RRepoGEO

REPOGEO REPORT · LITE

Andyyyy64/whichllm

Default branch main · commit ffcbc00d · scanned 5/22/2026, 11:31:54 PM

GitHub: 1,707 stars · 79 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 Andyyyy64/whichllm, 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
    Add a clear positioning statement to the README's opening

    Why:

    CURRENT
    **Find the best local LLM that actually runs on your hardware.** Auto-detects your GPU/CPU/RAM and ranks the top models from HuggingFace that fit your system.
    COPY-PASTE FIX
    **Find the best local LLM that actually runs on your hardware.** `whichllm` is a CLI tool designed for LLM discovery, selection, and benchmarking. It auto-detects your hardware (GPU/CPU/RAM) and ranks top models from HuggingFace that fit your system, helping you choose the right LLM without needing to install or configure multiple runtimes.
  • mediumtopics#2
    Add more specific topics for LLM selection and comparison

    Why:

    CURRENT
    ai, apple-silicon, benchmarks, cli, command-line-tool, gguf, gpu, huggingface, inference, llm, local-llm, ollama, python, vram
    COPY-PASTE FIX
    ai, apple-silicon, benchmarks, cli, command-line-tool, gguf, gpu, huggingface, inference, llm, local-llm, ollama, python, vram, llm-selection, llm-comparison, llm-evaluation, model-discovery, hardware-benchmarking
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/Andyyyy64/whichllm

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 Andyyyy64/whichllm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
llama.cpp
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 2×
  2. Ollama · recommended 1×
  3. LM Studio · recommended 1×
  4. oobabooga/text-generation-webui · recommended 1×
  5. llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How can I find the best performing local LLM for my specific hardware setup?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. text-generation-webui (oobabooga/text-generation-webui)
    4. llama.cpp
    5. llama-cpp-python
    6. KoboldCpp

    AI recommended 6 alternatives but never named Andyyyy64/whichllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good way to benchmark local LLM models on my machine's GPU and VRAM?
    you: not recommended
    AI recommended (in order):
    1. LMDeploy
    2. vLLM
    3. Hugging Face Transformers
    4. nvidia-smi
    5. llama.cpp
    6. TensorRT-LLM
    7. gpustat

    AI recommended 7 alternatives but never named Andyyyy64/whichllm. 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 Andyyyy64/whichllm?
    pass
    AI named Andyyyy64/whichllm explicitly

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

  • If a team adopts Andyyyy64/whichllm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Andyyyy64/whichllm 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 Andyyyy64/whichllm solve, and who is the primary audience?
    pass
    AI named Andyyyy64/whichllm 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 Andyyyy64/whichllm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Andyyyy64/whichllm.svg)](https://repogeo.com/en/r/Andyyyy64/whichllm)
HTML
<a href="https://repogeo.com/en/r/Andyyyy64/whichllm"><img src="https://repogeo.com/badge/Andyyyy64/whichllm.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Andyyyy64/whichllm — 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