Optimize GPU Resource Efficiency

Pepperdata Resource Optimization maximizes utilization and significantly reduces GPU cost by leveraging NVIDIA's Multi-Instance GPU (MIG) feature. Pepperdata continuously monitors GPU usage and demand. Based on this real-time data, Pepperdata dynamically creates the pools of sliced GPUs, adjusting the capacity of each GPU pool so each can scale up or down as needed to prevent underutilization and bottlenecks. Pepperdata then intelligently assigns workloads to the most appropriate GPU slices, learning from historical usage patterns to refine these assignments over time.

Pepperdata automatically partitions single GPUs into secure, independent GPU slices, creating three GPU partition pools in your environment for workload placement:

1. Full GPUs: Dedicated for demanding workloads that require an entire GPU.
2. ½ GPUs (2 MIG slices per GPU): For medium workloads that only require half of a GPU.
3. ⅓ GPUs (3 MIG slices per GPU): Ideal for lighter workloads that fit within a third of a GPU.

Managing MIG slices can be incredibly complex, manual, and challenging—but Pepperdata makes this effortless. Instead of investing tedious and time-consuming manual effort into planning GPU slices, tracking demand, coordinating workloads, and constantly resizing resources, Pepperdata does it all automatically for you. As a result, both platform operators and application developers are freed from the manual, error-prone overhead of guessing GPU needs and reconfiguring slices, which frees them to focus on higher-value work.