Optimizing GPU ROI: Inference by day, training by night

Enterprise graphics processing unit (GPU) infrastructure represents a significant investment, yet industry benchmarks show average utilization rates hovering at mid to low percentages. Many organizations operate at just 15% efficiency, effectively paying five times more per compute unit than necessary. Despite their high costs, GPUs frequently sit idle due to rigid departmental ownership, lack of orchestration and infrastructure built for peak demand rather than continuous use.

A fundamental shift in workload management can dramatically improve this inefficiency. Artificial intelligence (AI) workloads naturally fall into two distinct categories: inference and training. Inference runs during business hours, responding to real-time user demands with low-latency requirements. Training, on the other hand, is compute-intensive but can tolerate delays, interruptions and batch processing—making it the perfect candidate for off-hour execution.

By aligning GPU workloads with these natural rhythms—inference by day, training by night—organizations can push utilization rates beyond 60-85%, significantly improving their return on investment (ROI). Implementing this strategy requires sophisticated orchestration, effective memory management and time-based workload scheduling, but the rewards are undeniable: better efficiency, lower costs, and greater AI innovation without additional hardware investment.

The hidden cost of underutilized GPUs

For most enterprises, GPU inefficiency isn’t just a technical issue—it’s a financial liability. Enterprise-grade GPUs, which range from $5,000 to $40,000 per unit, are often deployed for a single function, leaving massive gaps in their usage.

Beyond hardware costs, underutilized GPUs continue consuming power, cooling and maintenance resources regardless of usage levels. GPUs also depreciate rapidly over three to five years, yet many businesses extract only a fraction of their potential computational value. When factoring in networking, storage, software and operational support, the total cost of ownership can reach two to three times the hardware cost alone.

This inefficiency also creates organizational bottlenecks. Teams without dedicated GPU access may delay or abandon AI projects, while isolated GPU deployments force redundant infrastructure and inconsistent management practices. As a result, businesses face not only financial waste but also missed opportunities for AI-driven innovation.

The power of complementary AI workloads

While GPU underutilization is a major challenge, the solution is already built into AI’s natural workload patterns.

Inference workloads are characterized by their need for low-latency performance and steady availability during business hours. They typically require less GPU memory but must scale efficiently to meet fluctuating user demands. Conversely, training workloads are highly compute-intensive but lack real-time constraints, making them ideal for execution during off-hours.

This natural complementarity allows businesses to schedule training workloads at night when inference demands decline. Instead of allowing GPUs to sit idle, they can be fully utilized for model training, retraining and batch processing. By optimizing workload timing, enterprises can maximize GPU efficiency without disrupting critical real-time operations.

Implementing the day/night strategy

A structured approach to GPU orchestration can unlock the full potential of AI infrastructure. The first step is leveraging an AI workload orchestration platform, such as Red Hat OpenShift AI, to dynamically allocate GPU resources based on real-time demand. Kubernetes-based orchestration enables businesses to enforce time-based policies, so inference jobs take priority during business hours and transition to training workloads overnight.

Geographic distribution provides another layer of optimization. Global organizations can schedule workloads across time zones, enabling continuous GPU utilization. When one region’s business day ends, another begins, allowing AI workloads to shift dynamically between locations without downtime.

Weekly and seasonal trends further enhance optimization. Many businesses experience lower inference demands on weekends, creating 48-hour windows for intensive training jobs. Similarly, seasonal variations in AI usage offer predictable opportunities for resource reallocation. With the right orchestration tools, enterprises can adjust dynamically to these fluctuations, so GPUs are always working at peak efficiency.

The ROI of smarter GPU orchestration

Adopting a day/night strategy isn’t just about squeezing more out of existing infrastructure—it’s about transforming GPU deployment into a strategic advantage. Organizations that optimize workload scheduling see substantial cost savings, reduced operational waste and a greater ability to scale AI initiatives without additional hardware investment.

Beyond the financial impact, smarter GPU orchestration improves overall AI agility. Teams gain access to shared, high-performance resources rather than being constrained by rigid departmental ownership. AI projects that were previously delayed due to limited access to compute power can move forward, accelerating innovation across the organization.

By making GPU infrastructure highly utilized around the clock, businesses can shift from a fragmented approach to AI to a streamlined, cost-effective and scalable system. The key lies in aligning workloads with natural usage cycles, leveraging enterprise-grade orchestration and continuously refining scheduling strategies based on real-world usage patterns.

Turning idle GPUs into an AI powerhouse

It’s time to rethink GPU utilization. With smarter scheduling and the right tools, enterprises can finally achieve the full potential of their AI infrastructure—and maximize the return on their investment. 

Learn more with the interactive experience, How Red Hat can help with AI adoption and by visiting the Red Hat OpenShift AI webpage.