RRepoGEO

REPOGEO REPORT · LITE

RahulSChand/gpu_poor

Default branch main · commit ad2fc0ef · scanned 5/18/2026, 6:11:56 AM

GitHub: 1,398 stars · 89 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
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 RahulSChand/gpu_poor, 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 README H1 and opening paragraph to highlight unique value

    Why:

    CURRENT
    # Can my GPU run this LLM? & at what token/s?

Calculates how much **GPU memory you need** and how much **token/s you can get** for any LLM & GPU/CPU.
    COPY-PASTE FIX
    # gpu_poor: LLM GPU Memory & Token/s Estimator for Resource-Constrained Environments

This tool precisely calculates GPU memory requirements and token generation speed for any LLM, specifically designed to help users with limited VRAM or single-GPU setups determine feasibility and optimize performance.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the repository root to clearly state the terms of use.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    ggml, gpu, huggingface, language-model, llama, llama2, llamacpp, llm, pytorch, quantization
    COPY-PASTE FIX
    Add 'llm-performance', 'gpu-profiling', 'resource-estimation', 'vram-calculator', 'llm-quantization-estimation' to the existing topics.

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 RahulSChand/gpu_poor
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. Hugging Face Transformers Library · recommended 1×
  3. DeepSpeed · recommended 1×
  4. PyTorch Profiler · recommended 1×
  5. Text Generation Inference (TGI) by Hugging Face · recommended 1×
  • CATEGORY QUERY
    How to estimate GPU memory usage and token generation speed for large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. DeepSpeed
    3. PyTorch Profiler
    4. Text Generation Inference (TGI) by Hugging Face
    5. vLLM
    6. NVIDIA Triton Inference Server
    7. ONNX Runtime
    8. TensorRT

    AI recommended 8 alternatives but never named RahulSChand/gpu_poor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to predict LLM performance and resource needs with different quantization techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. NVIDIA TensorRT
    3. OpenVINO
    4. ONNX Runtime
    5. PyTorch Quantization
    6. MLPerf Inference Benchmarks

    AI recommended 6 alternatives but never named RahulSChand/gpu_poor. 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 RahulSChand/gpu_poor?
    pass
    AI named RahulSChand/gpu_poor explicitly

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

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

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

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RahulSChand/gpu_poor — 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