RRepoGEO

REPOGEO REPORT · LITE

ScalingIntelligence/KernelBench

Default branch main · commit 423217d9 · scanned 6/26/2026, 4:28:12 PM

GitHub: 1,085 stars · 174 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 ScalingIntelligence/KernelBench, 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
    Add a clear positioning statement to the README's introduction

    Why:

    CURRENT
    A benchmark and environment for evaluating LLMs' ability to generate efficient GPU kernels
    COPY-PASTE FIX
    KernelBench is a benchmark and environment specifically designed for evaluating Large Language Models' ability to generate efficient GPU kernels. Unlike general GPU profilers (e.g., NVIDIA Nsight) or low-level libraries (e.g., cuBLAS), KernelBench focuses on the systematic assessment of LLM-generated code quality and performance.
  • mediumtopics#2
    Expand topics to include LLM-specific and AI code generation terms

    Why:

    CURRENT
    benchmark, codegen, evaluation, gpu, rl-environment, tooling
    COPY-PASTE FIX
    benchmark, codegen, evaluation, gpu, rl-environment, tooling, large-language-models, llm-evaluation, ai-code-generation, pytorch-cuda, kernel-optimization
  • lowreadme#3
    Add a section to README clarifying the project's license

    Why:

    COPY-PASTE FIX
    ## License
This project is released under [describe the specific terms of your license, e.g., 'a custom license based on X and Y', or 'the terms outlined in the LICENSE file']. Please refer to the `LICENSE` file for full details.

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 ScalingIntelligence/KernelBench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
cuBLAS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. cuBLAS · recommended 2×
  2. NVIDIA Nsight Compute · recommended 2×
  3. NVIDIA Nsight Systems · recommended 2×
  4. numpy/numpy · recommended 1×
  5. google/googletest · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models for generating efficient GPU kernel code?
    you: not recommended
    AI recommended (in order):
    1. NumPy (numpy/numpy)
    2. cuBLAS
    3. GoogleTest (google/googletest)
    4. Catch2 (catchorg/Catch2)
    5. Pytest (pytest-dev/pytest)
    6. NVIDIA Nsight Compute
    7. Google Benchmark (google/benchmark)
    8. nvbench (NVIDIA/nvbench)
    9. NVIDIA Nsight Systems
    10. NVIDIA cuDNN
    11. NVIDIA cuFFT
    12. Python (python/cpython)
    13. Bash
    14. Matplotlib (matplotlib/matplotlib)
    15. Seaborn (mwaskom/seaborn)
    16. Pandas (pandas-dev/pandas)
    17. Jupyter Notebooks (jupyter/notebook)
    18. nvcc

    AI recommended 18 alternatives but never named ScalingIntelligence/KernelBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for evaluating AI models that convert PyTorch operations into optimized CUDA kernels?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. NVIDIA Nsight Compute
    3. PyTorch Profiler (pytorch/pytorch)
    4. TensorRT
    5. cuBLAS
    6. cuDNN

    AI recommended 6 alternatives but never named ScalingIntelligence/KernelBench. 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 ScalingIntelligence/KernelBench?
    pass
    AI named ScalingIntelligence/KernelBench explicitly

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

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

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

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ScalingIntelligence/KernelBench — 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