RRepoGEO

REPOGEO REPORT · LITE

NotPunchnox/rkllama

Default branch main · commit 446c161c · scanned 6/13/2026, 2:26:59 AM

GitHub: 553 stars · 94 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 NotPunchnox/rkllama, 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 H1 to emphasize Rockchip NPU advantage

    Why:

    CURRENT
    # RKLLama: LLM Server and Client for Rockchip 3588/3576
    COPY-PASTE FIX
    # RKLLama: The LLM Server and Client for Rockchip NPU (RK3588/RK3576) - Run LLMs on your NPU, not just CPU/GPU!
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/NotPunchnox/rkllama/
  • mediumcomparison#3
    Add a dedicated comparison section to the README

    Why:

    CURRENT
    The difference from other software of this type like Ollama or Llama.cpp is that RKLLama allows models to run on the NPU.
    COPY-PASTE FIX
    ## Comparison to Ollama and Llama.cpp

While tools like Ollama and Llama.cpp provide excellent LLM inference, RKLLama offers a unique advantage for Rockchip NPU devices:

*   **NPU Acceleration:** RKLLama is specifically optimized to leverage the Neural Processing Unit (NPU) on Rockchip RK3588 and RK3576 platforms, enabling significantly faster and more efficient LLM inference compared to CPU-only or generic GPU solutions.
*   **Ollama-like Experience:** Provides a server and client interface similar to Ollama, making it easy to deploy and interact with LLMs on your Rockchip device.
*   **Python-based:** Built primarily in Python, offering flexibility and ease of integration for developers.

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 NotPunchnox/rkllama
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. OpenVINO · recommended 2×
  3. Rockchip NPU Execution Provider (RKNPU) · recommended 1×
  4. PyTorch · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to efficiently run large language models on Rockchip NPU devices like Orange Pi?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. Rockchip NPU Execution Provider (RKNPU)
    3. PyTorch
    4. TensorFlow
    5. ONNX
    6. Tengine
    7. Caffe
    8. TensorFlow Lite
    9. OpenVINO
    10. OpenVINO Model Optimizer
    11. OpenVINO Runtime
    12. PyTorch Mobile
    13. Torch-TensorRT

    AI recommended 13 alternatives but never named NotPunchnox/rkllama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an offline LLM server optimized for Rockchip NPU inference, similar to Ollama.
    you: not recommended
    AI recommended (in order):
    1. RKLLM (Rockchip LLM)
    2. OpenVINO
    3. ONNX Runtime
    4. Tengine Lite
    5. MNN (Mobile Neural Network)
    6. TVM (Apache TVM)

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite