As organizations rapidly scale AI workloads in Kubernetes, the ultimate hurdle isn't just running them, it's optimizing expensive GPU resources continuously.

IBM Turbonomic solves this by translating Prometheus performance telemetry into automated resourcing decisions. With the release of IBM Turbonomic 8.19.6, a new built-in onboarding wizard shrinks this initial setup from weeks to minutes, letting teams operationalize GPU optimization instantly.

The Challenge We're Solving: Configuration Silos That Delay Value

IBM Turbonomic’s container GPU optimization engine relies on a steady flow of Prometheus metrics to analyze workload demand. Historically, establishing this data pipeline required disjointed, manual coordination across platform, observability, and AI engineering teams to manage complex infrastructure configurations. Because this process was fragmented across distinct silos, onboarding often took days or weeks, delaying critical infrastructure insights.

Prometheus Integration: Streamlined Observability, Simplified

The onboarding experience simplifies Prometheus integration into a guided flow under Settings → Target Configuration in the Turbonomic UI.

Users are guided through securely connecting to an existing Prometheus server using tokens, service accounts, or preconfigured secrets, removing the need for manual configuration.

During the same setup, Prometheus query configurations are defined to ensure the right metrics are available for Turbonomic analysis. These mappings associate Prometheus queries with Turbonomic’s resource model, helping translate GPU and GPU memory telemetry, along with LLM inferencing workload performance signals such as response time and throughput, into actionable insights that directly inform how workloads should be resourced and optimized.

Once deployed, the Prometurbo components connect to Prometheus, and Turbonomic begins ingesting and normalizing this telemetry for continuous analysis.

From Metrics to Action: Driving GPU Optimization

Prometheus provides visibility into GPU utilization and LLM inferencing performance, but turning that visibility into the right resource decisions is where complexity often remains.

Turbonomic addresses this by continuously evaluating whether available GPU resources align with the performance needs of LLM workloads. Instead of relying on dashboards or manually defined thresholds, it analyzes incoming telemetry to identify where GPU resource allocation may not align with the performance needs of LLM workloads.

This gives teams clear, actionable guidance on how LLM workloads should scale horizontally to meet performance demands while improving overall GPU utilization efficiency.

Measurable Business & Operational Impact

• 500% Faster Onboarding: Eliminates cross-team handoffs and slashes target setup down to minutes, accelerating your time-to-value.
• Lower Engineering Overhead: Abstracting manual setup into a self-service wizard removes multi-team dependencies and drastically reduces internal support tickets.
• Maximized GPU ROI: Instantly identifies underutilized, high-cost hardware, allowing leadership to safely increase workload density and reinvest infrastructure savings.

Ready to Supercharge Your GPU Efficiency?

What used to require weeks of cross-team coordination now takes mere minutes. With Turbonomic 8.19.6, your organization can move instantly from basic observability to continuous, automated GPU optimization at scale. 

• Deep Dive into the Details: Review the full documentation and configuration prerequisites here.
• See It in Action: Experience automated, demand-driven AI scaling firsthand. Sign-Up for a Free Turbonomic Trial.
• We Want to Hear From You: Already testing out the new onboarding wizard? Help shape the future of Turbonomic automation by submitting your ideas. Submit Idea