AI Infrastructure From Scratch

AI Infrastructure From Scratch

IntermediateTopic6 lessons~4h 50m

Lessons, in order

01 The Layers and the Build-or-Buy LadderAI infrastructure is five layers stacked on top of each other: compute, cluster, serving, data and platform. You can rent the stack at any height — a model API rents all five; bare GPUs rent … Intermediate 40 min 02 Compute and CapacityChoosing compute for inference is three questions in order. Does the model fit? — memory decides which accelerators are even candidates. How many do I need? — measured throughput at your … Intermediate 50 min 03 The ClusterA cluster turns a pile of GPU machines into one pool that many workloads share. Four jobs define it: expose each accelerator to the scheduler, place work on devices that suit it, queue and … Intermediate 50 min 04 The Serving LayerThe serving layer turns model weights into an endpoint that is fast, scalable and replaceable. It has three parts. The engine runs one model on one set of GPUs as efficiently as possible. … Intermediate 55 min 05 The Data LayerGPUs get the attention, but an AI platform stores six different kinds of data, and each has a natural home. Model artifacts go in a registry. Source documents go in object storage. Vectors … Intermediate 45 min 06 Platform and OperationsCompute, cluster, serving and data make AI infrastructure. The platform layer makes it something forty teams can use without talking to each other or to you. It provides a small set of paved … Intermediate 50 min

About this topic

This topic builds an AI platform from the bottom up: what you need, in what order, starting from an application that calls someone else’s API and ending with your own GPU cluster serving many teams. Each lesson adds one layer and says when that layer is worth owning.

#	Lesson	The question it answers
01	The Layers and the Build-or-Buy Ladder	What does an AI platform consist of, and how much of it should I run?
02	Compute and Capacity	Which accelerators, how many, and bought how?
03	The Cluster	How do GPUs become a shared, schedulable resource?
04	The Serving Layer	How do weights become a fast, scalable endpoint?
05	The Data Layer	Where do weights, documents, vectors, state and events live?
06	Platform and Operations	How do many teams share it safely, and how is it run?

The lessons stay at design level. For the mechanisms underneath — batching, KV caches, interconnects, GPU telemetry — each lesson links into Inference Engineering, GPU Engineering and Observability Engineering.