RRepoGEO

REPOGEO REPORT · LITE

facebookincubator/AITemplate

Default branch main · commit c8aad377 · scanned 5/15/2026, 8:31:54 PM

GitHub: 4,718 stars · 388 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 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    deep-learning, neural-networks, gpu-acceleration, cuda, hip, inference, code-generation, python-framework, fp16, tensor-cores, matrix-cores, model-optimization, high-performance-computing, ai-compiler
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://facebookincubator.github.io/AITemplate
  • mediumreadme#3
    Emphasize dependency-free deployment in README highlights

    Why:

    CURRENT
    - Unified, open, and flexible. Seamless fp16 deep neural network models for NVIDIA GPU or AMD GPU. Fully open source, Lego-style easily extendable high-performance primitives for new model support. Supports a significantly more comprehensive range of fusions than existing solutions for both GPU platforms.
    COPY-PASTE FIX
    - Unified, open, and flexible. Seamless fp16 deep neural network models for NVIDIA GPU or AMD GPU. Fully open source, Lego-style easily extendable high-performance primitives for new model support. Supports a significantly more comprehensive range of fusions than existing solutions for both GPU platforms. Crucially, AITemplate generates self-contained binaries, eliminating dependencies on third-party runtimes like TensorRT or TVM for deployment.

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
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. microsoft/onnxruntime · recommended 1×
  3. apache/tvm · recommended 1×
  4. NVIDIA cuDNN · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How to generate optimized C++ code for high-performance FP16 neural network inference on GPUs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Apache TVM (apache/tvm)
    4. NVIDIA cuDNN
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow Lite (tensorflow/tensorflow)

    AI recommended 6 alternatives but never named facebookincubator/AITemplate. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python framework can accelerate deep learning model inference on both NVIDIA and AMD GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. ROCm
    3. CUDA
    4. TensorFlow
    5. ONNX Runtime
    6. MIGraphX
    7. OpenVINO
    8. TensorRT
    9. MXNet

    AI recommended 9 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