RRepoGEO

REPOGEO REPORT · LITE

airockchip/rknn-llm

Default branch main · commit 878f9361 · scanned 6/24/2026, 4:56:56 AM

GitHub: 1,539 stars · 204 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 airockchip/rknn-llm, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Software stack and toolkit for converting, quantizing, and deploying Large Language Models (LLMs) efficiently on Rockchip NPU-powered edge devices (RK3588, RK3576, RK3562, RV1126B).
  • hightopics#2
    Add relevant repository topics

    Why:

    COPY-PASTE FIX
    llm, large-language-models, rockchip, npu, edge-ai, model-deployment, quantization, inference, rknn-toolkit, embedded-ai
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project includes a LICENSE file. Please refer to the LICENSE file for the specific terms and conditions that apply.

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 airockchip/rknn-llm
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. RKNN-Toolkit2 · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. TNN · recommended 1×
  • CATEGORY QUERY
    How to deploy large language models efficiently on Rockchip NPU devices?
    you: not recommended
    AI recommended (in order):
    1. RKNN-Toolkit2
    2. PyTorch
    3. TensorFlow
    4. ONNX Runtime
    5. TNN
    6. MNN

    AI recommended 6 alternatives but never named airockchip/rknn-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Toolkit for converting and quantizing LLM models for embedded NPU platforms?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. ONNX Runtime
    3. TensorRT
    4. Apache TVM
    5. TensorFlow Lite
    6. Qualcomm AI Engine Direct
    7. Arm NN
    8. Vela

    AI recommended 8 alternatives but never named airockchip/rknn-llm. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 airockchip/rknn-llm?
    pass
    AI named airockchip/rknn-llm explicitly

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

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

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

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airockchip/rknn-llm — 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