AI System Architect Learning Track
Master end-to-end system design for AI/ML infrastructure: from microarchitecture to datacenter-scale deployment
Prerequisites
- Strong computer architecture fundamentals
- Experience with CPU/GPU programming
- Familiarity with ML training and inference
- System-level performance analysis background
Learning Outcomes
- Design optimal hardware configurations for ML workloads
- Debug performance pathologies in production AI systems
- Evaluate and select processors for datacenter AI deployments
- Architect memory hierarchies for transformer models
- Optimize inference serving for latency and throughput SLOs
- Design power-efficient AI accelerator systems
- Lead cross-functional hardware-software co-design decisions
Track Modules
System & Microarchitecture Deep Dive
End-to-end reasoning about compute + data pathologies with evidence-based fixes for CPU pipelines, GPU occupancy, and memory hierarchies
Tools & Methods: Top-Down, CDRD, and Roofline
Turn counters and simple models into clear diagnoses and action items using systematic performance analysis methodologies
Modeling & Simulation
Strategic simulation methodology: choose the right simulation paradigm and fidelity level; ask targeted questions, validate against reality
Sampling & Representativeness — SimPoint, SMARTS, ROI Discipline
Cut simulation time while bounding error and preserving phase behavior through systematic sampling methodologies
ML Systems in Datacenters — LLM Inference Realities
TTFT vs tokens/s optimization, batching strategies, KV-cache memory management, PagedAttention/vLLM impact, and practical serving tactics
Multimodal Foundation Models: Architecture & System Design
Comprehensive analysis of multimodal foundation model architectures, training methodologies, and system engineering challenges for vision-language AI systems
Benchmarks & Workloads — MLPerf Essentials
What MLPerf Inference/Training measure, how to read QPS/latency/accuracy, and pragmatic usage for architecture evaluation
Tail Latency & Scale-Out — p95/p99/p99.9 Engineering
Design for tails, not means: queueing theory, amplification effects, and tail-tolerant distributed system patterns
Cluster-Level Thinking — Scheduling, Placement, Isolation
SRE and platform engineering for ML training/serving clusters: resource allocation, gang scheduling, and system-level optimization
Validation & Measurement — Trust, But Verify
Cross-validate models with real counters, quantify uncertainty, and communicate limits in performance analysis
Power & Thermal Awareness — From Activity to perf/W
Translate simulated activity into power/thermal behavior and communicate perf/W trade-offs credibly using McPAT and HotSpot
AI System Architect Learning Track
Welcome to the most comprehensive learning path for AI System Architects! This track transforms you from a competent systems engineer into a principal-level architect capable of designing and optimizing AI infrastructure at any scale.
What You'll Master
As an AI System Architect, you'll bridge the gap between cutting-edge AI research and production-ready systems. This track teaches you to think end-to-end: from transistor-level behavior to datacenter-wide resource allocation.
Core Competencies Developed
🔬 Deep System Understanding
- CPU/GPU microarchitecture optimization for AI workloads
- Memory hierarchy design for transformer models
- Interconnect and I/O subsystem performance analysis
⚡ Performance Engineering Mastery
- Top-Down methodology for systematic bottleneck identification
- Roofline modeling for compute vs. memory bound analysis
- Hardware counter-based performance debugging
🏗️ AI Infrastructure Architecture
- LLM inference serving optimization (TTFT, KV-cache, batching)
- Multimodal foundation model architectures and cross-modal attention
- Distributed training system design and scheduling
- Power and thermal-aware system configuration
📊 Validation & Measurement
- Simulation methodology and model validation
- Statistical sampling for representative workload analysis
- MLPerf benchmark interpretation and capacity planning
Learning Path Structure
This track follows a carefully designed progression from foundational understanding to advanced system design:
Phase 1: Foundation (Weeks 1-2)
System & Microarchitecture Deep Dive → Tools & Methodologies
Build your foundation in modern processor design and performance analysis methodologies. Learn to diagnose performance pathologies systematically using Top-Down analysis and Roofline modeling.
Phase 2: Modeling & Analysis (Weeks 3-4)
Modeling & Simulation → Sampling & Representativeness
Master the art of performance modeling: when to simulate vs. measure, how to ensure representative results, and how to validate models against real hardware.
Phase 3: AI System Specialization (Weeks 5-7)
ML Datacenter Systems → Multimodal Foundation Models → MLPerf Benchmarks
Deep dive into AI-specific challenges: LLM serving optimization, advanced multimodal architectures, cross-modal attention systems, and benchmark-driven evaluation.
Phase 4: Scale & Operations (Weeks 8-9)
Tail Latency & Scale-Out → Cluster-Level Thinking → Validation & Power
Learn to design systems that work at scale: tail latency engineering, cluster resource management, and power-thermal optimization.
Real-World Applications
This track prepares you for the most challenging AI system architecture decisions:
- 🏢 Enterprise: Design optimal GPU clusters for training foundation models
- ☁️ Cloud Providers: Architect multi-tenant AI inference infrastructure
- 🔬 Research: Evaluate next-generation AI accelerators and memory systems
- 📱 Edge: Optimize power-constrained AI deployment for mobile/embedded
- 🏭 Silicon: Guide processor design decisions for AI workloads
Prerequisites & Preparation
Required Background:
- 3+ years systems engineering experience
- Strong C/C++ and Python programming skills
- Computer architecture fundamentals (caches, pipelines, memory systems)
- ML model training and inference experience
- Linux system administration and performance tools
Recommended Preparation:
- Review GPU architecture basics
- Familiarize yourself with CUDA programming model
- Set up access to performance profiling tools (VTune, Nsight, perf)
Assessment & Certification
Each module includes hands-on exercises that build toward a capstone project: designing an end-to-end AI system architecture for a specific use case (LLM serving, training cluster, or edge deployment).
Module Completion Criteria:
- ✅ Complete all practical exercises
- ✅ Demonstrate understanding through worked examples
- ✅ Apply concepts to a novel scenario
Track Certification Requirements:
- 📋 Complete all 11 modules with passing scores
- 🎯 Submit capstone architecture design document
- 💬 Present design decisions and trade-offs to peer review panel
Success Metrics
Upon completion, you'll demonstrate mastery through:
- Technical Depth: Design memory hierarchies optimized for 100B+ parameter models
- Systems Thinking: Architect fault-tolerant inference serving with p99 < 100ms SLOs
- Cross-Functional Leadership: Lead hardware selection decisions backed by quantitative analysis
- Innovation Capability: Identify and prototype next-generation system optimizations
Get Started
Ready to become a principal-level AI System Architect? Begin with Module 1: System & Microarchitecture Deep Dive and transform your understanding of high-performance AI systems.
This is the most comprehensive AI systems architecture curriculum available—designed by principal engineers for principal engineers. ✨