RRepoGEO

REPOGEO REPORT · LITE

pytorch/ao

Default branch main · commit 13cd013d · scanned 5/19/2026, 1:32:43 AM

GitHub: 2,824 stars · 506 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 pytorch/ao, 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
  • highreadme#1
    Strengthen README's top-level positioning to emphasize PyTorch-native optimization

    Why:

    CURRENT
    # TorchAO

### PyTorch-Native Training-to-Serving Model Optimization
    COPY-PASTE FIX
    # TorchAO: PyTorch-Native Training-to-Serving Model Optimization
  • mediumtopics#2
    Add more specific topics to highlight PyTorch-native model optimization

    Why:

    CURRENT
    brrr, cuda, dtypes, float8, inference, llama, mx, pytorch, quantization, sparsity, training, transformer
    COPY-PASTE FIX
    brrr, cuda, dtypes, float8, inference, llama, mx, pytorch, quantization, sparsity, training, transformer, pytorch-optimization, model-optimization, deep-learning-optimization, model-acceleration
  • lowreadme#3
    Add a clear statement about the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, after the 'Citation' link in the navigation block: `## License

This project is licensed under the terms found in the [LICENSE file](./LICENSE).`

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 pytorch/ao
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. TensorFlow Model Optimization Toolkit · recommended 2×
  4. NVIDIA Apex · recommended 2×
  5. Hugging Face Transformers · recommended 2×
  • CATEGORY QUERY
    How can I reduce memory footprint and accelerate inference for large deep learning models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization
    2. TensorFlow Lite
    3. ONNX Runtime
    4. PyTorch Pruning
    5. TensorFlow Model Optimization Toolkit
    6. NVIDIA Apex
    7. Hugging Face Transformers
    8. Keras
    9. PyTorch
    10. MobileNetV3
    11. EfficientNet
    12. DistilBERT
    13. NVIDIA TensorRT
    14. OpenVINO Toolkit

    AI recommended 14 alternatives but never named pytorch/ao. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are strategies for accelerating neural network training and deployment with minimal accuracy loss?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. TensorFlow Model Optimization Toolkit
    5. torch.nn.utils.prune
    6. NVIDIA's Automatic Mixed Precision (AMP)
    7. Hugging Face Transformers
    8. PaddlePaddle's PaddleSlim
    9. Keras
    10. NVIDIA Apex
    11. tf.keras.mixed_precision
    12. torch.cuda.amp
    13. MobileNet
    14. EfficientNet
    15. SqueezeNet
    16. NVIDIA TensorRT
    17. OpenVINO Toolkit
    18. Edge TPUs (Google Coral)
    19. TVM

    AI recommended 19 alternatives but never named pytorch/ao. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 pytorch/ao?
    pass
    AI named pytorch/ao explicitly

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

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

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

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pytorch/ao — 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