RRepoGEO

REPOGEO REPORT · LITE

efeslab/Nanoflow

Default branch Nanoflow-python · commit f179a907 · scanned 5/29/2026, 10:58:02 PM

GitHub: 961 stars · 49 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 efeslab/Nanoflow, 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
  • highabout#1
    Clarify the About description to prevent mis-categorization

    Why:

    CURRENT
    A throughput-oriented high-performance serving framework for LLMs
    COPY-PASTE FIX
    High-performance LLM serving framework for GPU inference, outperforming vLLM and TensorRT-LLM in throughput.
  • highlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of a standard open-source license like Apache-2.0 or MIT.
  • mediumreadme#3
    Strengthen the README's opening statement for category clarity

    Why:

    CURRENT
    NanoFlow is a throughput-oriented high-performance serving framework for LLMs. NanoFlow consistently delivers superior throughput compared to vLLM, Deepspeed-FastGen, and TensorRT-LLM.
    COPY-PASTE FIX
    Nanoflow is an advanced LLM serving framework designed for high-throughput GPU inference. It consistently delivers superior throughput compared to vLLM, Deepspeed-FastGen, and TensorRT-LLM, achieving up to 1.91x throughput boost compared to TensorRT-LLM.

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 efeslab/Nanoflow
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 1×
  2. TGI · recommended 1×
  3. NVIDIA TensorRT-LLM · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for high-throughput LLM inference serving on GPU?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. NVIDIA TensorRT-LLM
    4. DeepSpeed-MII
    5. OpenVINO
    6. Ray Serve
    7. Anyscale Endpoints
    8. TorchServe

    AI recommended 8 alternatives but never named efeslab/Nanoflow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which LLM serving framework offers superior throughput compared to vLLM and TensorRT-LLM?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed-MII (microsoft/DeepSpeed)
    2. LightLLM (ModelTC/lightllm)
    3. TGI (huggingface/text-generation-inference)
    4. OpenVINO (openvinotoolkit/openvino)
    5. Triton Inference Server (triton-inference-server/server)

    AI recommended 5 alternatives but never named efeslab/Nanoflow. 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 efeslab/Nanoflow?
    pass
    AI named efeslab/Nanoflow explicitly

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

  • If a team adopts efeslab/Nanoflow in production, what risks or prerequisites should they evaluate first?
    pass
    AI named efeslab/Nanoflow 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 efeslab/Nanoflow solve, and who is the primary audience?
    pass
    AI named efeslab/Nanoflow explicitly

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

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efeslab/Nanoflow — 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