GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
scaling-book
Good fit when
- You are working specifically with machine learning models that leverage Tensor Processing Units (TPUs) for performance and are looking to understand optimization techniques.
- If you require detailed methodologies focused on scaling LLMs using JAX on TPUs, this resource offers applicable insights.
Avoid when
- Do not use if your project focuses exclusively on GPU scaling or other hardware not aligned with Tensor Processing Units (TPUs).
- If you are looking for a general approach to any framework's scalability without emphasis on TPUs.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/jax-ml/scaling-bookSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides insights and methodologies for scaling machine learning models particularly focusing on the use of tensor processing units (TPUs).
Capability facts
- MCP server
- No MCP server detected
Source: repo_scan · Sep 19, 2026
- Languages
- html, javascript
Source: github.language+package.json · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
python bin/convert_to_single_md.pySource link
Tags
README
How To Scale Your Model This book aims to demystify the art of scaling LLMs on TPUs. We try to explain how TPUs work, how LLMs actually run at scale, and how to pick parallelism schemes during training and inference that avoid communication bottlenecks. The book is available at h...
For agents
This page has a .md twin and JSON over the API.