RRepoGEO

REPOGEO REPORT · LITE

volcengine/veScale

Default branch main · commit 20cf5c71 · scanned 6/27/2026, 6:43:44 PM

GitHub: 1,027 stars · 62 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
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 volcengine/veScale, 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
    Reposition the README's opening to clearly state the project's current purpose and offerings

    Why:

    CURRENT
    # Old veScale Has been Moved to `legacy/`; New veScale is Coming ...

# veScale

veScale is an internal PyTorch Distributed library, enabling hyperscale distributed training of LLMs and RLs. This repo open-sources a small piece of veScale for a better community.
    COPY-PASTE FIX
    # veScale: Hyperscale PyTorch Distributed Training for LLMs and RLs

veScale is a PyTorch Distributed library designed for hyperscale training of Large Language Models (LLMs) and Reinforcement Learning (RL) models. This repository open-sources key components, such as RaggedShard DTensor, providing advanced capabilities for efficient and scalable AI model development.
  • mediumtopics#2
    Expand repository topics to include more specific distributed training keywords

    Why:

    CURRENT
    llm-training, pytorch
    COPY-PASTE FIX
    llm-training, pytorch, distributed-training, deep-learning, large-language-models, reinforcement-learning, hyperscale-ai, distributed-pytorch
  • lowhomepage#3
    Add the repository URL as the homepage in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/volcengine/veScale

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 volcengine/veScale
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch FSDP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch FSDP · recommended 1×
  2. microsoft/DeepSpeed · recommended 1×
  3. NVIDIA/Megatron-LM · recommended 1×
  4. hpcaitech/ColossalAI · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    What solutions exist for hyperscale distributed training of LLMs using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. Colossal-AI (hpcaitech/ColossalAI)
    5. Accelerate (huggingface/accelerate)

    AI recommended 5 alternatives but never named volcengine/veScale. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch distributed library for efficient, large-scale training of AI models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch DistributedDataParallel (DDP)
    2. PyTorch Lightning
    3. Hugging Face Accelerate
    4. DeepSpeed
    5. Horovod
    6. FairScale

    AI recommended 6 alternatives but never named volcengine/veScale. 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 volcengine/veScale?
    pass
    AI named volcengine/veScale explicitly

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

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

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

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volcengine/veScale — 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