RRepoGEO

REPOGEO REPORT · LITE

tile-ai/TileRT

Default branch main · commit 242f7b30 · scanned 6/29/2026, 12:16:34 AM

GitHub: 1,492 stars · 94 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)

3 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 tile-ai/TileRT, 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 relevant GitHub topics to the repository

    Why:

    COPY-PASTE FIX
    llm-inference, low-latency, gpu-acceleration, ai-runtime, large-language-models, deep-learning, high-performance, model-serving
  • mediumreadme#2
    Ensure the core value proposition is immediately visible in the README

    Why:

    CURRENT
    The README currently starts with a 'News' section after the initial header block.
    COPY-PASTE FIX
    Rearrange the README so that the 'Overview' section, clearly stating TileRT's role as an 'ultra-low-latency LLM inference runtime' and its 'tile-based' approach, is the first substantive content after the title and badges.
  • mediumcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'TileRT vs. Other LLM Runtimes' (or similar), detailing how TileRT's tile-based approach and ultra-low-latency focus differentiate it from common alternatives like vLLM, Triton Inference Server, and 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 tile-ai/TileRT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. Triton Inference Server · recommended 1×
  3. TensorRT-LLM · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    What are the best runtimes for high-performance, low-latency large language model serving?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Triton Inference Server
    3. TensorRT-LLM
    4. DeepSpeed-MII
    5. OpenVINO
    6. ONNX Runtime

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

    Show full AI answer
  • CATEGORY QUERY
    How to achieve extreme token throughput for large language models on standard GPUs?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference)
    3. DeepSpeed-MII (Microsoft Inference Interface)
    4. TensorRT-LLM (NVIDIA)
    5. llama.cpp
    6. OpenVINO (Intel)

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

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

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

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

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tile-ai/TileRT — 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