RRepoGEO

REPOGEO REPORT · LITE

jax-ml/scaling-book

Default branch main · commit a67b827b · scanned 6/22/2026, 11:03:11 PM

GitHub: 1,176 stars · 168 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 jax-ml/scaling-book, 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
    Reposition README opening to clarify its role as an educational guide

    Why:

    CURRENT
    # How To Scale Your Model

This book aims to demystify the art of scaling LLMs on TPUs.
    COPY-PASTE FIX
    # How To Scale Your Model: An Educational Guide to LLM Scaling on TPUs

This online textbook and practical guide aims to demystify the art of scaling LLMs on TPUs, focusing on principles and techniques rather than specific frameworks.
  • mediumtopics#2
    Add topics emphasizing the repo's educational format

    Why:

    CURRENT
    jax, llm-inference, llms, roofline, tpus
    COPY-PASTE FIX
    jax, llm-inference, llms, roofline, tpus, ml-education, deep-learning-guide, scaling-principles
  • lowabout#3
    Refine the repository description for clearer educational positioning

    Why:

    CURRENT
    Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
    COPY-PASTE FIX
    An online textbook and practical guide for engineers and researchers on scaling Large Language Models (LLMs) efficiently on TPUs, covering parallelism schemes and communication bottlenecks.

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 jax-ml/scaling-book
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Megatron-LM · recommended 2×
  3. Colossal-AI · recommended 2×
  4. FairScale · recommended 1×
  5. PyTorch FSDP · recommended 1×
  • CATEGORY QUERY
    How to scale large language models efficiently on specialized AI accelerators, avoiding communication bottlenecks?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Megatron-LM
    3. FairScale
    4. PyTorch FSDP
    5. Colossal-AI
    6. XLA
    7. JAX
    8. TensorFlow
    9. NCCL
    10. MPI

    AI recommended 10 alternatives but never named jax-ml/scaling-book. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective parallelism schemes for optimizing massive deep learning models across many compute devices?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed
    2. DeepSpeed
    3. Megatron-LM
    4. TensorFlow Distributed Strategy API
    5. Ray
    6. Ray Train
    7. Colossal-AI

    AI recommended 7 alternatives but never named jax-ml/scaling-book. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 jax-ml/scaling-book?
    pass
    AI named jax-ml/scaling-book explicitly

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

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

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

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jax-ml/scaling-book — 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