In the field of AI infrastructure optimization, Pepperdata has introduced a new solution that helps organizations reduce GPU costs. With a single-minded focus on AI infrastructure optimization, Pepperdata is addressing the biggest challenge that most enterprises face. The problem is that GPUs are not used to their full capacity. Besides that, the issue of optimization is becoming increasingly important as AI workloads are increasing exponentially.
Harnessing GPU Efficiency
Pepperdata’s solution enables GPU cost savings of up to 30%. It does this by matching GPU supply with actual demand across the AI infrastructure footprint. The tool optimizes tasks such as real-time inference, batch inference, and Jupyter notebook workloads. As a result, users can boost throughput and reduce waste.
Dual Optimization Strategies for AI Workloads
Furthermore, the offering includes two main components: “GPU Demand Optimization” and “GPU Resource Optimization.” The first identifies mismatches between supply and demand, enabling strategic shifting of demand. The second uses NVIDIA’s Multi-Instance GPU (MIG) feature to partition GPUs into independent pools. Consequently, workloads can run more efficiently on-premises or in the cloud.
In addition, Pepperdata emphasizes its experience in Kubernetes resource optimization. It also previously helped enterprises save hundreds of millions in infrastructure costs. The company invites organizations to engage with the solution at the upcoming KubeCon event in Atlanta.
By adopting such AI infrastructure optimization tools, enterprises can improve GPU utilization, reduce overhead, and scale their AI workloads with more confidence and lower cost.
Explore IT Tech News for the latest advancements in Information Technology & insightful updates from industry experts !
News Source: Businesswire.com