RRepoGEO

REPOGEO REPORT · LITE

tonbistudio/turboquant-pytorch

Default branch master · commit 99971388 · scanned 5/29/2026, 1:38:03 PM

GitHub: 1,001 stars · 137 forks

AI VISIBILITY SCORE
28 /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
2 / 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 tonbistudio/turboquant-pytorch, 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 the repository

    Why:

    COPY-PASTE FIX
    pytorch, llm, quantization, kv-cache, compression, deep-learning, machine-learning, inference-optimization, turboquant
  • highreadme#2
    Reposition README H1 and first sentence to emphasize category and benefit

    Why:

    CURRENT
    # TurboQuant
A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's vector quantization algorithm for compressing LLM key-value caches.
    COPY-PASTE FIX
    # TurboQuant: PyTorch for LLM KV Cache Compression
A from-scratch PyTorch implementation of Google's TurboQuant (ICLR 2026) for LLM key-value cache compression, achieving up to 5x compression at 3-bit with 99.5% attention fidelity.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://tonbistudio.com/turboquant

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 tonbistudio/turboquant-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/optimum
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/optimum · recommended 1×
  2. microsoft/onnxruntime · recommended 1×
  3. openvinotoolkit/openvino · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. vllm-project/vllm · recommended 1×
  • CATEGORY QUERY
    Looking for a PyTorch library to compress LLM key-value caches efficiently for inference.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum (huggingface/optimum)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Intel OpenVINO (openvinotoolkit/openvino)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. vLLM (vllm-project/vllm)
    6. FlashAttention-2 (Dao-AILab/flash-attention)
    7. xFormers (facebookresearch/xformers)
    8. bitsandbytes (TimDettmers/bitsandbytes)
    9. PyTorch 2.0 (pytorch/pytorch)

    AI recommended 9 alternatives but never named tonbistudio/turboquant-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for achieving high compression of LLM KV caches with minimal fidelity loss?
    you: not recommended
    AI recommended (in order):
    1. GPTQ
    2. AWQ
    3. SmoothQuant
    4. QLoRA
    5. DeepSpeed-MII
    6. vLLM
    7. Google's Lookahead Decoding
    8. Medusa
    9. StreamingLLM
    10. H2O
    11. LRU
    12. LFU
    13. Hugging Face Transformers

    AI recommended 13 alternatives but never named tonbistudio/turboquant-pytorch. 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 tonbistudio/turboquant-pytorch?
    pass
    AI did not name tonbistudio/turboquant-pytorch — likely talking about a different project

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

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

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

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tonbistudio/turboquant-pytorch — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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