RRepoGEO

REPOGEO REPORT · LITE

rafska/awesome-local-llm

Default branch main · commit d370e52e · scanned 5/15/2026, 7:13:08 AM

GitHub: 1,886 stars · 193 forks

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
  • highreadme#1
    Reinforce the 'awesome list' nature and purpose in the README's opening

    Why:

    CURRENT
    A curated list of awesome platforms, tools, practices and resources that helps run LLMs locally
    COPY-PASTE FIX
    This is a curated list of awesome platforms, tools, practices, and resources designed to help you discover and effectively run Large Language Models (LLMs) locally. It serves as a comprehensive guide for developers, researchers, and enthusiasts seeking to explore the local LLM ecosystem.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/rafska/awesome-local-llm
  • lowreadme#3
    Explicitly highlight the comprehensive scope and ecosystem focus in the README introduction

    Why:

    COPY-PASTE FIX
    Unlike single-purpose tools, this list provides a holistic view of the local LLM ecosystem, covering everything from inference platforms and models to agent frameworks, hardware considerations, and fine-tuning resources.

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
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. LM Studio · recommended 2×
  3. Jan AI · recommended 1×
  4. oobabooga/text-generation-webui · recommended 1×
  5. ggerganov/llama.cpp · recommended 1×
  • CATEGORY QUERY
    How can I run large language models on my own computer effectively?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan AI
    4. text-generation-webui (oobabooga/text-generation-webui)
    5. llama.cpp (ggerganov/llama.cpp)
    6. Hugging Face Transformers (huggingface/transformers)
    7. bitsandbytes
    8. AutoGPTQ

    AI recommended 8 alternatives but never named rafska/awesome-local-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools and platforms exist for self-hosting AI models for local development?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. LocalAI
    4. vLLM
    5. Hugging Face Transformers Library
    6. MLflow
    7. TensorFlow Serving
    8. TorchServe

    AI recommended 8 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 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?

  • 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 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?

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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