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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

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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 Engineering Serving Systems

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Continuous Batching

Why batching requests together is essential for throughput, why naive batching wastes it anyway, and how continuous batching fixes that.

10 min readUpdated 2026-09-03ai-inferencebatchingserving-systems