03 - Course

System Optimization
for LLMs

Controlling performance at the execution layer through kernel optimization and hardware-aware design

Read Course Contents→
System Optimization for LLMs

Focus

GPU execution
machinery

Level

Systems Engineers /
Expert

Scope

CUDA kernels,
memory, pipelines

Outcome

Control execution
at instruction level

Master the Foundations

Systems Territory

Three critical domains for GPU execution mastery.

SM UNITS

GPU Execution Units

Master SM microarchitecture, register files, and Tensor Core pipelines for maximum instruction throughput.

SCHEDULE

Kernel Scheduling

Control warp execution, synchronization patterns, and memory allocation for predictable performance.

PIPELINE

Inference Latency Pipelines

Design end-to-end execution paths from instruction dispatch to memory hierarchy optimization.

Read Course Contents→

GPU Performance Specialists

Who This Is For

Engineers Responsible for LLM Performance

Eliminate production bottlenecks by identifying GPU execution inefficiencies that cause 10x performance degradation.

GPU

GPU Programmers Moving Into AI

Translate CUDA expertise into 5x faster LLM inference through specialized kernel optimization and warp scheduling.

CLUSTER

Infra Engineers Optimizing AI Systems

Reduce infrastructure costs 60% while scaling GPU clusters to handle 100K+ concurrent inference requests.

Read Course Contents→

Perspective Transformation

Performance Shift

FROM

Tuning Models

TO

Shaping Execution

Move beyond model-level tuning to directly control GPU execution patterns for predictable performance.

FROM

Symptoms

TO

Root Causes

Trace performance issues to their execution-level origins using runtime observability and kernel analysis.

Read Course Contents→

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.

Next Course: Foundations of Deep Learning→← Back to All Programs