RRepoGEO

REPOGEO REPORT · LITE

antgroup/glake

Default branch master · commit fb24ee8c · scanned 6/14/2026, 8:48:14 PM

GitHub: 502 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 antgroup/glake, 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 README's opening to explicitly state its domain

    Why:

    CURRENT
    The current README starts with "## GLake: Optimizing GPU memory management & IO transmission" followed by "Latest News" and then an "Introduction" section.
    COPY-PASTE FIX
    Add the following sentence directly after the H1: "GLake is a high-performance library for optimizing GPU memory management and IO transmission, specifically designed for large language model (LLM) training and inference, including advanced KV cache management."
  • mediumhomepage#2
    Add a homepage URL to the About section

    Why:

    COPY-PASTE FIX
    https://github.com/antgroup/glake
  • mediumtopics#3
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    deepspeed, gpu, llm, memory, onnx, pytorch
    COPY-PASTE FIX
    deepspeed, gpu, llm, memory, onnx, pytorch, kv-cache, gpu-optimization, memory-management, large-language-models

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 antgroup/glake
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. vLLM · recommended 2×
  4. bitsandbytes · recommended 1×
  5. AWQ · recommended 1×
  • CATEGORY QUERY
    How to efficiently manage GPU memory for large language models to improve performance?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. AWQ
    3. GPTQ
    4. Hugging Face Accelerate
    5. DeepSpeed
    6. PyTorch
    7. Hugging Face Transformers
    8. xFormers
    9. Triton
    10. DeepSpeed ZeRO
    11. PyTorch FSDP
    12. vLLM
    13. TGI (Text Generation Inference)

    AI recommended 13 alternatives but never named antgroup/glake. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking solutions to optimize KV cache management for large language models in PyTorch.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. vLLM
    3. PagedAttention
    4. DeepSpeed
    5. Hugging Face Transformers
    6. BetterTransformer
    7. OpenAI Triton
    8. TensorRT-LLM

    AI recommended 8 alternatives but never named antgroup/glake. 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 antgroup/glake?
    pass
    AI did not name antgroup/glake — 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 antgroup/glake in production, what risks or prerequisites should they evaluate first?
    pass
    AI named antgroup/glake 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 antgroup/glake solve, and who is the primary audience?
    pass
    AI named antgroup/glake explicitly

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

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antgroup/glake — 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