RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/cudnn-frontend

Default branch develop · commit 2965e7ae · scanned 6/7/2026, 1:12:06 PM

GitHub: 840 stars · 178 forks

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 NVIDIA/cudnn-frontend, 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 sentence to emphasize advanced kernels

    Why:

    CURRENT
    **cuDNN Frontend** is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels — scaled dot-product attention (**SDPA / Flash Attention**), grouped GEMM fusions for **Mixture-of-Experts (MoE)** training, fused normalization + activation, and more.
    COPY-PASTE FIX
    **cuDNN Frontend** is NVIDIA's open-source library providing high-performance GPU kernels for advanced deep learning operations like scaled dot-product attention (**SDPA / Flash Attention**), grouped GEMM fusions for **Mixture-of-Experts (MoE)** training, and fused normalization + activation. It serves as a modern C++ and Python interface to the cuDNN library.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with other libraries' or 'Why cuDNN Frontend?' that briefly explains how `cudnn-frontend` complements or differs from `FlashAttention-2`, `xFormers`, `NVIDIA Transformer Engine`, and the raw `cuDNN` API, focusing on its open-source kernel contribution and graph API approach.
  • lowreadme#3
    Expand on the benefits of 'OSS kernels' in the README

    Why:

    CURRENT
    We will begin open-sourcing kernels based on customer needs, with the goal to educate developers and enable them to customize as needed. We are now shipping **OSS kernels**, allowing you to inspect, modify, and contribute to the core logic. Check out our latest implementations: GEMM + Amax: Optimized FP8 matrix multiplication with absolute maximum calculation. GEMM + SwiGLU: High-performance implementation of the SwiGLU activation fused with GEMM. GEMM + sReLU: High-performance implementation of squared-ReLU fused with GEMM. GEMM + dsReLU: High-performance imple
    COPY-PASTE FIX
    We are now shipping **OSS kernels**, allowing you to inspect, modify, and contribute to the core logic. This unique open-source approach empowers developers to customize and optimize advanced GPU operations directly, going beyond fixed library implementations. Check out our latest implementations: GEMM + Amax: Optimized FP8 matrix multiplication with absolute maximum calculation. GEMM + SwiGLU: High-performance implementation of the SwiGLU activation fused with GEMM. GEMM + sReLU: High-performance implementation of squared-ReLU fused with GEMM. GEMM + dsReLU: High-performance imple

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/cudnn-frontend
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. NVIDIA Transformer Engine · recommended 1×
  4. NVIDIA cuBLASLt · recommended 1×
  5. NVIDIA cuDNN · recommended 1×
  • CATEGORY QUERY
    How to optimize deep learning models using advanced GPU kernels for NVIDIA Hopper architecture?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Transformer Engine
    2. NVIDIA cuBLASLt
    3. NVIDIA cuDNN
    4. NVIDIA CUTLASS
    5. PyTorch
    6. NVIDIA Triton
    7. TensorFlow

    AI recommended 7 alternatives but never named NVIDIA/cudnn-frontend. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient C++ or Python libraries for scaled dot-product attention and MoE on GPUs.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. xFormers
    3. DeepSpeed
    4. Megatron-LM
    5. PyTorch
    6. TensorFlow
    7. OpenAI Triton

    AI recommended 7 alternatives but never named NVIDIA/cudnn-frontend. 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 NVIDIA/cudnn-frontend?
    pass
    AI named NVIDIA/cudnn-frontend 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/cudnn-frontend in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/cudnn-frontend 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/cudnn-frontend solve, and who is the primary audience?
    pass
    AI named NVIDIA/cudnn-frontend explicitly

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

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NVIDIA/cudnn-frontend — 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