RRepoGEO

REPOGEO REPORT · LITE

hahnyuan/LLM-Viewer

Default branch main · commit 1893e4b5 · scanned 6/9/2026, 3:52:53 AM

GitHub: 650 stars · 89 forks

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 hahnyuan/LLM-Viewer, 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 the README's opening to differentiate from generic profilers

    Why:

    CURRENT
    # LLM-Viewer

LLM-Viewer is a tool for visualizing Language and Learning Models (LLMs) and analyzing the performance on different hardware platforms. It enables network-wise analysis, considering factors such as peak memory consumption and total inference time cost. With LLM-Viewer, you can gain valuable insights into LLM inference and performance optimization.
    COPY-PASTE FIX
    # LLM-Viewer: LLM Inference Visualization & Performance Analysis

LLM-Viewer is a dedicated, user-friendly tool for visualizing and analyzing the inference performance of Large Language Models (LLMs) on various hardware platforms. It offers deep, LLM-specific insights into computation, memory, and hardware roofline models, providing a higher-level perspective than generic system profilers.
  • mediumhomepage#2
    Add the project's homepage URL

    Why:

    COPY-PASTE FIX
    Add the correct URL for the LLM-Viewer web interface (e.g., `https://your-project-url.com`)

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 hahnyuan/LLM-Viewer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Nsight Systems
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Nsight Systems · recommended 2×
  2. PyTorch Profiler · recommended 2×
  3. Intel VTune Profiler · recommended 2×
  4. DeepSpeed · recommended 2×
  5. Nsight Compute · recommended 1×
  • CATEGORY QUERY
    How can I visualize and analyze the performance of large language models on various hardware?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. Nsight Compute
    3. TensorBoard
    4. TensorFlow Profiler
    5. PyTorch Profiler
    6. Weights & Biases
    7. Prometheus
    8. Grafana
    9. Intel VTune Profiler
    10. DeepSpeed
    11. Megatron-LM

    AI recommended 11 alternatives but never named hahnyuan/LLM-Viewer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for deep diving into LLM inference bottlenecks like memory and computation costs?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. PyTorch Profiler
    3. DeepSpeed
    4. Intel VTune Profiler
    5. TensorRT
    6. torch.cuda.memory_allocated()
    7. htop
    8. nvidia-smi

    AI recommended 8 alternatives but never named hahnyuan/LLM-Viewer. 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 hahnyuan/LLM-Viewer?
    pass
    AI named hahnyuan/LLM-Viewer explicitly

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

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

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

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hahnyuan/LLM-Viewer — 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