RRepoGEO

REPOGEO REPORT · LITE

dvmazur/mixtral-offloading

Default branch master · commit ce545188 · scanned 5/28/2026, 11:33:11 PM

GitHub: 2,329 stars · 227 forks

AI VISIBILITY SCORE
35 /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
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 dvmazur/mixtral-offloading, 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 H1 and first sentence to highlight consumer hardware support

    Why:

    CURRENT
    # Mixtral offloading

This project implements efficient inference of Mixtral-8x7B models.
    COPY-PASTE FIX
    # Mixtral Offloading: Run Mixtral-8x7B on Consumer GPUs & Colab

This project enables efficient inference of Mixtral-8x7B models even on consumer desktops and Google Colab by intelligently offloading experts.
  • highhomepage#2
    Add a Homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/github/dvmazur/mixtral-offloading/blob/master/notebooks/demo.ipynb
  • mediumtopics#3
    Expand repository topics to include hardware constraints

    Why:

    CURRENT
    colab-notebook, deep-learning, google-colab, language-model, llm, mixture-of-experts, offloading, pytorch, quantization
    COPY-PASTE FIX
    colab-notebook, deep-learning, google-colab, language-model, llm, mixture-of-experts, offloading, pytorch, quantization, consumer-gpu, low-resource, vram-optimization

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 dvmazur/mixtral-offloading
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
llama.cpp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. llama.cpp · recommended 1×
  2. vLLM · recommended 1×
  3. Hugging Face transformers · recommended 1×
  4. bitsandbytes · recommended 1×
  5. ExLlamaV2 · recommended 1×
  • CATEGORY QUERY
    How to run large language models like Mixtral efficiently on consumer GPUs or Colab?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. vLLM
    3. Hugging Face transformers
    4. bitsandbytes
    5. ExLlamaV2
    6. TGI (Text Generation Inference)
    7. DeepSpeed-MII

    AI recommended 7 alternatives but never named dvmazur/mixtral-offloading. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a solution for offloading and quantizing Mixture-of-Experts models for low-resource environments.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/deepspeed)
    2. Hugging Face Optimum (huggingface/optimum)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. Intel OpenVINO (openvinotoolkit/openvino)
    5. TensorRT
    6. Apache TVM (apache/tvm)
    7. PyTorch Quantization Toolkit (pytorch/pytorch)
    8. TensorFlow Lite (tensorflow/tensorflow)

    AI recommended 8 alternatives but never named dvmazur/mixtral-offloading. 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 dvmazur/mixtral-offloading?
    pass
    AI named dvmazur/mixtral-offloading explicitly

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

  • If a team adopts dvmazur/mixtral-offloading in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dvmazur/mixtral-offloading 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 dvmazur/mixtral-offloading solve, and who is the primary audience?
    pass
    AI named dvmazur/mixtral-offloading 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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MARKDOWN (README)
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dvmazur/mixtral-offloading — 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