RRepoGEO

REPOGEO REPORT · LITE

tonbistudio/turboquant-pytorch

Default branch master · commit 99971388 · scanned 6/28/2026, 1:43:28 PM

GitHub: 1,021 stars · 139 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 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 relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    pytorch, llm, quantization, kv-cache, compression, deep-learning, machine-learning, google-turboquant
  • highreadme#2
    Strengthen README H1 and opening sentence for problem/solution clarity

    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

Drastically reduce LLM memory footprint and boost inference speed by compressing key-value caches with our PyTorch implementation of Google's TurboQuant.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Insert URL to project page, documentation, or demo here]

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
Hugging Face Optimum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Optimum · recommended 2×
  2. bitsandbytes · recommended 2×
  3. FlashAttention-2 · recommended 1×
  4. PagedAttention · recommended 1×
  5. vLLM · recommended 1×
  • CATEGORY QUERY
    How to compress large language model key-value caches in PyTorch for better inference?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. PagedAttention
    3. vLLM
    4. DeepSpeed-MII
    5. DeepSpeed-FastGen
    6. Hugging Face Optimum
    7. ONNX Runtime
    8. Intel OpenVINO
    9. TensorRT-LLM
    10. LMDeploy
    11. bitsandbytes
    12. AWQ
    13. GPTQ

    AI recommended 13 alternatives but never named tonbistudio/turboquant-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for PyTorch libraries to quantize LLM key-value caches to reduce memory usage.
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. Hugging Face Optimum
    3. PyTorch native quantization
    4. NVIDIA TensorRT
    5. Intel Neural Compressor

    AI recommended 5 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 named tonbistudio/turboquant-pytorch explicitly

    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
  • Prioritized action items8 vs 3 in Lite