RRepoGEO

REPOGEO REPORT · LITE

tenstorrent/tt-metal

Default branch main · commit d1439304 · scanned 6/24/2026, 7:52:37 AM

GitHub: 1,544 stars · 512 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
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 tenstorrent/tt-metal, 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 clear H1 to the README emphasizing Tenstorrent hardware

    Why:

    CURRENT
    The current README's most prominent descriptive text is 'TT-NN is a Python & C++ Neural Network OP library.'
    COPY-PASTE FIX
    Add the following as the very first content in the README: `# tt-metal: Low-Level Programming and Operator Library for Tenstorrent AI Accelerators`
  • hightopics#2
    Refine topics to emphasize Tenstorrent hardware and low-level AI

    Why:

    CURRENT
    accelerator, ai, cuda, deepseek, gpu, img-gen, kernels, llama, llm, metal, scale-out, stable-diffusion, tenstorrent, video-gen
    COPY-PASTE FIX
    accelerator, ai, deepseek, img-gen, kernels, llama, llm, metal, scale-out, stable-diffusion, tenstorrent, video-gen, tenstorrent-hardware, ai-accelerator-sdk, low-level-ai
  • mediumabout#3
    Update the GitHub 'About' description for hardware specificity

    Why:

    CURRENT
    :metal: TT-NN operator library, and TT-Metalium low level kernel programming model.
    COPY-PASTE FIX
    :metal: TT-NN operator library and TT-Metalium low-level kernel programming model for Tenstorrent AI accelerators.

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 tenstorrent/tt-metal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO Toolkit
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO Toolkit · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. NVIDIA TensorRT · recommended 1×
  4. Habana SynapseAI SDK · recommended 1×
  5. DeepSpeed-MII · recommended 1×
  • CATEGORY QUERY
    How to accelerate large language model inference on specialized AI hardware?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. Habana SynapseAI SDK
    4. ONNX Runtime
    5. DeepSpeed-MII
    6. vLLM
    7. Hugging Face Transformers
    8. TensorFlow Lite
    9. PyTorch Compile
    10. TorchInductor

    AI recommended 10 alternatives but never named tenstorrent/tt-metal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to optimize neural network operators for AI chip performance.
    you: not recommended
    AI recommended (in order):
    1. Apache TVM
    2. TensorRT
    3. XLA
    4. OpenVINO Toolkit
    5. ONNX Runtime
    6. Glow

    AI recommended 6 alternatives but never named tenstorrent/tt-metal. 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 tenstorrent/tt-metal?
    pass
    AI named tenstorrent/tt-metal explicitly

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

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

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

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tenstorrent/tt-metal — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite