A Practical Deep Dive on LLM Inference and Optimization!

...covered with fundamentals, bottlenecks, and techniques!

After covering LLM fine-tuning techniques in the full LLMOps course, we now move to LLM inference and optimization.

Read Part 13 of the full LLMOps course here →

It covers how LLM inference actually works under the hood, the prefill and decode phases, KV caching and its optimizations like PagedAttention and prefix caching, attention-level optimizations like FlashAttention and GQA, speculative decoding, model parallelism strategies, and hands-on experiments comparing vLLM with standard inference.

Read Part 13 of the full LLMOps course here →

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Why care?

Fine-tuning a model is only half the picture. If you cannot serve it efficiently, none of that effort translates to a usable product.

In production, inference costs often dwarf training costs. A model that takes too long to respond loses users. A model that cannot handle concurrent requests wastes GPU capacity. And a model that runs out of memory on long contexts breaks down entirely.

LLM inference optimization is what bridges the gap between a model that works in a notebook and a model that works at scale. Techniques like KV caching, PagedAttention, continuous batching, and speculative decoding are not optional extras. They are what make real-time LLM applications possible.

This chapter gives you a precise mental model of how inference works and the practical toolkit to make it faster, cheaper, and more scalable.

• Read Part 1 on fundamentals of LLMOps here →

• Read Part 2 on understanding the core building blocks of LLMs →

• Read Part 3 on the key components of LLMs, focusing on the attention mechanism, architectures like transformers and mixture-of-experts, and the fundamentals of pretraining and fine-tuning →

• Read Part 4 on decoding strategies, generation parameters, best practices, and the broader lifecycle of LLM-based applications →

• Read Part 5 on context + prompt engineering from a system perspective, in-context learning, types of prompts, and different prompting techniques →

• Read Part 6 on prompt versioning, defensive prompting, and techniques like verbalized sampling, role prompting, and more →

• Read Part 7 on context engineering, covering context types, context construction principles, and retrieval-centric techniques for building high-signal inputs →

• Read Part 8 on memory, dynamic, and temporal context in LLM systems, covering short and long-term memory, dynamic context injection, and common context failure modes in agentic applications →

• Read Part 9 on evaluation methods and approaches for LLM-based applications, primarily focusing on building a strong understanding of the fundamental concepts →

• Read Part 10 on evaluation benchmarks in LLM applications, with task-specific methodologies, and the core tooling for evaluation of LLM apps →

• Read Part 11 on evaluation of multi-turn systems, tool use evaluations, tracing, and red teaming →

• Read Part 12 on LLM fine-tuning, parameter-efficient methods like LoRA and QLoRA, and alignment techniques such as RLHF, DPO, and GRPO →

Over to you: What would you like to learn in the LLMOps course?

Thanks for reading!