RRepoGEO

REPOGEO REPORT · LITE

microsoft/T-MAC

Default branch main · commit 7042f8f7 · scanned 6/1/2026, 10:16:45 AM

GitHub: 961 stars · 84 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 microsoft/T-MAC, 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
    Clarify the project's core purpose at the very top of the README

    Why:

    CURRENT
    # T-MAC

<h3 align="center">
    
    <p><a href=https://huggingface.co/1bitLLM/bitnet_b1_58-3B>BitNet</a> on M2-Ultra with T-MAC (LUT-based) vs llama.cpp (dequantization-based)</p>
</h3>
    COPY-PASTE FIX
    # T-MAC

T-MAC is a high-performance kernel library designed to accelerate low-bit (int1/2/3/4) Large Language Model (LLM) inference on CPUs and NPUs, utilizing lookup tables to eliminate dequantization overhead.

<h3 align="center">
    
    <p><a href=https://huggingface.co/1bitLLM/bitnet_b1_58-3B>BitNet</a> on M2-Ultra with T-MAC (LUT-based) vs llama.cpp (dequantization-based)</p>
</h3>
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, low-bit, quantization, inference, cpu, npu, acceleration, machine-learning, deep-learning, bitnet
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://huggingface.co/1bitLLM/bitnet_b1_58-3B

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 microsoft/T-MAC
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. PyTorch · recommended 2×
  4. TensorFlow Lite · recommended 2×
  5. llama.cpp · recommended 1×
  • CATEGORY QUERY
    How can I efficiently run highly quantized large language models on standard CPUs or NPUs?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. OpenVINO
    3. ONNX Runtime
    4. MLC LLM
    5. TensorRT-LLM
    6. PyTorch
    7. TensorFlow
    8. TensorFlow Lite

    AI recommended 8 alternatives but never named microsoft/T-MAC. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques to accelerate low-bit LLM inference, especially on edge devices?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Jetson Series
    2. Google Coral Edge TPU
    3. Qualcomm Snapdragon Processors
    4. Intel Movidius Myriad X
    5. PyTorch
    6. TensorFlow Lite
    7. ONNX Runtime
    8. NVIDIA TensorRT
    9. OpenVINO
    10. TVM (Apache TVM)
    11. TinyLlama
    12. MobileBERT
    13. DistilBERT
    14. Phi-2
    15. Phi-3 Mini

    AI recommended 15 alternatives but never named microsoft/T-MAC. 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 microsoft/T-MAC?
    pass
    AI named microsoft/T-MAC explicitly

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

  • If a team adopts microsoft/T-MAC in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/T-MAC 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 microsoft/T-MAC solve, and who is the primary audience?
    pass
    AI named microsoft/T-MAC 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 microsoft/T-MAC. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/microsoft/T-MAC"><img src="https://repogeo.com/badge/microsoft/T-MAC.svg" alt="RepoGEO" /></a>
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microsoft/T-MAC — 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