REPOGEO REPORT · LITE

guinmoon/LLMFarm

Default branch main · commit ee6d251a · scanned 5/19/2026, 4:41:53 AM

GitHub: 2,028 stars · 168 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 guinmoon/LLMFarm, 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 the README's opening paragraph to emphasize its application nature

Why:

CURRENT

LLMFarm is an iOS and MacOS app to work with large language models (LLM). It allows you to load different LLMs with certain parameters.With LLMFarm, you can test the performance of different LLMs on iOS and macOS and find the most suitable model for your project.

COPY-PASTE FIX

LLMFarm is a powerful, user-friendly desktop and mobile application for running large language models (LLMs) locally and offline on iOS and macOS. It provides a graphical interface to easily load, configure, and interact with various LLMs, enabling private, on-device AI experiences.

mediumtopics#2

Add more specific topics related to local and offline LLM applications

Why:

CURRENT

ai, ggml, gpt-2, gptneox, ios, llama, macos, rwkv, starcoder, swift

COPY-PASTE FIX

ai, ggml, gpt-2, gptneox, ios, llama, macos, rwkv, starcoder, swift, local-llm, offline-llm, llm-app

lowreadme#3

Add a 'Why LLMFarm?' section to differentiate from frameworks and cloud APIs

Why:

COPY-PASTE FIX

### Why LLMFarm?
LLMFarm stands out as a dedicated application for running LLMs directly on your Apple devices, offering a user-friendly interface for local, offline, and private AI interactions. Unlike lower-level frameworks (e.g., Core ML, MLX) which require significant development effort, or cloud-based APIs that send your data off-device, LLMFarm provides a ready-to-use solution for experimenting with and deploying various LLMs on your own hardware.

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 guinmoon/LLMFarm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Core ML

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Core ML · recommended 2×
Ollama · recommended 1×
LM Studio · recommended 1×
Jan · recommended 1×
llama.cpp · recommended 1×

CATEGORY QUERY
How can I run large language models offline directly on my iPhone or MacBook?
you: not recommended
AI recommended (in order):
1. Ollama
2. LM Studio
3. Jan
4. llama.cpp
5. MLC LLM
6. Core ML
7. RunPod
8. Vast.ai
9. Google Cloud
AI recommended 9 alternatives but never named guinmoon/LLMFarm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What framework allows deploying and testing various AI models on Apple silicon devices?
you: not recommended
AI recommended (in order):
1. Core ML
2. PyTorch (pytorch/pytorch)
3. TensorFlow (tensorflow/tensorflow)
4. MLX (ml-explore/mlx)
5. ONNX Runtime (microsoft/onnxruntime)
6. Turi Create (apple/turicreate)
AI recommended 6 alternatives but never named guinmoon/LLMFarm. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 guinmoon/LLMFarm?
pass
AI named guinmoon/LLMFarm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts guinmoon/LLMFarm in production, what risks or prerequisites should they evaluate first?
pass
AI named guinmoon/LLMFarm 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 guinmoon/LLMFarm solve, and who is the primary audience?
pass
AI named guinmoon/LLMFarm 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 guinmoon/LLMFarm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/guinmoon/LLMFarm.svg)](https://repogeo.com/en/r/guinmoon/LLMFarm)

HTML

<a href="https://repogeo.com/en/r/guinmoon/LLMFarm"><img src="https://repogeo.com/badge/guinmoon/LLMFarm.svg" alt="RepoGEO" /></a>

Pro

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guinmoon/LLMFarm — 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