Maximize University GPU Resources with Dynamic Resource Optimization

Meet with an optimization expert to discover strategies for increasing GPU utilization, throughput, and research efficiency across campus.
university-dashboard-graphic
THE CHALLENGE

GPU Demand Far Outpaces Supply

GPU-powered high performance computing (HPC) has become foundational across nearly every department—from computational physics and climate modeling to machine learning and social sciences. But demand often exceeds available supply, due to:

Limited Inventory

Limited GPU inventory and slow procurement cycles causes stalled research cycles inability to support growing AI/HPC demand.

Competition

Competition between departments and users such as researchers, faculty, and students leads to inequitable access and delayed progress for lower-funded labs or student-led projects.

Low Utilization

Long queue times, resource contention, and inefficient scheduling causes slower experimentation, fewer iterations, and reduced publication or grant competitiveness.

Limited Visibility

Idle GPUs when workloads are mismatched to GPU type or size creates wasted compute resources and inflating operational costs.

MAXIMIZE GPU RESOURCES

Optimize Your Entire GPU Footprint

Pepperdata enables universities to:

  • Increase GPU utilization by matching demand with available supply
  • Run more workloads on the same infrastructure through GPU slicing and automated rightsizing
  • Reduce operational costs tied to idle resources, fragmentation, and inefficient queueing

 

WATCH A DEMO DOWNLOAD THE DATASHEET
Create an image that shows these benefits Key Capabilities Identifies mismatches between GPU supply and workload demand Reallocates demand by time and-1
DATASHEET

Easily Shift GPU Demand and Maximize Cluster Capacity

Pepperdata analyzes telemetry from campus GPU clusters to identify demand patterns and surface where demand can be shifted. Operators gain full visibility by GPU type and time, helping them reassign workloads to periods or GPU types with excess capacity.

DOWNLOAD THE DATASHEET

Pepperdata GPU Resource Optimization

Pepperdata uses NVIDIA MIG to automatically partition GPUs into secure, independent slices—ensuring workloads receive exactly the capacity they need.

GPU Slice Pools

Full GPU
  • For the most intensive workloads
1/2 GPU
  • For moderately demanding workloads
1/3 GPU
  • For smaller workloads

Key Capabilities:

supply-demand-align-icon

Automatically creates GPU slice pools 

identify-gaps-bullet

Rightsizes workloads and allocates them to the optimal slice

utilization-icon-1

Dynamically adjusts pool capacity based on real-time usage

Supported GPU Hardware

GPU Demand Optimization All GPU types
GPU Resource Optimization
NVIDIA A100 and newer GPUs, including the following:
  • A100
  • B200
  • GH200
  • H100
  • H200
GET STARTED

See Pepperdata in Action

Request a walkthrough to see how your institution can improve GPU usage, eliminate bottlenecks, and unlock more research output.

LET'S CHAT LEARN MORE
Build a white background image that highlights these core benefits and generate these in rows with a border per row More effective GPU capacity Highe-1