GPU Health Check Strategy – Overview

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GPU Health Check Strategy – Overview#

GPU infrastructure failures in production environments create multiple challenges. When GPU jobs fail, they waste expensive compute resources and block other workloads, resulting in high downtime costs. Performance issues often go undetected, causing silent degradation that leads to incorrect results or inefficient resource utilization. Without automated health checks, operators must manually validate GPU health, which is not scalable and introduces manual overhead. In addition, ad-hoc testing creates inconsistent validation, leaving gaps that allow undetected issues into production. Troubleshooting becomes slow and inefficient due to the absence of standardized testing, making root cause analysis time-consuming.

This reference design establishes a comprehensive, automated health check framework for AMD Instinct GPU infrastructure using the AMD GPU Operator’s AGFHC (AMD GPU Fleet Health Check) capability. The design covers the complete GPU node lifecycle from initial deployment through production operations, maintenance, and expansion.

To address these challenges, this reference design introduces a lifecycle-based health check strategy that combines automation, scalability, and resilience:

  • Prevent Bad Hardware from Entering Production through comprehensive burn-in testing for new nodes

  • Maintain Production Health with automated periodic health checks and pre-flight validation

  • Enable Self-Healing by automating detection, isolation, remediation, and validation workflows

  • Accelerate Troubleshooting using component-specific tests and standardized runbooks

  • Scale with Fleet Growth via batching, sharding, and distributed orchestration

Key Benefits#

  • Reduced GPU Downtime: Automated detection and remediation of GPU failures

  • Improved Reliability: Proactive health monitoring prevents production incidents

  • Faster Deployment: Standardized validation processes for new nodes

  • Lower MTTR: Automated troubleshooting and component-specific testing

  • Scalability: Architecture supports 1000+ node deployments

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