Automated Fractional GPUs
Run More AI Workloads. Buy Fewer GPUs.
Cut GPU Costs by 70%.
ScaleOps autonomously detects each AI workload’s GPU usage behavior, assigns the right fractional GPU policy, and continuously manages fractional GPU allocation in real time. No manual configuration required.
The Problem with Static GPU Allocation
Kubernetes Allocates GPUs as Whole Units
One pod gets one GPU. Sharing is not built in, so most GPU capacity sits idle.
AI Workload Behavior is Dynamic
Workload behavior changes with application demand. A fractional policy that fit yesterday may starve a workload today.
Open-Source Solutions Can’t Keep Up
Existing solutions rely on static, node-level setups. Getting it right across a fleet of AI workloads isn’t a setup problem, it’s a permanent maintenance burden.
Fractional GPU Allocation, Per Workload
ScaleOps continuously analyzes each workload’s actual GPU consumption and allocates fractional GPU resources based on live behavior. Over-provisioned allocations are corrected automatically, so workloads stop holding capacity they never used.
Autonomous Workload Detection and Policy Assignment
ScaleOps detects whether each inference workload is real-time, near-real-time, or batch, based on observed workload characteristics. It then assigns the right policy to drive rightsizing and GPU sharing optimization, with no manual intervention required.
Maximize GPU Utilization
Learns From Patterns, Adapts in Real Time
ScaleOps continuously monitors each workload, reacting in real time and automatically re-optimizing resources and GPU sharing as usage evolves, guided by deep insights from workload and application behavior.
Cloud Resource Management Reinvented
Boost Performance & Reliability
Ensure consistent performance and uptime, even in the most dynamic environments.
Free Your Engineers
Eliminate repeated manual tuning forever, allowing you to focus on innovation.
Cut Costs by 80%
Pay only for the cloud resources you need without compromising performance.
Instant Value with Seamless Automation