RRepoGEO

REPOGEO REPORT · LITE

BlackSamorez/tensor_parallel

Default branch main · commit a7d19394 · scanned 6/8/2026, 5:36:37 PM

GitHub: 655 stars · 44 forks

AI VISIBILITY SCORE
28 /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
2 / 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 BlackSamorez/tensor_parallel, 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
    Clarify unique value proposition in README opening

    Why:

    CURRENT
    Run large PyTorch models on multiple GPUs in one line of code with potentially linear speedup.
    COPY-PASTE FIX
    tensor_parallel enables **intra-layer tensor parallelism** for large PyTorch models, automatically splitting weights across multiple GPUs for efficient training and inference with a single line of code.
  • highhomepage#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://github.com/BlackSamorez/tensor_parallel
  • mediumtopics#3
    Add more specific topics for parallelism

    Why:

    CURRENT
    deep-learning, machine-learning, natural-language-processing, nlp, python, pytorch, pytorch-transformers
    COPY-PASTE FIX
    deep-learning, machine-learning, natural-language-processing, nlp, python, pytorch, pytorch-transformers, tensor-parallelism, model-parallelism, gpu-acceleration

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 BlackSamorez/tensor_parallel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/accelerate
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/accelerate · recommended 2×
  2. Lightning-AI/lightning · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. PyTorch's `DistributedDataParallel` (DDP) · recommended 1×
  5. PyTorch's `DataParallel` (DP) · recommended 1×
  • CATEGORY QUERY
    How to efficiently distribute large PyTorch models across multiple GPUs for inference?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Accelerate (huggingface/accelerate)

    AI recommended 1 alternative but never named BlackSamorez/tensor_parallel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an easy way to parallelize PyTorch model training across several GPUs automatically.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (Lightning-AI/lightning)
    2. Hugging Face Accelerate (huggingface/accelerate)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. PyTorch's `DistributedDataParallel` (DDP)
    5. PyTorch's `DataParallel` (DP)

    AI recommended 5 alternatives but never named BlackSamorez/tensor_parallel. 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 BlackSamorez/tensor_parallel?
    pass
    AI did not name BlackSamorez/tensor_parallel — likely talking about a different project

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

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

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

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BlackSamorez/tensor_parallel — 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