RRepoGEO

REPOGEO REPORT · LITE

google/gemma.cpp

Default branch main · commit 3ed403e2 · scanned 5/11/2026, 8:02:28 AM

GitHub: 6,893 stars · 635 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 google/gemma.cpp, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 to highlight Gemma-specific, research focus

    Why:

    CURRENT
    # gemma.cpp

gemma.cpp is a lightweight, standalone C++ inference engine for the Gemma foundation models from Google.
    COPY-PASTE FIX
    # gemma.cpp

gemma.cpp is the **official lightweight, standalone C++ inference engine for Google's Gemma foundation models**, specifically designed for experimentation and research use cases.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://ai.google.dev/gemma

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 google/gemma.cpp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ggerganov/llama.cpp
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ggerganov/llama.cpp · recommended 2×
  2. microsoft/onnxruntime · recommended 2×
  3. openvinotoolkit/openvino · recommended 2×
  4. apache/tvm · recommended 2×
  5. NVIDIA/TensorRT · recommended 1×
  • CATEGORY QUERY
    What are lightweight C++ options for local large language model inference?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. OpenVINO (openvinotoolkit/openvino)
    4. Apache TVM (apache/tvm)
    5. NVIDIA TensorRT (NVIDIA/TensorRT)

    AI recommended 5 alternatives but never named google/gemma.cpp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to embed a fast language model inference engine directly into a C++ application?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorRT
    4. OpenVINO (openvinotoolkit/openvino)
    5. LibTorch (pytorch/pytorch)
    6. Apache TVM (apache/tvm)

    AI recommended 6 alternatives but never named google/gemma.cpp. 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 google/gemma.cpp?
    pass
    AI did not name google/gemma.cpp — 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 google/gemma.cpp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google/gemma.cpp 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 google/gemma.cpp solve, and who is the primary audience?
    pass
    AI named google/gemma.cpp explicitly

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

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google/gemma.cpp — 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