Bridging the Gap: How to Align GPU Supply with AI Demand

As organizations scale up their AI initiatives, GPUs have become both indispensable and expensive. Yet despite massive investments in AI infrastructure in the cloud or on prem, many enterprises face a persistent challenge: GPU supply rarely aligns perfectly with demand. The result? Critical workloads get bottlenecked while expensive hardware sits idle.

Pepperdata's GPU Demand Optimization solution changes the status quo by empowering platform owners with the visibility and control they need to balance GPU supply and demand intelligently.

The Challenge of Mismatched GPU Supply and Demand

Pepperdata has worked with dozens of companies from numerous industries who all expressed some common challenges around GPU resource management:

• GPUs are very expensive

• GPU availability to align with demand is often mismatched

• Utilization is low

Yet, they have no way of aligning the demand with the supply. Monitoring and observability tools may partially address the problem, but they often fall short. They show what is running and their detailed metrics on piece meal basis, but they don't provide a complete picture of your environment.

It's like knowing what is on each guest's plate in a restaurant, but not knowing how many different dishes are on the menu. For example, which dishes are the most popular and which ones are not being ordered at all?

Introducing Pepperdata GPU Demand Optimization

Pepperdata fills this gap by showing you a macro level view of your environment:

• How are GPUs being used in your environment over the last several days?
• Is there a usage pattern—some time slots are extremely busy while others are idle?
• Are some GPUs sitting idle most of the time?
• Are some GPUs more in demand than the availability?
• Can you shift the excessive demand of one GPU type to another?
• Do you need to procure more GPUs because demand can not be met with the availability?
• What percentage of workloads need to wait for GPUs?
• What's the max wait time?

With this granular visibility into your GPU footprint, platform owners can make better decision making for allocating resources to specific workloads, and predictively plan how to best utilize the GPUs they have at their disposal.

This enables platform managers to move from firefighting to proactive optimization.

Pepperdata GPU Demand Optimization enables enterprises to:

• Gain full visibility into GPU demand across all resources—cloud and on-prem.
  
  
• Align workloads by rescheduling or shifting jobs to underutilized GPUs.
  
  
• Spread demand across the GPU footprint to avoid bottlenecks and idle hardware.

This transforms GPU management from guesswork into strategy.

A Technical Fellow one Fortune 10 enterprise told us:

"We really value Pepperdata's GPU demand map because it solves our GPU scheduling challenges with our teams around the world.”

Close the GPU Demand Gap

The race to scale AI workloads isn’t just about adding more GPUs—it’s about using the ones you have smarter. With Pepperdata GPU Demand Optimization, enterprises can close the costly gap between GPU supply and demand, reduce waste, and ensure that mission-critical workloads run on time.

Get Started Today

Ready to get started optimizing your GPU supply and demand at scale? Pepperdata is currently working with a select group of partners. Check out pepperdata.ai to learn more and fill out this form to secure your spot on the waitlist.