RRepoGEO

REPOGEO REPORT · LITE

QwenLM/FlashQLA

Default branch main · commit 6ef4858b · scanned 6/6/2026, 3:57:43 AM

GitHub: 531 stars · 42 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 QwenLM/FlashQLA, 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
    linear-attention, llm-inference, gpu-kernels, triton-alternative, deep-learning-optimization, pretraining, edge-inference, nvidia-hopper, tilelang, flashattention-alternative
  • highreadme#2
    Strengthen the README's opening sentence for immediate positioning

    Why:

    CURRENT
    FlashQLA is a high-performance linear attention kernel library built on TileLang.
    COPY-PASTE FIX
    FlashQLA is a high-performance linear attention kernel library built on TileLang, offering a 2-3x speedup over FLA Triton kernels for large language model pretraining and edge inference on NVIDIA Hopper GPUs.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://qwen.ai/blog?id=flashqla

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 QwenLM/FlashQLA
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Dao-AILab/flash-attention · recommended 1×
  2. cuBLASLt · recommended 1×
  3. NVIDIA/cutlass · recommended 1×
  4. pytorch/xla · recommended 1×
  5. openai/triton · recommended 1×
  • CATEGORY QUERY
    How to accelerate linear attention kernels for large language model pretraining on NVIDIA GPUs?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2 (Dao-AILab/flash-attention)
    2. cuBLASLt
    3. CUTLASS (NVIDIA/cutlass)
    4. PyTorch/XLA (pytorch/xla)
    5. Triton (openai/triton)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. Optimum (huggingface/optimum)

    AI recommended 7 alternatives but never named QwenLM/FlashQLA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a faster alternative to Triton kernels for linear attention in edge inference.
    you: not recommended
    AI recommended (in order):
    1. Apache TVM (apache/tvm)
    2. NVIDIA TensorRT
    3. ONNX Runtime (microsoft/onnxruntime)
    4. Intel Distribution of OpenVINO Toolkit (openvinotoolkit/openvino)
    5. Halide (halide/Halide)

    AI recommended 5 alternatives but never named QwenLM/FlashQLA. 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 QwenLM/FlashQLA?
    pass
    AI named QwenLM/FlashQLA explicitly

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

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

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

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QwenLM/FlashQLA — 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