RRepoGEO

REPOGEO REPORT · LITE

rafska/awesome-local-llm

Default branch main · commit b6acd1c8 · scanned 6/25/2026, 11:17:51 PM

GitHub: 2,329 stars · 263 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 rafska/awesome-local-llm, 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
  • highhomepage#1
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/rafska/awesome-local-llm
  • highreadme#2
    Strengthen the README's opening to emphasize its role as a discovery hub for local LLM tools

    Why:

    CURRENT
    A curated list of awesome platforms, tools, practices and resources that helps run LLMs locally
    COPY-PASTE FIX
    This is the definitive curated list of awesome platforms, tools, practices, and resources designed to help you discover, evaluate, and run Large Language Models (LLMs) efficiently on your local hardware. Unlike individual tools, this repository provides a comprehensive overview and comparison point for the entire local LLM ecosystem.
  • mediumtopics#3
    Add more specific topics to reflect the breadth of tools covered

    Why:

    CURRENT
    ai, awesome, awesome-list, llm, local, local-ai, local-llm, resources, self-hosted, selfhosted
    COPY-PASTE FIX
    ai, awesome, awesome-list, llm, local, local-ai, local-llm, resources, self-hosted, selfhosted, llm-inference, llm-ui, rag, agent-frameworks, llm-models, llm-tools

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 rafska/awesome-local-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. Ollama · recommended 2×
  3. Jan · recommended 2×
  4. llama.cpp · recommended 1×
  5. llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How can I run large language models on my own hardware without cloud dependencies?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. Jan
    4. llama.cpp
    5. llama-cpp-python
    6. Hugging Face `transformers` library
    7. bitsandbytes
    8. AutoGPTQ
    9. AWQ
    10. Text Generation WebUI (oobabooga/text-generation-webui)

    AI recommended 10 alternatives but never named rafska/awesome-local-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools and resources for self-hosting generative AI models locally?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. LocalAI
    5. text-generation-webui
    6. Hugging Face Transformers Library
    7. ComfyUI

    AI recommended 7 alternatives but never named rafska/awesome-local-llm. 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 rafska/awesome-local-llm?
    pass
    AI did not name rafska/awesome-local-llm — 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?

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

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

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rafska/awesome-local-llm — 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