Efficient GPU Utilization With Workload Pre-Emption in AMD Resource Manager

2026-06-26 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

AMD Resource Manager introduces workload pre-emption, a project-level feature designed to enhance GPU utilization by automatically reclaiming resources from idle workloads. This functionality, available in AMD Resource Manager v1.1.9 and AMD AI Workbench v1.1.9, monitors GPU activity against an administrator-defined threshold (e.g., 10% GPU compute capacity) and an idle timer (e.g., 15 minutes). If a workload's activity drops below the threshold for the specified duration, it is terminated, and its GPUs are returned to the shared pool. The system offers two pre-emption policies: "During GPU pressure," which only reclaims GPUs when other workloads are queued, and "Always," which terminates idle workloads regardless of immediate demand. This feature complements existing quota-based pre-emption and priority classes, providing a comprehensive approach to managing AMD Instinct™ MI300X GPU resources. The article details configuration for new and existing projects, including a practical demonstration with an AMD Inference Microservice (AIM).

Key takeaway

For MLOps Engineers managing AMD Instinct™ GPU clusters, implementing AMD Resource Manager's workload pre-emption is crucial for optimizing resource allocation. You should enable this feature on projects with fluctuating or experimental workloads, setting a 10% GPU activity threshold and a 15-minute idle timer with an "Always" policy to ensure maximum utilization. This proactive reclamation prevents idle GPUs from being held unnecessarily, freeing up capacity for prioritized tasks and reducing operational costs without requiring changes to team workflows.

Key insights

AMD Resource Manager's workload pre-emption automatically reclaims idle GPUs based on configurable utilization thresholds and timers, improving resource efficiency.

Principles

• GPU utilization monitoring drives resource reclamation.
• Configurable policies balance demand and strict reclamation.
• Project-level settings ensure consistent resource management.

Method

Configure project-level pre-emption with a GPU activity threshold (e.g., 10%) and an idle timer (e.g., 15 minutes). Select "During GPU pressure" or "Always" policy.

In practice

• Terminate idle R&D/experimentation AIMs.
• Reclaim GPUs from unutilized JupyterLab instances.
• Apply to all development workspaces in a project.

Topics

• AMD Resource Manager
• GPU Utilization
• Workload Pre-emption
• AMD Instinct GPUs
• AI Workbench
• Resource Management

Best for: MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.