REPOGEO REPORT · LITE

Luce-Org/lucebox-hub

Default branch main · commit 73433eea · scanned 5/18/2026, 4:23:35 PM

GitHub: 2,152 stars · 200 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

27 /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

1 / 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 Luce-Org/lucebox-hub, 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 to explicitly state this repo's function

Why:

CURRENT

Local LLM inference server built for speed. Custom kernels, speculative prefill & decoding, quantized GGUF paths.
Each project is a new optimization to our engine for a specific model family and hardware target.

COPY-PASTE FIX

Lucebox Hub is an LLM inference server built for speed, featuring custom kernels, speculative prefill & decoding, and quantized GGUF paths. Each project within this repository is a new optimization to our engine for a specific model family and hardware target.

mediumreadme#2

Add a 'Why Lucebox Hub?' section comparing to alternatives

Why:

COPY-PASTE FIX

## Why Lucebox Hub?
While projects like `llama.cpp`, `Ollama`, and `vLLM` offer excellent general-purpose LLM serving, Lucebox Hub focuses on pushing the boundaries of speed for specific consumer hardware (e.g., RTX 3090) through highly optimized custom kernels, speculative prefill, and decoding. Our benchmarks demonstrate significant speedups over common alternatives for targeted models and hardware.

lowabout#3

Clarify the repository description to include 'Hub'

Why:

CURRENT

Lucebox: LLM inference server built for speed for specific consumer hardware.

COPY-PASTE FIX

Lucebox Hub: An LLM inference server built for speed for specific consumer hardware, featuring custom kernels and speculative decoding.

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 Luce-Org/lucebox-hub

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

vLLM

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

vLLM · recommended 2×
llama.cpp · recommended 1×
Ollama · recommended 1×
Text Generation Inference (TGI) by Hugging Face · recommended 1×
ExLlamaV2 · recommended 1×

CATEGORY QUERY
What are the best options for high-performance local LLM serving on consumer GPUs?
you: not recommended
AI recommended (in order):
1. llama.cpp
2. Ollama
3. vLLM
4. Text Generation Inference (TGI) by Hugging Face
5. ExLlamaV2
6. TensorRT-LLM
AI recommended 6 alternatives but never named Luce-Org/lucebox-hub. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Are there alternatives to common LLM runtimes offering faster inference with custom kernels?
you: not recommended
AI recommended (in order):
1. NVIDIA TensorRT-LLM
2. vLLM
3. DeepSpeed-MII
4. OpenVINO
5. ONNX Runtime
6. TVM
AI recommended 6 alternatives but never named Luce-Org/lucebox-hub. 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 Luce-Org/lucebox-hub?
pass
AI did not name Luce-Org/lucebox-hub — 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 Luce-Org/lucebox-hub in production, what risks or prerequisites should they evaluate first?
pass
AI named Luce-Org/lucebox-hub 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 Luce-Org/lucebox-hub solve, and who is the primary audience?
pass
AI did not name Luce-Org/lucebox-hub — 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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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/Luce-Org/lucebox-hub"><img src="https://repogeo.com/badge/Luce-Org/lucebox-hub.svg" alt="RepoGEO" /></a>

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Luce-Org/lucebox-hub — 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