01 - Course

System Architecture
for AI

Where AI actually runs: hardware, runtime, and infrastructure layers

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System Architecture for AI

Focus

Hardware-aware
AI systems

Level

Engineers /
Infra / ML

Scope

CPU, GPU,
memory, runtime

Outcome

Diagnose real
bottlenecks

Master the Foundations

Systems Territory

Three critical layers that determine real world AI performance.

CPU/GPU Execution

Understand how CPUs and GPUs actually behave under real workloads, beyond theoretical specifications.

L1L2L3DRAM

Memory Hierarchy

Master thread placement, cache locality, NUMA effects, and kernel scheduling for optimal performance.

APPRUNTIMEKERNELHARDWARE

Runtime Stack

Learn streaming, concurrency, device affinity, and multi-GPU execution for heterogeneous systems.

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Built for Engineers

Who This Is For

ML Engineers

Frustrated by performance bottlenecks and looking to understand where systems actually constrain your models.

Systems Engineers

Entering AI infrastructure and needing to understand how hardware, runtime, and execution layers work together.

Infra Engineers

Supporting large AI stacks and needing to optimize deployment, scaling, and resource management across systems.

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Perspective Transformation

Thinking Shift

FROM

Models

TO

System Bottlenecks

Stop optimizing models in isolation. Start understanding where systems actually constrain performance.

FROM

Cloud Abstractions

TO

Physical Execution

Move beyond abstractions. Understand the actual hardware, memory, and execution layers beneath the surface.

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GET STARTED

Ready for the next level?

Continue your learning journey with the next course in the series, or explore all training programs to find what fits your needs.

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