Pepperdata, a leader in Kubernetes resource optimization in the cloud and on prem, is debuting the general availability of pepperdata.ai, an automated optimization solution to reduce the cost of running AI workloads on GPUs.
"As enterprises scale up GPU infrastructure to support AI initiatives, they are discovering a painful truth: Most GPUs are underutilized. Expensive hardware sits idle or fragmented, while operators struggle to balance performance, cost, and access," said Ash Munshi, CEO of Pepperdata. "Pepperdata intelligently allocates and manages GPU resources, ensuring that critical AI workloads, including Real-Time Inference, Batch Inference, and Jupyter Notebooks, receive the necessary computing power while eliminating waste and delivering substantial savings and faster time-to-insight."
Pepperdata is offering two powerful and complementary solutions for GPU resource optimization. GPU Demand Optimization empowers platform owners to:
- Identify mismatches between GPU supply and demand
- Shift demand strategically by time or GPU type
- Maximize GPU usage by spreading demand across your GPU footprint
GPU Resource Optimization leverages NVIDIA's Multi-Instance GPU (MIG) feature to automatically partition GPUs into secure, independent pools for intelligent workload placement, enabling platform owners to:
- Achieve more effective GPU usage in the cloud or on prem
- Increase throughput automatically by running more workloads to completion
- Realize significant cost savings
For more information about this news, visit www.pepperdata.com.