RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/TileGym

Default branch main · commit 0b635693 · scanned 6/14/2026, 3:06:36 PM

GitHub: 752 stars · 74 forks

AI VISIBILITY SCORE
35 /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
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 NVIDIA/TileGym, 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 clarify project purpose

    Why:

    CURRENT
    TileGym is a CUDA Tile kernel library that provides a rich collection of kernel tutorials and examples for tile-based GPU programming.
    COPY-PASTE FIX
    TileGym is a comprehensive CUDA Tile kernel library offering tutorials and practical examples for learning and optimizing tile-based GPU programming, with a focus on applications for large language models (LLMs) like Llama 3.1 and DeepSeek V2.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    cuda, gpu-programming, tile-programming, kernel-optimization, deep-learning, llm, large-language-models, nvidia, blackwell, ampere, tutorials, examples, gpu-kernels
  • mediumreadme#3
    Clarify the project's license within the README's 'License' section

    Why:

    COPY-PASTE FIX
    Add the following sentence to the 'License and Third-Party Notices' section: 'This project is licensed under the terms detailed in the accompanying LICENSE file. Please refer to the LICENSE file for full details, including any specific conditions or third-party notices.'

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 NVIDIA/TileGym
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CUDA C++
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CUDA C++ · recommended 1×
  2. cuBLAS · recommended 1×
  3. cuDNN · recommended 1×
  4. NVIDIA Nsight Compute · recommended 1×
  5. NVIDIA Nsight Systems · recommended 1×
  • CATEGORY QUERY
    How can I learn and optimize tile-based GPU kernel programming for deep learning models?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++
    2. cuBLAS
    3. cuDNN
    4. NVIDIA Nsight Compute
    5. NVIDIA Nsight Systems
    6. OpenAI Triton
    7. TVM
    8. ROCm
    9. HIP
    10. rocBLAS
    11. rocDNN
    12. ROCm-Profiler
    13. SYCL
    14. oneAPI DPC++
    15. ComputeCpp

    AI recommended 15 alternatives but never named NVIDIA/TileGym. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for CUDA kernel examples to optimize large language model performance with tiling.
    you: not recommended
    AI recommended (in order):
    1. CUTLASS (NVIDIA/cutlass)
    2. TransformerEngine (NVIDIA/TransformerEngine)
    3. Triton (OpenAI/triton)
    4. CUDA Samples (NVIDIA/cuda-samples)
    5. XLA (TensorFlow/XLA)
    6. AITemplate (PyTorch/AITemplate)
    7. DeepLearningExamples (DeepLearningExamples/PyTorch/LanguageModeling/Transformer)

    AI recommended 7 alternatives but never named NVIDIA/TileGym. 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 NVIDIA/TileGym?
    pass
    AI named NVIDIA/TileGym explicitly

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

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