A CURATED READING LIST
If your goal is to start learning AI/LLM inference engineering rather than general ML, check this out. They build a useful progression: how generation works → why inference is slow → memory/KV cache → serving → production optimization.
Read at your own pace. Save this list to keep track of your progress.
1·outcomeschool.com
Why I added thisBefore getting deep into Inference, It is important to understand the Transformer architecure basics. This article explains the architecture, how the data flows in simple words. For a newbie, this is a good soft starting point.
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A curated collection of books, papers, courses, and hands-on resources for learning distributed systems, drawn from a Hacker News discussion. Includes foundational papers, practical tutorials, and recommended deep dives.