface/optimum · recommended 1×
  4. NVIDIA/TensorRT · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I efficiently compress large language models for deployment across different GPU and CPU types?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum with ONNX Runtime (huggingface/optimum)
    2. NVIDIA TensorRT (NVIDIA/TensorRT)
    3. OpenVINO (openvinotoolkit/openvino)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch Quantization (pytorch/pytorch)
    6. Apache TVM (apache/tvm)
    7. MLC LLM (mlc-ai/mlc-llm)

    AI recommended 7 alternatives but never named ModelCloud/GPTQModel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help optimize LLM inference speed on Nvidia, AMD, and Intel hardware using quantization?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. NVIDIA FasterTransformer (NVIDIA/FasterTransformer)
    3. bitsandbytes (TimDettmers/bitsandbytes)
    4. AutoGPTQ (PanQiWei/AutoGPTQ)
    5. ROCm (Radeon Open Compute platform) (ROCm/ROCm)
    6. ONNX Runtime (microsoft/onnxruntime)
    7. PyTorch (pytorch/pytorch)
    8. MIGraphX (ROCm/MIGraphX)
    9. OpenVINO Toolkit (openvinotoolkit/openvino)
    10. Intel Extension for PyTorch (IPEX) (intel/intel-extension-for-pytorch)
    11. Intel Neural Compressor (intel/neural-compressor)

    AI recommended 11 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 named ModelCloud/GPTQModel explicitly

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

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ModelCloud/GPTQModel — 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