RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/TileKernels

Default branch main · commit 36d9e45d · scanned 6/19/2026, 2:32:36 AM

GitHub: 1,595 stars · 140 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
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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to clarify software library role and performance focus

    Why:

    CURRENT
    # Tile Kernels

Optimized GPU kernels for LLM operations, built with TileLang.
    COPY-PASTE FIX
    # Tile Kernels

Tile Kernels is a high-performance software library providing highly optimized GPU kernels for large language model (LLM) operations. Built with TileLang, it targets deep learning researchers and engineers seeking maximum GPU performance and efficient execution of LLM workloads.
  • mediumhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add a link to the TileLang project page, a dedicated documentation site, or the DeepSeek AI main page if applicable (e.g., 'https://tilelang.ai' or 'https://deepseek.com/').

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
Dao-AILab/flash-attention
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Dao-AILab/flash-attention · recommended 2×
  2. NVIDIA H100 Tensor Core GPUs · recommended 1×
  3. NVIDIA A100 Tensor Core GPUs · recommended 1×
  4. NVIDIA L40S GPUs · recommended 1×
  5. NVIDIA RTX 4090 · recommended 1×
  • CATEGORY QUERY
    How to achieve maximum GPU performance for large language model operations?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA H100 Tensor Core GPUs
    2. NVIDIA A100 Tensor Core GPUs
    3. NVIDIA L40S GPUs
    4. NVIDIA RTX 4090
    5. PyTorch with `torch.compile` (Dynamo) (pytorch/pytorch)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. Megatron-LM (NVIDIA/Megatron-LM)
    8. FlashAttention (Dao-AILab/flash-attention)
    9. FlashAttention-2 (Dao-AILab/flash-attention)

    AI recommended 9 alternatives but never named deepseek-ai/TileKernels. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking highly optimized GPU kernels for Mixture of Experts and quantization in LLMs.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA FasterTransformer
    2. DeepSpeed
    3. vLLM
    4. FlashAttention
    5. bitsandbytes
    6. Intel Extension for PyTorch

    AI recommended 6 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