Large-Scale Training and Sparse Models
For engineers training large models across accelerators and researchers studying sparse conditional computation.
Part of Language AI Handbook. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.
Become a more confident language AI practitioner.
Build the understanding to explain how language models work and make more informed choices about using them. The handbook connects the foundations to the methods behind modern language AI, with explanations you can return to as you learn.
See what you’ll learnWhat you will learn
- Understand Training Infrastructure.
- Understand Training Optimization.
- Understand Mixture of Experts.
Who this volume is for
- Engineers, researchers, students, AI enthusiasts, linguists, product managers, and anyone interested in understanding or building modern language AI systems, from foundational NLP to advanced large language models.
Prerequisites: Assumes Volumes 5, 7, and 9.
Volume 10 PDF edition
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Get all-volume access · $199Reader verdict
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out of 5
12 five-star reader reviews
Genuine feedback, with names abbreviated for privacy.
Meet the community“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“Thank you Michael. It's top!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“Thanks for putting cool stuff out into the world.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
“Thank you Michael. It's top!”
“Thanks for putting cool stuff out into the world.”
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Exact contents
29 chapters across 3 parts
Every linked chapter is free to read online.
Part XXXI: Training Infrastructure
- 01GPU Architecture
Covers GPU memory hierarchy, CUDA cores, tensor cores, GPU specifications.
- 02Memory Management
Covers memory breakdown (activations, parameters, gradients, optimizer states), memory estimation, OOM debugging.
- 03Data Parallelism
Covers DDP algorithm, gradient synchronization, all-reduce operations, DDP scaling.
- 04Tensor Parallelism
Covers column parallelism, row parallelism, communication patterns, Megatron-style parallelism.
- 05Pipeline Parallelism
Covers pipeline stages, micro-batching, pipeline bubbles, pipeline schedules (GPipe, 1F1B).
- 06ZeRO Optimization
Covers ZeRO stage 1 (optimizer state partitioning), ZeRO stage 2 (gradient partitioning), ZeRO stage 3 (parameter partitioning), ZeRO memory savings.
- 07FSDP
Covers FSDP concepts, FSDP vs ZeRO, FSDP sharding strategies, FSDP usage.
- 08Activation Checkpointing
Covers checkpointing concept, checkpoint selection, checkpointing overhead, selective checkpointing.
- 09Mixed Precision Training
Covers floating point formats, loss scaling, BF16 advantages, mixed precision implementation.
- 10Communication Optimization
Covers gradient compression, communication overlap, topology-aware communication, NCCL optimization.
- 11Checkpointing and Recovery
Covers checkpoint contents, checkpoint frequency, async checkpointing, fault recovery.
Part XXXII: Training Optimization
- 01Learning Rate Warmup
Covers warmup motivation, linear warmup, warmup duration, warmup for large batches.
- 02Learning Rate Decay
Covers step decay, exponential decay, inverse square root decay, decay scheduling.
- 03Cosine Learning Rate Schedule
Covers cosine decay formula, cosine with restarts, cosine schedule parameters, cosine vs linear.
- 04Large Batch Training
Covers batch size effects, learning rate scaling, batch size limits, LAMB optimizer.
- 05Weight Decay
Covers weight decay formula, decoupled weight decay, weight decay selection, weight decay interaction with Adam.
- 06Gradient Accumulation
Covers accumulation procedure, accumulation steps, accumulation for memory, accumulation correctness.
- 07Training Stability
Covers loss spikes, gradient norm monitoring, stability techniques, training stability debugging.
- 08Hyperparameter Selection
Covers hyperparameter search, hyperparameter transfer, critical vs robust hyperparameters, default recipes.
Part XXXIII: Mixture of Experts
- 01Sparse Models
Covers dense vs sparse trade-offs, conditional computation motivation, sparse model efficiency, sparse model challenges.
- 02Expert Networks
Covers expert architecture, expert as FFN, expert capacity, expert count selection, expert placement in transformer.
- 03Gating Networks
Covers router architecture, routing score computation, router training, router learned behavior.
- 04Top-K Routing
Covers top-1 routing, top-2 routing, k selection trade-offs, routing implementation, combining expert outputs.
- 05Load Balancing
Covers expert utilization imbalance, collapse failure mode, load metrics, balanced routing importance.
- 06Auxiliary Balancing Loss
Covers load balancing loss formulation, loss coefficient tuning, balancing vs task loss, auxiliary loss implementation.
- 07Router Z-Loss
Covers router instability, z-loss formulation, z-loss benefits, z-loss coefficient, combined auxiliary losses.
- 08Expert Parallelism
Covers expert placement strategies, all-to-all communication, communication overhead, expert parallelism implementation.
- 09Switch Transformer
Covers Switch Transformer design, top-1 routing choice, capacity factor, Switch scaling results.
- 10Mixtral
Covers Mixtral architecture, Mixtral expert design, Mixtral performance, Mixtral efficiency, Mixtral vs dense models.
Choose the edition that fits your next step
Volume 10: Large-Scale Training and Sparse Models
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Volume 1 · 570 pages
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Volume 12 · 510 pages
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Reasoning
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Model Compression · Inference Optimization
Volume 15 · 600 pages
Retrieval, Knowledge, and Memory
Retrieval-Augmented Generation · Continual Learning
Volume 16 · 510 pages
Agents and AI Coding Systems
Code Generation · Tool Use and Agents
Volume 17 · 540 pages
Speech, Multimodal, and Embodied Language
Multimodal Models · Speech and Audio
Volume 18 · 950 pages
Evaluation, Factuality, and Reliability
Evaluation Fundamentals · Benchmark Evaluation · Human and Model Evaluation · Hallucination and Factuality
Volume 19 · 760 pages
Responsible, Secure, and Interpretable Language AI
Bias and Fairness · Interpretability · Safety and Security
Volume 20 · 510 pages
Applications, Production, and Compound AI Systems
LLM Applications · Production Systems
Author and edition details
About the author and this edition
Michael Brenndoerfer
Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.
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- Last reviewed
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Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
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Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.