RRepoGEO

REPOGEO REPORT · LITE

LitLeo/TensorRT_Tutorial

Default branch master · commit 1d370d82 · scanned 5/13/2026, 5:47:35 AM

GitHub: 1,051 stars · 196 forks

AI VISIBILITY SCORE
30 /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
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 LitLeo/TensorRT_Tutorial, 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
    Reposition the README's opening to clearly state the repo's purpose

    Why:

    CURRENT
    # 中文翻译文档
 - 2023-9-27 增加 TensorRT8.5.3的中文翻译文档，使用Chat-GPT翻译+精校，chapter1-2
 
# 建议看最新视频版本！列表如下
    COPY-PASTE FIX
    This repository provides a comprehensive tutorial and practical guide for optimizing and accelerating deep neural networks using NVIDIA TensorRT. It includes video lectures, translated documentation, and code examples to help deep learning engineers and practitioners master TensorRT for high-performance inference.

# 中文翻译文档
 - 2023-9-27 增加 TensorRT8.5.3的中文翻译文档，使用Chat-GPT翻译+精校，chapter1-2
 
# 建议看最新视频版本！列表如下
  • highabout#2
    Add a concise description to the repository

    Why:

    COPY-PASTE FIX
    A comprehensive tutorial and practical guide for optimizing and accelerating deep neural networks using NVIDIA TensorRT, including video lectures, translated documentation, and code examples.

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 LitLeo/TensorRT_Tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 2×
  2. OpenVINO Toolkit · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. PyTorch JIT (TorchScript) · recommended 1×
  5. TensorFlow Lite · recommended 1×
  • CATEGORY QUERY
    How to achieve high-performance deep learning inference on graphics processing units?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. PyTorch JIT (TorchScript)
    5. TensorFlow Lite
    6. Apache TVM

    AI recommended 6 alternatives but never named LitLeo/TensorRT_Tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical guides for deploying and accelerating neural networks on GPU hardware.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Deep Learning Performance Documentation
    2. NVIDIA TensorRT

    AI recommended 2 alternatives but never named LitLeo/TensorRT_Tutorial. 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 LitLeo/TensorRT_Tutorial?
    pass
    AI named LitLeo/TensorRT_Tutorial explicitly

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

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

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

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LitLeo/TensorRT_Tutorial — 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