Lilly Tech Systems
On-Premise AI Infrastructure
Design, build, and operate on-premise AI infrastructure for enterprise machine learning and LLM workloads with GPU clusters, networking, and orchestration.
6
Lessons
✍
Real-World Examples
🕑
Self-Paced
100%
Free
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
Beginner
1. Introduction
Why organizations build on-premise AI compute and what it takes to succeed.
Start here →
Intermediate
2. Hardware Planning
Select GPUs, servers, and compute hardware for training and inference clusters.
Lesson 2 →
Intermediate
3. Network Architecture
Design high-performance networking with InfiniBand, RoCE, and GPU interconnects.
Lesson 3 →
Advanced
4. Storage Architecture
Design storage systems for AI workloads including parallel file systems and data pipelines.
Lesson 4 →
Advanced
5. Container Orchestration
Deploy and manage AI workloads with Kubernetes, GPU scheduling, and monitoring.
Lesson 5 →
Advanced
6. Best Practices
Operational best practices for capacity planning, security, and team processes.
Lesson 6 →