RRepoGEO

REPOGEO REPORT · LITE

TheTom/turboquant_plus

Default branch main · commit 1224fef3 · scanned 5/10/2026, 4:32:37 AM

GitHub: 6,743 stars · 898 forks

AI VISIBILITY SCORE
23 /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
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 TheTom/turboquant_plus, 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 'About' description for LLM inference

    Why:

    COPY-PASTE FIX
    High-performance, quantized LLM inference with KV cache compression, featuring a `llama.cpp` fork, Swift MLX integration, and prebuilt binaries for cross-platform deployment.
  • mediumreadme#2
    Add a concise project summary directly under the main heading

    Why:

    COPY-PASTE FIX
    This repository is an experimental integration and research workspace for TurboQuant-related work targeting `llama.cpp`, focusing on KV cache compression for high-performance local LLM inference across platforms like Apple Silicon, CUDA, and ROCm.

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 TheTom/turboquant_plus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 1×
  2. mlmodelc · recommended 1×
  3. llama.cpp · recommended 1×
  4. MLX · recommended 1×
  5. Hugging Face `transformers` · recommended 1×
  • CATEGORY QUERY
    Seeking tools for fast, quantized LLM inference on Apple Silicon, ideally with native Swift.
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. mlmodelc
    3. llama.cpp
    4. MLX
    5. Hugging Face `transformers`
    6. optimum
    7. coremltools
    8. ONNX Runtime
    9. TensorFlow Lite

    AI recommended 9 alternatives but never named TheTom/turboquant_plus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which solutions provide cross-platform LLM serving with prebuilt binaries and OpenAI API support?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. LocalAI (go-skynet/LocalAI)
    4. vLLM (vllm-project/vllm)
    5. text-generation-inference (huggingface/text-generation-inference)
    6. LiteLLM (BerriAI/litellm)

    AI recommended 6 alternatives but never named TheTom/turboquant_plus. 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 TheTom/turboquant_plus?
    pass
    AI named TheTom/turboquant_plus explicitly

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

  • If a team adopts TheTom/turboquant_plus in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TheTom/turboquant_plus 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 TheTom/turboquant_plus solve, and who is the primary audience?
    pass
    AI did not name TheTom/turboquant_plus — 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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TheTom/turboquant_plus — 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