Tuning FSx for Lustre to Maximize Machine Learning I/O Efficiency

Running ML workloads at scale is not just about having enough compute—it’s also about feeding your GPUs or CPUs fast enough data to avoid idle cycles. Poor I/O throughput can quietly degrade training performance, lead to inefficient resource usage, or even cause job failures in distributed training scenarios. Amazon FSx for Lustre provides the raw power for high-throughput storage, but tuning it effectively within EKS is essential to fully unlock its capabilities.