RRepoGEO

REPOGEO REPORT · LITE

Snowflake-Labs/snowflake-arctic

Default branch main · commit eadfba3b · scanned 6/9/2026, 10:48:03 PM

GitHub: 559 stars · 51 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Snowflake-Labs/snowflake-arctic, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Open-source research and optimized stacks from Snowflake AI for efficient, low-latency, high-throughput inference and fine-tuning of large language models like Llama 3.1 405B.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, deep-learning, machine-learning, ai, inference, fine-tuning, llama, llama-3-1, snowflake, ai-research, high-throughput, low-latency
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://www.snowflake.com/blog/snowflake-arctic-best-llm-enterprise-ai/

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 Snowflake-Labs/snowflake-arctic
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Ecosystem
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Ecosystem · recommended 1×
  2. Transformers · recommended 1×
  3. PEFT · recommended 1×
  4. TRL · recommended 1×
  5. Hugging Face Inference Endpoints · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune and deploy large language models for enterprise applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Ecosystem
    2. Transformers
    3. PEFT
    4. TRL
    5. Hugging Face Inference Endpoints
    6. Hugging Face AutoTrain
    7. AWS SageMaker
    8. SageMaker JumpStart
    9. SageMaker Training
    10. SageMaker Endpoints
    11. Google Cloud Vertex AI
    12. Vertex AI Model Garden
    13. Vertex AI Workbench
    14. Vertex AI Training & Endpoints
    15. Microsoft Azure Machine Learning
    16. Azure ML Model Catalog
    17. Azure ML Compute
    18. Azure ML Endpoints
    19. OpenAI API
    20. OpenAI Fine-tuning API
    21. GPT-3.5 Turbo
    22. GPT-4
    23. Anyscale Endpoints
    24. Ray
    25. VLLM

    AI recommended 25 alternatives but never named Snowflake-Labs/snowflake-arctic. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for low-latency, high-throughput large language model inference and training.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. vLLM (vllm-project/vllm)
    4. PyTorch FSDP (pytorch/pytorch)
    5. Hugging Face Accelerate (huggingface/accelerate)
    6. TensorRT
    7. OpenVINO (openvinotoolkit/openvino)

    AI recommended 7 alternatives but never named Snowflake-Labs/snowflake-arctic. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 Snowflake-Labs/snowflake-arctic?
    pass
    AI named Snowflake-Labs/snowflake-arctic explicitly

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

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

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

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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