RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/cutlass

Default branch main · commit e8ecfad7 · scanned 7/1/2026, 1:02:45 AM

GitHub: 9,981 stars · 1,928 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
58 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 when recommended
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/cutlass, 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 highlight customization

    Why:

    CURRENT
    CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement. CUTLASS decomposes these "moving parts" into reusable, modular software components and abstractions.
    COPY-PASTE FIX
    CUTLASS is a highly flexible CUDA C++ template library providing low-level, modular building blocks for implementing high-performance matrix-matrix multiplication (GEMM) and related computations. It enables deep customization for various data types, tiling sizes, and algorithmic policies, making it ideal for specialized GPU kernels and deep learning primitives across NVIDIA architectures.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    cpp, cuda, deep-learning, deep-learning-library, gpu, nvidia, python
    COPY-PASTE FIX
    cpp, cuda, deep-learning, deep-learning-library, gpu, nvidia, python, cuda-templates, custom-gpu-kernels, linear-algebra-primitives, mixed-precision-computing
  • lowreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    The licensing terms for CUTLASS are fully detailed in the `LICENSE` file located in this repository.

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
1 / 2
50% of queries surface NVIDIA/cutlass
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
rocBLAS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. rocBLAS · recommended 2×
  2. CUDA C++ · recommended 1×
  3. cuBLAS · recommended 1×
  4. ROCm · recommended 1×
  5. HIP · recommended 1×
  • CATEGORY QUERY
    How to implement high-performance matrix multiplication on GPU with custom data types?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++
    2. cuBLAS
    3. ROCm
    4. rocBLAS
    5. HIP
    6. OpenCL
    7. TVM (Tensor Virtual Machine)
    8. Triton
    9. SYCL
    10. oneAPI DPC++
    11. oneAPI

    AI recommended 11 alternatives but never named NVIDIA/cutlass. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    C++ template library for building efficient deep learning primitives on accelerators?
    you: #6
    AI recommended (in order):
    1. cuDNN
    2. rocBLAS
    3. rocFFT
    4. MIOpen
    5. oneDNN
    6. cutlass ← you
    7. Eigen
    8. ArrayFire
    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/cutlass?
    pass
    AI named NVIDIA/cutlass 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/cutlass in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/cutlass 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/cutlass solve, and who is the primary audience?
    pass
    AI named NVIDIA/cutlass 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/cutlass — 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