Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 17 — Future Threats and Emerging Defenses
Seventeenth post of the LLM Primer VII walkthrough — and the series finale. Agent risks and the lethal trifecta, multimodal attack surfaces, deepfakes and C2PA provenance, plus a closing map of the whole LLM Primer arc and the Physical AI sister volume.
2026-05-26
Chapter 14 — Token Economics and API Pricing
Fourteenth post of the LLM Primer VI walkthrough. The input-vs-output token asymmetry, the hidden cost of conversation history, and the invisible reasoning tokens that quietly rewrite the daily bill.
2026-05-06
Chapter 12 — Disaggregated Serving and Kubernetes
Twelfth post of the LLM Primer VI walkthrough. Why aggregating prefill and decode wastes compute, and how LeaderWorkerSet, NVIDIA Grove, and KAI Scheduler split them apart on Kubernetes.
2026-05-04
Chapter 9 — Speculative Decoding
Ninth post of the LLM Primer VI walkthrough. The draft-verify paradigm — EAGLE, Medusa, MTP, Lookahead, N-gram — and the verification bottleneck that decides real speedup.
2026-05-01
Chapter 7 — Advanced Batching Strategies
Seventh post of the LLM Primer VI walkthrough. Static vs dynamic vs continuous (in-flight) batching, iteration-level scheduling, and how a batch's slots actually progress on the GPU.
2026-04-29
Chapter 6 — Pruning and Knowledge Distillation
Sixth post of the LLM Primer VI walkthrough. Structured vs unstructured pruning, 2:4 sparsity on Hopper, and the distillation lineage from soft probabilities to Patient Knowledge Distillation and MiniLLM.
2026-04-28
Chapter 5 — Demystifying Quantization
Fifth post of the LLM Primer VI walkthrough. From BF16 to INT4 to Blackwell FP4 — quantization algorithms (AWQ, GPTQ, GGUF, SmoothQuant), NVIDIA ModelOpt, and when quantization is safe versus lossy.
2026-04-27
Chapter 4 — Specialized AI Silicon and ASICs
Fourth post of the LLM Primer VI walkthrough. Groq LPUs, AWS Inferentia2, Google TPUs, and Intel Gaudi — where specialized silicon fits alongside general-purpose GPUs.
2026-04-26
Chapter 3 — Data Center GPUs for Generative AI
Third post of the LLM Primer VI walkthrough. The NVIDIA lineup (H100, H200, B200, L40S) vs AMD MI300X — and why HBM bandwidth matters more than FLOPs for decoding.
2026-04-25
Chapter 1 — The Mechanics of Token Generation
First post of the LLM Primer VI walkthrough. The autoregressive bottleneck, the prefill/decode split, and why a high-end GPU is 99.7% idle while serving a single user.
2026-04-23
LLM Primer VI — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book VI in the LLM Primer series — Scaling AI Systems. Why inference is the discipline that decides whether an LLM app survives real users, and the schedule for the sixteen posts that follow, April 23 through May 8.
2026-04-22
Chapter 8 — Optimizing Performance, Serving, and Cost
Eighth and final post of the LLM Primer V walkthrough. Semantic caching, dynamic model routing, and what actually happens inside the inference server — plus a look ahead to Volume VI on scaling.
2026-04-21
Chapter 7 — Efficiency and Transformer Variants
Seventh post of the LLM Primer II walkthrough. The computational complexity of attention, the GPU memory and throughput math that constrains real systems, FlashAttention derived from first principles, and the family of clever variants — multi-query, gated, low-rank — that keep big models running.
2026-03-09
Chapter 9 — Performance, Scaling, and Costs: The Real Engineering Trade-offs
Chapter 9 of the LLM Primer I series. The operational realities of running LLMs at scale — model size vs capability, the latency–throughput trade-off, cost economics, quantization, and edge deployment. Why frontier-tier models are often the wrong choice even when you can afford them.
2026-02-26
Chapter 5 — Training Large Models: What Actually Goes Into a Frontier Model
Chapter 5 of the LLM Primer I series. How frontier LLMs are actually trained — the data pipeline, the loss function, the months of GPU time, and why "training" is now an industrial-scale engineering problem more than a research problem. Demystifies what those hundred-million-dollar training runs are paying for.
2026-02-22