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Optimizing GPU Efficiency and AI Costs with Pepperdata

As AI workloads explode, platform owners face an increasingly common challenge: a massive gap between GPU demand and supply. Pending workloads, idle GPUs, and rising costs make it harder than ever to scale AI efficiently. In this video, we explore how Pepperdata.ai helps enterprises regain control of their GPU environments with two breakthrough solutions: Demand Optimization – Get granular visibility into GPU usage across your entire infrastructure. Identify inefficiencies, balance supply and demand, and uncover hidden capacity.

How Pepperdata Increases Resource Utilization for Reduced Costs on Kubernetes | Pepperdata

Without Pepperdata, the Kubernetes system scheduler only sees resource allocations—causing low utilization and inflated spend. Watch the video to learn how Pepperdata optimizes workloads running on Kubernetes. Visit pepperdata.com to learn more.

Vertical Pod Autoscaling: How It Compares to Pepperdata Capacity Optimizer

Vertical Pod Autoscaling (VPA) is a component within Kubernetes designed to automatically resize the CPU and memory requests of pods based on their observed, historical usage patterns. While Pepperdata Capacity Optimizer and VPA both change the resource requests of pods in response to changing application resource requirements, there are several key differences.