RRepoGEO

REPOGEO REPORT · LITE

meta-pytorch/gpt-fast

Default branch main · commit 6ecad9b5 · scanned 5/20/2026, 12:27:37 AM

GitHub: 6,210 stars · 572 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)

2 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 meta-pytorch/gpt-fast, 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, transformer, inference, quantization, speculative-decoding, gpu-acceleration, native-pytorch, performance-demonstration
  • highreadme#2
    Reposition the README H1 to emphasize 'reference implementation'

    Why:

    CURRENT
    # gpt-fast Simple and efficient pytorch-native transformer text generation.
    COPY-PASTE FIX
    # gpt-fast: A minimal, high-performance PyTorch-native reference implementation for transformer text generation.
  • mediumhomepage#3
    Add the blog post URL as the repository homepage

    Why:

    COPY-PASTE FIX
    [URL of the blog post mentioned in the README]

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 meta-pytorch/gpt-fast
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vLLM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. vLLM · recommended 2×
  2. TorchScript · recommended 1×
  3. torch.compile · recommended 1×
  4. FlashAttention · recommended 1×
  5. PyTorch Quantization · recommended 1×
  • CATEGORY QUERY
    How to achieve very low latency transformer text generation using native PyTorch for inference?
    you: not recommended
    AI recommended (in order):
    1. TorchScript
    2. torch.compile
    3. FlashAttention
    4. PyTorch Quantization
    5. vLLM
    6. torch.utils.cpp_extension

    AI recommended 6 alternatives but never named meta-pytorch/gpt-fast. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking efficient PyTorch solutions for deploying large language models with quantization and multi-GPU support.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. bitsandbytes
    4. vLLM
    5. DeepSpeed
    6. NVIDIA TensorRT-LLM
    7. OpenVINO
    8. PyTorch FSDP

    AI recommended 8 alternatives but never named meta-pytorch/gpt-fast. 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 meta-pytorch/gpt-fast?
    pass
    AI named meta-pytorch/gpt-fast explicitly

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

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

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

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meta-pytorch/gpt-fast — 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