RRepoGEO

REPOGEO REPORT · LITE

microsoft/MInference

Default branch main · commit a4eb395f · scanned 5/19/2026, 6:31:38 AM

GitHub: 1,213 stars · 77 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
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/MInference, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-inference, long-context-llm, sparse-attention, deep-learning-acceleration, gpu-optimization, machine-learning-inference
  • highreadme#2
    Insert a clear, specific opening sentence in the README

    Why:

    CURRENT
    _Now, you can process **1M context 10x faster in a single A100** using Long-context LLMs like LLaMA-3-8B-1M, GLM-4-1M, with even **better accuracy**, try **MInference 1.0** right now!_
    COPY-PASTE FIX
    MInference is a cutting-edge solution designed to speed up Long-context LLMs' inference by using approximate and dynamic sparse attention, reducing inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
    
    _Now, you can process **1M context 10x faster in a single A100** using Long-context LLMs like LLaMA-3-8B-1M, GLM-4-1M, with even **better accuracy**, try **MInference 1.0** right now!_
  • mediumabout#3
    Refine the 'About' description for clarity and impact

    Why:

    CURRENT
    [NeurIPS'24 Spotlight, ICLR'25, ICML'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
    COPY-PASTE FIX
    Accelerate Long-context LLM inference by up to 10x for pre-filling on an A100 using approximate and dynamic sparse attention, maintaining accuracy. Featured at NeurIPS'24 Spotlight, ICLR'25, ICML'25.

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/MInference
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 2×
  2. FlashAttention / FlashAttention-2 · recommended 1×
  3. PagedAttention (vLLM) · recommended 1×
  4. DeepSpeed-MII / DeepSpeed Inference · recommended 1×
  5. NVIDIA Triton Inference Server · recommended 1×
  • CATEGORY QUERY
    How to accelerate inference for large language models with very long contexts?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2
    2. PagedAttention (vLLM)
    3. DeepSpeed-MII / DeepSpeed Inference
    4. NVIDIA Triton Inference Server
    5. AWQ
    6. GPTQ
    7. bitsandbytes
    8. Google's Draft-and-Verify
    9. Medusa
    10. LoRA (Low-Rank Adaptation)

    AI recommended 10 alternatives but never named microsoft/MInference. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to reduce latency for pre-filling long prompts in LLMs efficiently.
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference by Hugging Face)
    3. TensorRT-LLM
    4. Hugging Face Transformers Library
    5. DeepMind's AlphaCode 2
    6. Google's Med-PaLM 2
    7. OpenVINO
    8. ONNX Runtime
    9. AWQ (Activation-aware Weight Quantization)
    10. GPTQ (General-purpose Quantization)
    11. bitsandbytes

    AI recommended 11 alternatives but never named microsoft/MInference. 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/MInference?
    pass
    AI named microsoft/MInference 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/MInference in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/MInference 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/MInference solve, and who is the primary audience?
    pass
    AI named microsoft/MInference explicitly

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

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microsoft/MInference — 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