Efficient Inference and On-Device Models
For engineers reducing latency, memory, energy, and cost from datacenter serving to private edge deployment.
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 Model Compression.
- Understand Inference Optimization.
- Understand Small and On-Device Language Models.
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 decoder architecture basics from Volume 9.
Volume 14 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
20 chapters across 3 parts
Every linked chapter is free to read online. 5 further chapters are planned; unlinked titles are still in progress.
Part XLI: Model Compression
- 01Knowledge Distillation
Covers distillation objective, temperature in distillation, teacher selection, distillation for LLMs.
- 02Distillation Variants
Covers feature distillation, attention transfer, progressive distillation, on-policy distillation.
- 03Pruning Basics
Covers weight pruning, structured vs unstructured, pruning criteria, pruning schedule.
- 04Structured Pruning
Covers head pruning, layer pruning, width pruning, structured pruning implementation.
- 05Model Merging
Covers weight averaging, task arithmetic, TIES merging, DARE merging.
- 06Model Merging Applications
Covers multi-task merging, style merging, capability composition, merging evaluation.
Part XLII: Inference Optimization
- 01KV Cache
Covers KV cache motivation, cache structure, cache memory requirements, cache management.
- 02KV Cache Memory
Covers cache size calculation, batch size effects, sequence length effects, memory bottleneck.
- 03Paged Attention
Covers memory fragmentation problem, page-based allocation, vLLM approach, paged attention benefits.
- 04KV Cache Compression
Covers cache eviction strategies, attention sink preservation, H2O algorithm, cache quantization.
- 05Weight Quantization Basics
Covers quantization fundamentals, per-tensor vs per-channel, symmetric vs asymmetric, calibration.
- 06INT8 Quantization
Covers INT8 range mapping, absmax quantization, smooth quantization, INT8 accuracy.
- 07INT4 Quantization
Covers 4-bit challenges, group-wise quantization, 4-bit accuracy trade-offs, 4-bit formats.
- 08GPTQ
Covers GPTQ algorithm, layer-wise quantization, Hessian approximation, GPTQ implementation.
- 09AWQ
Covers salient weight preservation, AWQ algorithm, AWQ vs GPTQ, AWQ benefits.
- 10GGUF Format
Covers GGML/GGUF history, quantization types, GGUF file format, llama.cpp integration.
- 11Speculative Decoding: Fast LLM Inference Without Quality Loss
Covers speculative decoding concept, draft model selection, verification procedure, acceptance rate.
- 12Speculative Decoding Math: Algorithms & Speedup Limits
Covers acceptance criterion, expected speedup, draft quality effects, optimal draft length.
- 13Continuous Batching: Optimizing LLM Inference Throughput
Covers static vs continuous batching, iteration-level scheduling, request completion handling, throughput gains.
- 14LLM Inference Serving: Architecture, Routing & Auto-Scaling
Master LLM inference serving architecture, token-aware load balancing, and auto-scaling. Optimize time-to-first-token and throughput for production systems.
Part XLIII: Small and On-Device Language Models
- 01Small Language Model Design · In progress
Capacity allocation, data quality, architecture choices, and capability boundaries below frontier scale.
- 02Hardware-Aware Model Optimization · In progress
Memory bandwidth, kernels, quantization formats, sparsity, and co-design for phones, browsers, and edge devices.
- 03On-Device Adaptation and Personalization · In progress
Private fine-tuning, adapters, local retrieval, user control, and update strategies under tight budgets.
- 04Hybrid Edge-Cloud Inference · In progress
Routing, partitioning, privacy boundaries, offline fallbacks, latency, and cost-aware orchestration.
- 05Evaluating Small Models in Context · In progress
Task fit, energy, latency, privacy, reliability, and comparing systems rather than parameter counts alone.
Choose the edition that fits your next step
Volume 14: Efficient Inference and On-Device Models
640 pages focused on this subject, with future updates included.
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Get all-volume accessExplore the other volumes
Volume 1 · 570 pages
Language Data and Classical NLP
Text as Data · Classical Text Representations · Distributional Semantics
Volume 2 · 790 pages
Embeddings, Tokenization, and Sequence Labeling
Word Embeddings · Subword Tokenization · Sequence Labeling
Volume 3 · Coming soon
Linguistic Structure and Meaning
Morphology · Syntax · Semantics · Discourse · Dialogue
Volume 4 · 920 pages
Neural NLP and Sequence Models
Neural Network Foundations · Recurrent Neural Networks · Sequence-to-Sequence
Volume 5 · 860 pages
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Self-Attention · Positional Encoding · Transformer Blocks · Transformer Architectures
Volume 6 · 510 pages
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Efficient Attention · Long Context
Volume 7 · 760 pages
Pre-training Data and Scaling
Data Curation · Pre-training Objectives · Scaling Laws
Volume 8 · 440 pages
Encoder, Multilingual, and Translation Models
BERT and Variants · Encoder-Decoder Models
Volume 9 · 730 pages
Decoder Language Models and Capabilities
GPT Architecture · Modern Decoder Models · Emergent Capabilities
Volume 10 · 920 pages
Large-Scale Training and Sparse Models
Training Infrastructure · Training Optimization · Mixture of Experts
Volume 11 · 730 pages
Fine-tuning and Efficient Adaptation
Fine-tuning Fundamentals · Parameter-Efficient Fine-tuning · Instruction Tuning
Volume 12 · 510 pages
Alignment, Preferences, and Oversight
Alignment and RLHF
Volume 13 · 220 pages
Reasoning and Verifiable Learning
Reasoning
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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- First edition
- Published
- Last reviewed
Version history
Kept current, not frozen in time
Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.
Current release
Edition 2026.08.0
Initial volume-library release with 19 written PDFs, a Volume 3 placeholder, and no combined edition.