RRepoGEO

REPOGEO REPORT · LITE

Tlntin/Qwen-TensorRT-LLM

Default branch main · commit 7da636fe · scanned 6/13/2026, 1:07:13 PM

GitHub: 619 stars · 58 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 Tlntin/Qwen-TensorRT-LLM, 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
    Add a concise, accurate repository description

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    An archived example repository demonstrating Qwen LLM inference acceleration and deployment using NVIDIA TensorRT-LLM, including various quantization methods and API integrations. Note: This repository is no longer actively maintained as official TensorRT-LLM now supports Qwen.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    qwen, llm, tensorrt-llm, nvidia, inference, acceleration, quantization, deployment, triton, fastapi
  • mediumreadme#3
    Reposition the README's opening to clarify purpose and maintenance status

    Why:

    CURRENT
    # 总述
    ### 背景介绍
    - 介绍本工作是 <a href="https://github.com/NVIDIA/trt-samples-for-hackathon-cn/tree/master/Hackathon2023">NVIDIA TensorRT Hackathon 2023</a> 的参赛题目,本项目使用TRT-LLM完成对Qwen-7B-Chat实现推理加速。相关代码已经放在release/0.1.0分支,感兴趣的同学可以去该分支学习完整流程。
    
    #### 自2024年4月24日起,TensorRT-LLM官方仓库最新main分支已经支持qwen/qwen2,故本仓库不再做重大更新。
    COPY-PASTE FIX
    # Qwen LLM Inference Acceleration with NVIDIA TensorRT-LLM (Archived)
    
    This repository provides an example implementation for accelerating Qwen Large Language Model inference using NVIDIA TensorRT-LLM, featuring various quantization techniques and deployment options (Gradio, FastAPI, Triton).
    
    **Please note:** As of April 24, 2024, this repository is no longer actively maintained as the official TensorRT-LLM repository now natively supports Qwen/Qwen2 models. This project originated from the NVIDIA TensorRT Hackathon 2023.

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 Tlntin/Qwen-TensorRT-LLM
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT-LLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT-LLM · recommended 1×
  2. Hugging Face Optimum with NVIDIA TensorRT · recommended 1×
  3. bitsandbytes · recommended 1×
  4. AutoGPTQ · recommended 1×
  5. NVIDIA cuBLASLt · recommended 1×
  • CATEGORY QUERY
    How to accelerate large language model inference with various quantization methods on NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM
    2. Hugging Face Optimum with NVIDIA TensorRT
    3. bitsandbytes
    4. AutoGPTQ
    5. NVIDIA cuBLASLt
    6. ONNX Runtime with NVIDIA Execution Provider

    AI recommended 6 alternatives but never named Tlntin/Qwen-TensorRT-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a solution to deploy open-source LLMs with OpenAI-compatible API and web demo.
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference)
    3. OpenWebUI
    4. LocalAI
    5. Ollama
    6. FastChat

    AI recommended 6 alternatives but never named Tlntin/Qwen-TensorRT-LLM. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Tlntin/Qwen-TensorRT-LLM?
    pass
    AI did not name Tlntin/Qwen-TensorRT-LLM — 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 Tlntin/Qwen-TensorRT-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tlntin/Qwen-TensorRT-LLM 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 Tlntin/Qwen-TensorRT-LLM solve, and who is the primary audience?
    pass
    AI did not name Tlntin/Qwen-TensorRT-LLM — 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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Tlntin/Qwen-TensorRT-LLM — 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