RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/cutlass

Default branch main · commit 982cb9e7 · scanned 5/19/2026, 3:42:17 PM

GitHub: 9,740 stars · 1,861 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
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 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 sentence to highlight its library nature and data type support

    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.
    COPY-PASTE FIX
    CUTLASS is a high-performance CUDA C++ template library and Python DSL for implementing custom matrix-matrix multiplication (GEMM) and related linear algebra computations on NVIDIA GPUs. It offers modular abstractions with extensive support for mixed-precision and a wide range of data types.
  • mediumtopics#2
    Add more specific topics to emphasize its role as a template library for kernel development

    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, gpu-programming, linear-algebra-library, kernel-development
  • lowlicense#3
    Add a line to the README clarifying the project's license

    Why:

    COPY-PASTE FIX
    Please refer to the `LICENSE` file for details on the specific terms and conditions governing the use of CUTLASS.

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
#3.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
cuBLAS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. cuBLAS · recommended 2×
  2. cuTENSOR · recommended 1×
  3. __nv_mma_sync Intrinsics · recommended 1×
  4. Apache TVM · recommended 1×
  5. rocBLAS · recommended 1×
  • CATEGORY QUERY
    How to implement efficient mixed-precision GEMM kernels in CUDA C++?
    you: #3
    AI recommended (in order):
    1. cuBLAS
    2. cuTENSOR
    3. CUTLASS ← you
    4. __nv_mma_sync Intrinsics
    5. Apache TVM
    Show full AI answer
  • CATEGORY QUERY
    What are good libraries for high-performance GPU linear algebra, supporting various data types?
    you: not recommended
    AI recommended (in order):
    1. cuBLAS
    2. rocBLAS
    3. Intel oneMKL
    4. PyTorch
    5. TensorFlow
    6. Eigen
    7. ArrayFire

    AI recommended 7 alternatives but never named NVIDIA/cutlass. 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/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