RRepoGEO

REPOGEO REPORT · LITE

NVlabs/Fast-dLLM

Default branch main · commit a9b81e4c · scanned 6/29/2026, 2:38:02 AM

GitHub: 1,054 stars · 130 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 NVlabs/Fast-dLLM, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    diffusion-llm, llm-acceleration, training-free, kv-cache, parallel-decoding, vision-language-models, vlm-acceleration, autonomous-driving, block-diffusion, speculative-decoding
  • mediumreadme#2
    Add a concise problem statement and solution summary at the top of the README

    Why:

    COPY-PASTE FIX
    Fast-dLLM addresses the critical need for efficient, training-free acceleration of diffusion-based Large Language Models (dLLMs), Vision-Language Models (dVLMs), and Vision-Language-Action (VLA) models. It achieves this through novel techniques like KV Cache, parallel decoding, and block diffusion, significantly improving inference speed and efficiency without requiring extensive model retraining.

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 NVlabs/Fast-dLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/onnxruntime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/onnxruntime · recommended 2×
  2. NVIDIA TensorRT · recommended 2×
  3. microsoft/DeepSpeed · recommended 1×
  4. huggingface/optimum · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How to accelerate diffusion LLM inference without requiring extensive model retraining?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed-MII (microsoft/DeepSpeed)
    2. Hugging Face Optimum (huggingface/optimum)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. NVIDIA TensorRT
    5. torch.compile (pytorch/pytorch)
    6. bitsandbytes (TimDettmers/bitsandbytes)
    7. ONNX Runtime Quantization (microsoft/onnxruntime)
    8. NVIDIA TensorRT
    9. FlashAttention (Dao-AILab/flash-attention)
    10. xFormers (facebookresearch/xformers)
    11. OpenVINO (openvinotoolkit/openvino)

    AI recommended 11 alternatives but never named NVlabs/Fast-dLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to improve decoding speed and efficiency for large diffusion language models.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2
    2. DeepSpeed-MII
    3. vLLM
    4. TensorRT-LLM
    5. bitsandbytes
    6. OpenVINO
    7. ONNX Runtime

    AI recommended 7 alternatives but never named NVlabs/Fast-dLLM. 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 NVlabs/Fast-dLLM?
    pass
    AI named NVlabs/Fast-dLLM explicitly

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

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

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

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NVlabs/Fast-dLLM — 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