RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/kvpress

Default branch main · commit d689d7fd · scanned 6/24/2026, 5:42:19 AM

GitHub: 1,117 stars · 155 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 NVIDIA/kvpress, 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 opening to emphasize practical application

    Why:

    CURRENT
    kvpress implements multiple KV cache compression methods and benchmarks using 🤗 transformers, aiming to simplify the development of new methods for researchers and developers in this field.
    COPY-PASTE FIX
    kvpress provides ready-to-use KV cache compression methods and benchmarks for 🤗 transformers, directly addressing the high memory costs of deploying long-context LLMs. It simplifies the application of these techniques for developers and researchers aiming to optimize LLM inference.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/blog/nvidia/kvpress
  • lowtopics#3
    Add more specific optimization topics

    Why:

    CURRENT
    inference, kv-cache, kv-cache-compression, large-language-models, llm, long-context, python, pytorch, transformers
    COPY-PASTE FIX
    inference, kv-cache, kv-cache-compression, large-language-models, llm, long-context, python, pytorch, transformers, memory-optimization, llm-inference-optimization

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 NVIDIA/kvpress
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 1×
  2. H2O · recommended 1×
  3. StreamingLLM · recommended 1×
  4. AWQ · recommended 1×
  5. GPTQ · recommended 1×
  • CATEGORY QUERY
    Strategies to reduce memory consumption for long-context LLMs via KV cache compression?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. H2O
    3. StreamingLLM
    4. AWQ
    5. GPTQ
    6. Longformer
    7. BigBird

    AI recommended 7 alternatives but never named NVIDIA/kvpress. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python tools for optimizing transformer key-value cache memory during LLM inference?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. DeepSpeed-MII
    3. Hugging Face Optimum
    4. FlashAttention-2
    5. TensorRT-LLM
    6. OpenVINO

    AI recommended 6 alternatives but never named NVIDIA/kvpress. 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 NVIDIA/kvpress?
    pass
    AI named NVIDIA/kvpress explicitly

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

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

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

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NVIDIA/kvpress — 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