RRepoGEO

REPOGEO REPORT · LITE

ScalingIntelligence/KernelBench

Default branch main · commit 423217d9 · scanned 5/27/2026, 4:23:31 PM

GitHub: 1,029 stars · 171 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 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
    Reposition README H1 to emphasize "LLM Benchmark"

    Why:

    CURRENT
    # KernelBench: Can LLMs Write Efficient GPU Kernels? [ICML '25]
    COPY-PASTE FIX
    # KernelBench: An LLM Benchmark for Efficient GPU Kernel Generation [ICML '25]
  • hightopics#2
    Add missing core keywords to topics list

    Why:

    CURRENT
    benchmark, codegen, evaluation, gpu, rl-environment, tooling
    COPY-PASTE FIX
    benchmark, codegen, evaluation, gpu, llm, large-language-models, pytorch, cuda, tooling
  • mediumlicense#3
    Clarify existing license in README

    Why:

    COPY-PASTE FIX
    Add a "## License" section to your README with the following text: "This project is licensed under [INSERT ACTUAL LICENSE NAME(S) FROM YOUR LICENSE FILE HERE]. Please refer to the [LICENSE](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
OpenAI Evals
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Evals · recommended 1×
  2. nvcc · recommended 1×
  3. hipcc · recommended 1×
  4. nvprof · recommended 1×
  5. NVIDIA Nsight Systems · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models for generating efficient GPU kernel code?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals
    2. nvcc
    3. hipcc
    4. nvprof
    5. NVIDIA Nsight Systems
    6. NVIDIA Nsight Compute
    7. rocprof
    8. Google Benchmark
    9. time
    10. diff
    11. MLPerf Inference

    AI recommended 11 alternatives but never named ScalingIntelligence/KernelBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for LLM-driven transpilation of PyTorch operations into optimized CUDA kernels?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Anthropic Claude 3 Opus
    3. Triton
    4. Apache TVM
    5. TorchInductor
    6. CUDA C++
    7. cuBLAS
    8. cuDNN

    AI recommended 8 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