RRepoGEO

REPOGEO REPORT · LITE

aliyun/SimAI

Default branch master · commit f5efb5a9 · scanned 6/3/2026, 9:23:02 AM

GitHub: 1,002 stars · 174 forks

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 aliyun/SimAI, 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's opening to clearly state its purpose

    Why:

    CURRENT
    # SimAI

[](LICENSE)
[](https://ennanzhai.github.io/pub/nsdi25spring-simai.pdf)

# Latest News
    COPY-PASTE FIX
    # SimAI

SimAI is an open-source framework for large-scale simulation of Large Language Model (LLM) inference performance and resource allocation, enabling detailed analysis of prefill/decode separation, GPU memory, and request scheduling strategies.

[](LICENSE)
[](https://ennanzhai.github.io/pub/nsdi25spring-simai.pdf)

# Latest News
  • highabout#2
    Add a concise description to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Open-source framework for large-scale simulation of Large Language Model (LLM) inference performance, resource allocation, and scheduling strategies.
  • hightopics#3
    Add specific topics that reflect the repository's core functionality

    Why:

    COPY-PASTE FIX
    llm-inference-simulation, large-language-models, performance-modeling, resource-allocation, gpu-memory, request-scheduling, deep-learning-systems

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 aliyun/SimAI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Triton Inference Server
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Triton Inference Server · recommended 1×
  2. Model Analyzer · recommended 1×
  3. DeepSpeed · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. DeepSpeed-Inference · recommended 1×
  • CATEGORY QUERY
    How to simulate large language model inference performance and resource allocation?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. Model Analyzer
    3. DeepSpeed
    4. DeepSpeed-MII
    5. DeepSpeed-Inference
    6. TensorRT-LLM
    7. OpenVINO
    8. PyTorch
    9. TensorFlow
    10. JAX
    11. torch.profiler
    12. tf.profiler
    13. nvidia-smi
    14. MLPerf Inference Benchmarks

    AI recommended 14 alternatives but never named aliyun/SimAI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate multi-request LLM inference workloads and scheduling strategies?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. Locust (locustio/locust)
    3. Prometheus (prometheus/prometheus)
    4. Grafana (grafana/grafana)
    5. JMeter (apache/jmeter)
    6. K6 (grafana/k6)
    7. DeepSpeed-MII (microsoft/DeepSpeed)
    8. httpx (encode/httpx)
    9. requests (psf/requests)

    AI recommended 9 alternatives but never named aliyun/SimAI. 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 aliyun/SimAI?
    pass
    AI named aliyun/SimAI explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of aliyun/SimAI. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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aliyun/SimAI — 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