RRepoGEO

REPOGEO REPORT · LITE

IST-DASLab/marlin

Default branch master · commit 1f25790b · scanned 5/11/2026, 9:53:15 AM

GitHub: 1,071 stars · 88 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 IST-DASLab/marlin, 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
    Strengthen README's opening to highlight unique medium-batch advantage

    Why:

    CURRENT
    This is Marlin, a **M**ixed **A**utoR**egressive **Lin**ear kernel (and the name of one of the planet's fastest fish), an extremely optimized FP16xINT4 matmul kernel aimed at LLM inference that can deliver close to ideal (4x) speedups up to batchsizes of 16-32 tokens (in contrast to the 1-2 tokens of prior work with comparable speedup).
    COPY-PASTE FIX
    Marlin is an extremely optimized FP16xINT4 matmul kernel for LLM inference, uniquely designed to deliver near-ideal (4x) speedups for *medium batch sizes* of 16-32 tokens. Unlike prior work limited to 1-2 tokens, Marlin excels in scenarios like larger-scale serving, speculative decoding, or advanced multi-inference schemes.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://ist-daslab.github.io/marlin/
  • lowcomparison#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, under a heading like 'Marlin vs. Other Quantization Kernels' or 'Why Marlin for Medium Batches?', detailing how Marlin's approach and performance for batch sizes 16-32 tokens differentiate it from alternatives such as `bitsandbytes`, `vLLM`, `GPTQ-for-LLaMa`, `ExLlamaV2`, 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 IST-DASLab/marlin
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vllm-project/vllm
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vllm-project/vllm · recommended 2×
  2. TimDettmers/bitsandbytes · recommended 2×
  3. NVIDIA/TensorRT-LLM · recommended 2×
  4. huggingface/transformers · recommended 1×
  5. ggerganov/llama.cpp · recommended 1×
  • CATEGORY QUERY
    How to accelerate LLM inference with 4-bit quantization for medium batch sizes?
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. Hugging Face `transformers` (huggingface/transformers)
    3. `bitsandbytes` (TimDettmers/bitsandbytes)
    4. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    5. llama.cpp (ggerganov/llama.cpp)
    6. DeepSpeed-MII (microsoft/DeepSpeed)

    AI recommended 6 alternatives but never named IST-DASLab/marlin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an optimized kernel for 4-bit quantized LLM inference to maximize GPU utilization.
    you: not recommended
    AI recommended (in order):
    1. GPTQ-for-LLaMa (qwopq/GPTQ-for-LLaMa)
    2. ExLlamaV2 (turboderp/exllamav2)
    3. bitsandbytes (TimDettmers/bitsandbytes)
    4. AWQ (mit-han-lab/awq)
    5. vLLM (vllm-project/vllm)
    6. TensorRT-LLM (NVIDIA/TensorRT-LLM)

    AI recommended 6 alternatives but never named IST-DASLab/marlin. 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 IST-DASLab/marlin?
    pass
    AI named IST-DASLab/marlin explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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