RRepoGEO

REPOGEO REPORT · LITE

facebookincubator/AITemplate

Default branch main · commit c8aad377 · scanned 6/26/2026, 3:32:10 PM

GitHub: 4,723 stars · 389 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
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 facebookincubator/AITemplate, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    deep-learning, neural-networks, gpu-inference, cuda, hip, fp16, tensorcore, matrixcore, model-optimization, c-plus-plus
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://facebookincubator.github.io/AITemplate
  • mediumreadme#3
    Highlight the 'no external runtimes' differentiator in the README intro

    Why:

    CURRENT
    AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving. AITemplate highlights include:
    COPY-PASTE FIX
    AITemplate (AIT) is a Python framework that transforms deep neural networks into self-contained, high-performance CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving, crucially without relying on external runtimes like cuBLAS or TensorRT. AITemplate highlights include:

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 facebookincubator/AITemplate
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. PyTorch · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. MIGraphX · recommended 1×
  5. ONNX · recommended 1×
  • CATEGORY QUERY
    How to achieve lightning-fast FP16 neural network inference on both NVIDIA and AMD GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. ONNX Runtime
    3. TensorRT
    4. MIGraphX
    5. ONNX
    6. TensorFlow
    7. TensorFlow Lite
    8. TensorFlow Serving
    9. OpenVINO
    10. ROCm
    11. CUDA
    12. HIP

    AI recommended 12 alternatives but never named facebookincubator/AITemplate. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework compiles deep learning models to optimized C++ for GPU inference without external runtimes?
    you: not recommended
    AI recommended (in order):
    1. TensorRT
    2. Apache TVM (apache/tvm)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. OpenVINO Toolkit (openvinotoolkit/openvino)
    5. PlaidML (plaidml/plaidml)

    AI recommended 5 alternatives but never named facebookincubator/AITemplate. 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 facebookincubator/AITemplate?
    pass
    AI named facebookincubator/AITemplate explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of facebookincubator/AITemplate. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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facebookincubator/AITemplate — 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
facebookincubator/AITemplate — RepoGEO report