LLM ApplicationsAdvancedUpdated 2026-06-01⚡ High-frequency topics · Cohort 3

Transformer & Inference Optimization

18 lessons 9 hours video / illustrated text

Inference optimization is the hardest and most underrated direction of LLM deployment — 'why is inference so much more expensive than training' and 'how do you make inference faster' are the high-frequency capstone questions in interviews. Starting from the math of Attention, this course systematically covers KV Cache, FlashAttention, vLLM / TensorRT-LLM, and quantization — so you can explain the underlying logic of inference optimization in both interviews and daily work.

By the end of this course you will be able to:

explain Attention's computational complexity and optimization motivation at the math level
understand the internals of KV Cache / PagedAttention / Continuous Batching
master the FlashAttention v1/v2/v3 evolution and IO-complexity analysis
master the internals and engineering of GPTQ / AWQ / GGUF quantization
compare the use cases and selection of vLLM / TGI / TensorRT-LLM
standard answers and derivations for 40 high-frequency inference-optimization questions

Attention's mathematical essence dissected
KV Cache / PagedAttention internals
FlashAttention internals and the v1/v2/v3 evolution
Performance comparison: vLLM / TGI / TensorRT-LLM
Quantization techniques: GPTQ / AWQ / GGUF
inference acceleration such as Speculative Decoding

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The course is organized into four modules totaling 18 lessons.

01 Attention Mechanisms in Depth

6 lessons · 3 hours

the mathematical essence of Self-Attention30 min
the design motivation behind Multi-Head Attention28 min
the MQA / GQA / MLA evolution32 min
positional-encoding evolution: sinusoidal → RoPE → ALiBi30 min
sliding-window attention and long context28 min
Attention-module quiz: 12 high-frequency interview questions28 min

02 KV Cache and PagedAttention

4 lessons · 2 hours

KV Cache internals and memory analysis30 min
PagedAttention: the virtual-memory idea32 min
Continuous Batching internals28 min
KV Cache-module quiz: 8 high-frequency interview questions24 min

03 FlashAttention and quantization

4 lessons · 2 hours

the FlashAttention v1/v2/v3 evolution32 min
IO-complexity analysis and memory optimization28 min
quantization internals: PTQ vs QAT28 min
GPTQ / AWQ / GGUF comparison30 min

04 inference frameworks and Speculative Decoding

4 lessons · 2 hours

vLLM architecture and usage30 min
TGI / TensorRT-LLM comparison28 min
Speculative Decoding internals30 min
inference-framework quiz: 10 high-frequency interview questions28 min

🎯 Who it’s for

job seekers for big-tech inference / performance-optimization roles
engineers who want to go deep on LLM system optimization
people who are often asked 'why is it fast' in interviews and cannot answer
those who already know LLM fundamentals and want to crack the hard parts

🚀 What you’ll gain

end-to-end inference-optimization knowledge from math to engineering
the ability to make selection decisions across mainstream inference frameworks
the ability to clearly explain 'why optimize this way' in interviews
the core edge for performance-optimization roles

📋 Prerequisites

comfortable with PyTorch basics
know the Transformer architecture (ideally after Full-Stack Program Module 1)
basic high-performance-computing concepts are a plus

⏰ Pace

9 hours total; recommended pace: complete in 2 weeks
8 lessons per week + hands-on drills
Access starts immediately on enrollment; review anytime, forever

The Instructor

CAS PhD · senior algorithm engineer · sits on real hiring loops · author behind the WeChat account 「兔老板工作室」

View the full instructor profile →

It's for big-tech inference/performance-optimization job seekers, engineers deepening their LLM-system-optimization skills, and anyone who keeps being asked 'why is it fast?' in interviews without a good answer. Prerequisites: comfort with PyTorch basics and knowledge of the Transformer architecture.

Starting from the math of Attention, it covers KV Cache/PagedAttention/Continuous Batching internals, the FlashAttention v1/v2/v3 evolution and IO-complexity analysis, the vLLM/TensorRT-LLM frameworks, plus GPTQ/AWQ/GGUF quantization principles and engineering.

It's 9 hours total, ideally completed over 2 weeks at 8 lessons plus hands-on drills per week. Access opens immediately on enrollment with permanent viewing — and it equips you to explain 'why optimize this way' in interviews.

During autoregressive generation, each new token must attend to every preceding token. KV Cache stores the already-computed Keys/Values to avoid recomputation, sharply boosting inference speed; the cost is memory that grows with sequence length — exactly what techniques like PagedAttention optimize. Module 3 covers it thoroughly from the memory perspective.

The key is PagedAttention — it pages KV Cache and allocates memory on demand like an OS's virtual memory. Combined with continuous batching and prefix caching, throughput and memory utilization far exceed native frameworks. Module 4 walks you through a real deployment and comparison.

FlashAttention is IO-aware exact Attention: it tiles the computation so the full attention matrix never round-trips through HBM, cutting memory reads/writes dramatically — faster and cheaper on long sequences. Module 2 (Attention Mechanisms in Depth) derives it completely.

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Transformer & Inference Optimization

🎯 Who it’s for

🚀 What you’ll gain

📋 Prerequisites

⏰ Pace

The Instructor

Crack the hardest part of LLM inference roles