RRepoGEO

REPOGEO REPORT · LITE

tile-ai/TileRT

Default branch main · commit cda127e7 · scanned 5/30/2026, 12:11:49 AM

GitHub: 1,042 stars · 63 forks

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 topics for LLM inference and serving

    Why:

    COPY-PASTE FIX
    llm, inference, low-latency, gpu, runtime, deep-learning, machine-learning, ai, serving, rust, cuda, transformer
  • highreadme#2
    Add a concise, keyword-rich introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add the following text right after the second `<p>` tag containing the navigation links, and before the `______________________________________________________________________` separator: `TileRT is an ultra-low-latency, tile-based runtime specifically engineered for efficient large language model (LLM) inference on GPUs. Built for production, it delivers high token throughput and supports advanced features like Multi-Token Prediction (MTP) for accelerated LLM serving.`
  • mediumabout#3
    Expand the 'About' description with key features and benefits

    Why:

    CURRENT
    Tile-Based Runtime for Ultra-Low-Latency LLM Inference
    COPY-PASTE FIX
    TileRT is a high-performance, tile-based runtime for ultra-low-latency large language model (LLM) inference on GPUs. It enables efficient LLM serving with features like multi-token prediction (MTP) and continuous batching, designed for production environments requiring high token throughput.

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
triton-inference-server/server
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. triton-inference-server/server · recommended 1×
  2. vllm-project/vllm · recommended 1×
  3. NVIDIA/TensorRT-LLM · recommended 1×
  4. openvinotoolkit/openvino · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    How to achieve ultra-low latency for large language model inference in production?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. vLLM (vllm-project/vllm)
    3. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    4. OpenVINO (openvinotoolkit/openvino)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. DeepSpeed-MII (microsoft/DeepSpeed)
    7. Ray Serve (ray-project/ray)

    AI recommended 7 alternatives but never named tile-ai/TileRT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best runtimes for accelerating large language model serving with high token throughput?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI
    3. NVIDIA 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

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?

Embed your GEO score

Drop this badge into the README of tile-ai/TileRT. 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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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/tile-ai/TileRT"><img src="https://repogeo.com/badge/tile-ai/TileRT.svg" alt="RepoGEO" /></a>
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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