RRepoGEO

REPOGEO REPORT · LITE

InternLM/lmdeploy

Default branch main · commit 3cb5f03f · scanned 5/12/2026, 10:06:45 PM

GitHub: 7,850 stars · 697 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 InternLM/lmdeploy, 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
    Add a concise, benefit-oriented opening paragraph to the README

    Why:

    CURRENT
    The README excerpt starts with 'Latest News' after the initial badges and links.
    COPY-PASTE FIX
    Add the following text immediately after the initial badges/links and before 'Latest News':

LMDeploy is a comprehensive, high-performance toolkit designed for efficiently compressing, deploying, and serving large language models (LLMs). It provides an integrated suite of advanced optimizations to achieve high throughput and low latency for LLM inference, making it ideal for production environments requiring robust and scalable LLM serving capabilities.
  • mediumtopics#2
    Expand repository topics with more specific LLM serving and optimization terms

    Why:

    CURRENT
    codellama, cuda-kernels, deepspeed, fastertransformer, internlm, llama, llama2, llama3, llm, llm-inference, turbomind
    COPY-PASTE FIX
    Add the following topics: llm-serving, llm-deployment, quantization, inference-engine, high-throughput
  • lowcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'LMDeploy vs. Alternatives' or 'Why Choose LMDeploy?' that briefly compares its features, performance, and unique advantages against vLLM, NVIDIA TensorRT-LLM, and TGI.

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 InternLM/lmdeploy
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. NVIDIA TensorRT-LLM · recommended 1×
  3. TGI · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. Ray Serve · recommended 1×
  • CATEGORY QUERY
    How to efficiently serve large language models with high throughput on GPU?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. NVIDIA TensorRT-LLM
    3. TGI
    4. DeepSpeed-MII
    5. Ray Serve
    6. OpenVINO

    AI recommended 6 alternatives but never named InternLM/lmdeploy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for optimizing and quantizing large language models for faster inference?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum (huggingface/optimum)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. NVIDIA TensorRT
    4. OpenVINO Toolkit (openvinotoolkit/openvino)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)
    7. DeepSpeed (microsoft/DeepSpeed)
    8. TVM (apache/tvm)

    AI recommended 8 alternatives but never named InternLM/lmdeploy. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 InternLM/lmdeploy?
    pass
    AI named InternLM/lmdeploy explicitly

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

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