RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/TileKernels

Default branch main · commit 36d9e45d · scanned 5/9/2026, 7:32:50 AM

GitHub: 1,487 stars · 120 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 deepseek-ai/TileKernels, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-optimization, gpu-kernels, deep-learning, pytorch, tilelang, high-performance-computing, moe, quantization
  • highreadme#2
    Strengthen README's opening to emphasize LLM optimization and GPU performance

    Why:

    CURRENT
    # Tile Kernels

Optimized GPU kernels for LLM operations, built with TileLang. TileLang is a domain-specific language for expressing high-performance GPU kernels in Python, featuring easy migration, agile development, and automatic optimization.
    COPY-PASTE FIX
    # Tile Kernels: Highly Optimized GPU Kernels for LLM Operations

Tile Kernels provides a library of highly optimized GPU kernels specifically designed to accelerate large language model (LLM) operations, built using the TileLang domain-specific language. It addresses the problem of inefficient computation in deep learning models by delivering near-peak hardware performance for critical LLM components, enabling AI researchers and engineers to achieve significant performance improvements.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Insert official project homepage URL here, e.g., a dedicated documentation site or project page]

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 deepseek-ai/TileKernels
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT-LLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT-LLM · recommended 1×
  2. DeepSpeed · recommended 1×
  3. vLLM · recommended 1×
  4. FlashAttention · recommended 1×
  5. PyTorch 2.0 · recommended 1×
  • CATEGORY QUERY
    How to optimize large language model operations for peak GPU performance?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM
    2. DeepSpeed
    3. vLLM
    4. FlashAttention
    5. PyTorch 2.0
    6. ONNX Runtime
    7. bitsandbytes
    8. AutoGPTQ

    AI recommended 8 alternatives but never named deepseek-ai/TileKernels. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python-based DSL to build highly optimized GPU kernels efficiently.
    you: not recommended
    AI recommended (in order):
    1. Numba
    2. PyTorch
    3. TensorFlow
    4. JAX
    5. Taichi

    AI recommended 5 alternatives but never named deepseek-ai/TileKernels. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 deepseek-ai/TileKernels?
    pass
    AI named deepseek-ai/TileKernels explicitly

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

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

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

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deepseek-ai/TileKernels — 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