RRepoGEO

REPOGEO REPORT · LITE

ai-dynamo/dynamo

Default branch main · commit 0e6e83ea · scanned 5/18/2026, 7:06:44 PM

GitHub: 6,807 stars · 1,112 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 ai-dynamo/dynamo, 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, distributed-inference, inference-serving, gpu-orchestration, kv-caching, multi-node, ai-serving, deep-learning-inference, rust, python
  • highreadme#2
    Strengthen the README's opening statement for core purpose

    Why:

    CURRENT
    **The open-source, datacenter-scale inference stack.** Dynamo is the orchestration layer above inference engines — it doesn't replace SGLang, TensorRT-LLM, or vLLM, it turns them into a coordinated multi-node inference system. Disaggregated serving, intelligent routing, multi-tier KV caching, and automatic scaling work together to maximize throughput and minimize latency for LLM, reasoning, multimodal, and video generation workloads.
    COPY-PASTE FIX
    Dynamo is the open-source, datacenter-scale orchestration layer for distributed LLM inference serving. It coordinates multiple inference engines (like vLLM, SGLang, TensorRT-LLM) across GPUs and nodes to provide intelligent routing, multi-tier KV caching, and automatic scaling for high-throughput, low-latency LLM, reasoning, multimodal, and video generation workloads.
  • mediumreadme#3
    Explicitly state the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

Dynamo is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for full details.

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 ai-dynamo/dynamo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray Serve
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray Serve · recommended 2×
  2. NVIDIA Triton Inference Server · recommended 1×
  3. DeepSpeed · recommended 1×
  4. Hugging Face TGI (Text Generation Inference) · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    How to scale large language model inference across multiple GPU servers?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. DeepSpeed
    3. Hugging Face TGI (Text Generation Inference)
    4. Ray Serve
    5. OpenVINO
    6. ONNX Runtime

    AI recommended 6 alternatives but never named ai-dynamo/dynamo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for managing distributed inference serving with intelligent routing and caching for LLMs?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Triton Inference Server
    3. KServe
    4. Ray Serve
    5. OpenLLM
    6. BentoML

    AI recommended 6 alternatives but never named ai-dynamo/dynamo. 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 ai-dynamo/dynamo?
    pass
    AI named ai-dynamo/dynamo explicitly

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

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

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

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ai-dynamo/dynamo — 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