RRepoGEO

REPOGEO REPORT · LITE

October2001/Awesome-KV-Cache-Compression

Default branch main · commit 1cdb974e · scanned 6/4/2026, 8:13:22 AM

GitHub: 713 stars · 25 forks

AI VISIBILITY SCORE
22 /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
1 / 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 October2001/Awesome-KV-Cache-Compression, 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 clarify it's an awesome list of papers

    Why:

    COPY-PASTE FIX
    This is a curated awesome list of must-read research papers and surveys on KV Cache Compression for Large Language Models.
  • hightopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    awesome-list, large-language-models, papers
    COPY-PASTE FIX
    awesome-list, large-language-models, llm-inference, kv-cache, memory-optimization, research-papers, surveys, deep-learning-papers
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/October2001/Awesome-KV-Cache-Compression

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 October2001/Awesome-KV-Cache-Compression
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPTQ
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPTQ · recommended 1×
  2. AWQ · recommended 1×
  3. bitsandbytes · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    What techniques can I use to reduce memory footprint for large language models?
    you: not recommended
    AI recommended (in order):
    1. GPTQ
    2. AWQ
    3. bitsandbytes
    4. ONNX Runtime
    5. Hugging Face Transformers
    6. DistilBERT
    7. PyTorch
    8. NVIDIA Apex
    9. FlashAttention
    10. FlashAttention-2
    11. xFormers
    12. Hugging Face Accelerate
    13. DeepSpeed
    14. LoRA
    15. QLoRA

    AI recommended 15 alternatives but never named October2001/Awesome-KV-Cache-Compression. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking research papers on optimizing key-value cache for efficient LLM inference.
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. LightLLM (ModelTC/lightllm)
    3. FlashInfer (flashinfer-ai/flashinfer)
    4. StreamingLLM (mit-han-lab/streaming-llm)
    5. FasterTransformer (NVIDIA/FasterTransformer)
    6. Triton Inference Server (triton-inference-server/server)
    7. DeepSpeed-MII (microsoft/DeepSpeed-MII)
    8. DeepSpeed Inference (microsoft/DeepSpeed)
    9. TensorRT-LLM (NVIDIA/TensorRT-LLM)

    AI recommended 9 alternatives but never named October2001/Awesome-KV-Cache-Compression. 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 October2001/Awesome-KV-Cache-Compression?
    pass
    AI did not name October2001/Awesome-KV-Cache-Compression — 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 October2001/Awesome-KV-Cache-Compression in production, what risks or prerequisites should they evaluate first?
    pass
    AI named October2001/Awesome-KV-Cache-Compression 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 October2001/Awesome-KV-Cache-Compression solve, and who is the primary audience?
    pass
    AI did not name October2001/Awesome-KV-Cache-Compression — 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?

Embed your GEO score

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October2001/Awesome-KV-Cache-Compression — 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