Part 1: Managing Large-Scale LLM Training with AWS ParallelCluster

This article shares operational insights from managing large-scale LLM training environments using AWS ParallelCluster for Korea's National AI projects, supporting 30 p5en.48xlarge instances across major research institutions.

• Configure ParallelCluster with fixed MinCount/MaxCount for stable GPU resource availability in research environments
• Enable EFA (Elastic Fabric Adapter) for lower latency and higher bandwidth in distributed deep learning training
• Implement Slurm Partitions (debug, normal, priority, exclusive) with tiered priority levels and time limits
• Use Slurm Accounting with QoS policies to enforce per-user resource limits and prevent monopolization
• Set memory-based scheduling and capacity reservations to ensure consistent resource allocation
• Define standard resource ratios (16 CPUs and 200GB memory per GPU) for predictable job placement

Combining ParallelCluster configuration best practices with Slurm Partitions and Accounting creates a fair, stable multi-user GPU cluster environment for large-scale AI research workloads.