Guide to Maintaining Fault-Tolerant GPU Clusters Based on Kubernetes

To reliably handle the massive workloads of the generative AI and LLM era, a flexible orchestration layer that assumes hardware failure as a given is essential.

Key strategies for enhancing cluster stability are as follows.

• Automated Fleet Lifecycle Management: Monitors GPU temperature, power, ECC errors, and more, automatically performing Drain and Cordon on nodes when signs of failure are detected.
• Deep Observability: Monitors GPU metrics, InfiniBand fabric status, and storage throughput in real time to detect Grey Failure (degraded-performance nodes).
• Scheduler Integration: Optimizes job rescheduling through the Slinky project, which combines the scalability of Kubernetes with the precise scheduling capabilities of Slurm.
• Stateless Node Operation: Boots all nodes from Immutable images to maintain consistency and prevent Configuration Drift.
• High-Performance Networking and Security: Reduces communication bottlenecks with InfiniBand using SHARP technology, and maximizes compute performance by offloading networking and security tasks to DPUs.