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Data Structures & AlgorithmsComplexity analysis up to graphs and DP.Operating SystemsProcesses, scheduling, memory and concurrency.Database Management SystemsRelational modelling, transactions and indexing.Computer NetworksFrom wires and radio waves up to the web.

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AI Inference EngineeringQuantization, batching and KV caching in production.System DesignScalable systems from first principles.Context EngineeringDesigning the context that LLMs actually reason over.Docker & KubernetesContainers and orchestration, explained.

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AI Inference Engineering

Model Optimization3

Serving Systems4

Continuous Batching
PagedAttention and KV Cache Memory Management
Disaggregated Prefill/Decode Serving
Serving Frameworks and Observability

Hardware & Compilation3

Advanced Serving Techniques4

Benchmarking & Evaluation5

Scaling & Deployment3

Lessons

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AI Inference Engineering

Model Optimization3

Serving Systems4

Continuous Batching
PagedAttention and KV Cache Memory Management
Disaggregated Prefill/Decode Serving
Serving Frameworks and Observability

Hardware & Compilation3

Advanced Serving Techniques4

Benchmarking & Evaluation5

Scaling & Deployment3

Learn AI Inference EngineeringServing Systems

Serving Systems

4 lessons

1
Continuous Batching
Why batching requests together is essential for throughput, why naive batching wastes it anyway, and how continuous batching fixes that.
10 min read
2
PagedAttention and KV Cache Memory Management
Borrowing virtual memory paging from operating systems to stop the KV cache from wasting most of a GPU's memory.
10 min read
3
Disaggregated Prefill/Decode Serving
Why the two halves of serving a request fight over the same GPU, and what happens when you give them separate hardware entirely.
9 min read
4
Serving Frameworks and Observability
What an inference server actually does end to end, the frameworks that implement it, and the metrics that tell you whether it's healthy.
10 min read

Serving Systems Quiz6 questions · pass mark 60%