RRepoGEO

REPOGEO REPORT · LITE

XiongjieDai/GPU-Benchmarks-on-LLM-Inference

Default branch main · commit aa72e0ec · scanned 6/19/2026, 1:07:58 PM

GitHub: 1,922 stars · 75 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
17 /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
1 / 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 XiongjieDai/GPU-Benchmarks-on-LLM-Inference, 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
  • hightopics#1
    Add descriptive topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    gpu-benchmarks, llm-inference, nvidia-gpu, apple-silicon, llama-cpp, performance-testing, hardware-benchmarking, deep-learning, machine-learning
  • highreadme#2
    Strengthen README's opening to clarify project type

    Why:

    CURRENT
    Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference? 🧐
    COPY-PASTE FIX
    This repository provides comprehensive benchmarks and performance comparisons of various NVIDIA GPUs and Apple Silicon for Large Language Model (LLM) inference tasks. 🧐
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, choosing a suitable open-source license such as MIT or Apache-2.0.

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 XiongjieDai/GPU-Benchmarks-on-LLM-Inference
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA H100 Tensor Core GPU
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA H100 Tensor Core GPU · recommended 1×
  2. NVIDIA A100 Tensor Core GPU · recommended 1×
  3. NVIDIA L40S GPU · recommended 1×
  4. NVIDIA RTX 6000 Ada Generation · recommended 1×
  5. NVIDIA GeForce RTX 4090 · recommended 1×
  • CATEGORY QUERY
    Which hardware performs best for large language model inference tasks?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA H100 Tensor Core GPU
    2. NVIDIA A100 Tensor Core GPU
    3. NVIDIA L40S GPU
    4. NVIDIA RTX 6000 Ada Generation
    5. NVIDIA GeForce RTX 4090
    6. NVIDIA GeForce RTX 3090 / 3090 Ti

    AI recommended 6 alternatives but never named XiongjieDai/GPU-Benchmarks-on-LLM-Inference. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the performance differences between various GPUs for LLM workloads?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA H100
    2. NVIDIA H200
    3. NVIDIA A100
    4. NVIDIA RTX 4090
    5. NVIDIA RTX 3090
    6. NVIDIA RTX 3090 Ti
    7. NVIDIA RTX 4080 Super
    8. NVIDIA RTX 4080
    9. NVIDIA RTX 3060
    10. AMD Instinct MI300X
    11. AMD Instinct MI250
    12. NVIDIA CUDA
    13. cuDNN
    14. AMD ROCm

    AI recommended 14 alternatives but never named XiongjieDai/GPU-Benchmarks-on-LLM-Inference. 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 XiongjieDai/GPU-Benchmarks-on-LLM-Inference?
    pass
    AI named XiongjieDai/GPU-Benchmarks-on-LLM-Inference explicitly

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

  • If a team adopts XiongjieDai/GPU-Benchmarks-on-LLM-Inference in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name XiongjieDai/GPU-Benchmarks-on-LLM-Inference — 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?

  • In one sentence, what problem does the repo XiongjieDai/GPU-Benchmarks-on-LLM-Inference solve, and who is the primary audience?
    pass
    AI did not name XiongjieDai/GPU-Benchmarks-on-LLM-Inference — 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?

Embed your GEO score

Drop this badge into the README of XiongjieDai/GPU-Benchmarks-on-LLM-Inference. 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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XiongjieDai/GPU-Benchmarks-on-LLM-Inference — 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