GPU Memory Optimization
Stop Paying for GPU Memory Your AI Workloads Don’t Use
Inference workloads reserve significantly more GPU memory than they consume. ScaleOps autonomously corrects overprovisioned GPU memory requests, reclaiming capacity for sharing and reducing the memory footprint of each workload.
The Problem with Reserved GPU Memory for Inference Workloads
Reserved Allocation Wastes GPU Memory
GPU Memory is sized for worst-case demand that rarely arrives. GPU memory sits reserved and idle instead of being available for other workloads.
Overprovisioned, but Blocked For GPU Sharing
When a workload appears to saturate GPU memory, fractional GPU policies cannot apply, even when GPU memory utilization is low.
Static Configuration, Dynamic Memory Usage
Traffic shifts and model changes alter how workloads consume memory. Deploy-time configurations can’t keep up.
Autonomous Workload Memory Profiling
ScaleOps observes each inference workload’s actual GPU memory consumption in live production, tracking how usage shifts across different load conditions and concurrency levels. No manual profiling. No static assumptions.
Continuous Memory Reservation Rightsizing
ScaleOps continuously analyzes each workload’s actual GPU consumption and manages inference memory reservations based on live behavior. Over-provisioned allocations are corrected automatically, so workloads stop holding capacity they never used.
Maximize GPU Utilization
Unlock GPU Sharing
ScaleOps enables GPU sharing for memory-bound workloads that are blocked by overprovisioning. Once memory reservation aligns with actual usage, fractional GPU policies apply without manual changes.
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