RRepoGEO

REPOGEO REPORT · LITE

GoogleCloudPlatform/localllm

Default branch main · commit 2939bd32 · scanned 6/28/2026, 2:07:37 PM

GitHub: 1,551 stars · 119 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
30 /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
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 GoogleCloudPlatform/localllm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Run quantized Large Language Models (LLMs) locally within Google Cloud Workstations, leveraging llama-cpp-python for inference in a cloud development environment.
  • mediumreadme#2
    Refine the README's opening sentence to emphasize cloud context

    Why:

    CURRENT
    # local-llm

Run LLMs locally on Cloud Workstations. Uses:
    COPY-PASTE FIX
    # local-llm

Run quantized Large Language Models (LLMs) locally within Google Cloud Workstations, providing a ready-to-use development environment. Uses:

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 GoogleCloudPlatform/localllm
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. oobabooga/text-generation-webui · recommended 2×
  4. bitsandbytes · recommended 2×
  5. Hugging Face Transformers Library · recommended 1×
  • CATEGORY QUERY
    How can I run large language models locally within a cloud development environment?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. text-generation-webui (oobabooga/text-generation-webui)
    4. Hugging Face Transformers Library
    5. bitsandbytes
    6. accelerate
    7. Llama.cpp
    8. llama-cpp-python
    9. vLLM
    10. Ray Serve

    AI recommended 10 alternatives but never named GoogleCloudPlatform/localllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best way to set up a local LLM inference environment using quantized models?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. Jan
    4. text-generation-webui (oobabooga/text-generation-webui)
    5. llama.cpp
    6. Transformers
    7. bitsandbytes
    8. AutoGPTQ

    AI recommended 8 alternatives but never named GoogleCloudPlatform/localllm. 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 GoogleCloudPlatform/localllm?
    pass
    AI named GoogleCloudPlatform/localllm explicitly

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

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

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

Embed your GEO score

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GoogleCloudPlatform/localllm — 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