GPUs are expensive—maximize their value with Hopsworks on Kubernetes. As AI adoption grows, enterprises are increasingly managing their own GPU clusters for cost, security, and compliance reasons. However, inefficient GPU utilization leads to wasted resources, delays, and inflated costs. This webinar will explore how to optimize GPU usage at scale with Hopsworks, that integrates training (Ray, PyTorch), data preparation (Spark, Flink, Pandas), and model serving (KServe/vLLM) while enabling intelligent GPU sharing across teams and projects.

• Prioritize Critical AI Workloads – Plan GPU tasks efficiently to ensure high-priority jobs run first.
• Boost Productivity Across Teams – Share GPUs dynamically while maintaining performance and fair resource distribution.
• Accelerate AI from Training to Deployment – Ensure faster predictions with optimized scheduling and utilization.
• Full Cost Visibility & Governance – Allocate GPU budgets, charge usage to business units, and manage quotas effortlessly.

As the only end-to-end AI platform designed for enterprises, Hopsworks provides a fully integrated infrastructure for running AI at scale. It runs natively on Kubernetes, offering GPU virtualization through sharding, advanced scheduling (priority scheduling, pre-emption, gang scheduling), and controlled resource allocation. We will also explore how to track GPU usage by business units, manage exclusive team access, and optimize resource efficiency for maximum performance and cost savings.