Real-Time GPU Resource
Management
Kubernetes treats every GPU as fully occupied or fully available. ScaleOps AI Infra adds the intelligence layer Kubernetes is missing: dynamic fractional GPU allocation that cuts waste by up to 70% without sacrificing performance.
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Autonomous GPU Workload Rightsizing
Run more workloads on every GPU. AI Infra continuously monitors GPU memory and compute consumption to enable dynamic GPU sharing: no static slicing, no driver changes, no MIG profiles to manage. The platform identifies each workload’s actual resource footprint and rightsizes fractional allocations automatically, enabling high-density bin-packing that fits more models on fewer GPUs.
AI Replica Optimization
Scale inference workloads on actual GPU demand, not device-level averages. AI Infra surfaces per-pod GPU utilization as HPA-ready custom metrics, even when multiple workloads share the same device. Define scaling thresholds based on real workload consumption, so each workload scales independently, maintaining latency targets with fewer over-provisioned replicas.
Performance-Aware Observability
See exactly which workloads are driving GPU demand. AI Infra provides pod-level visibility into GPU memory and compute consumption, even when multiple workloads share the same device. Identify waste, safely consolidate workloads, and maintain the performance isolation production inference requires.
Why Teams Choose AI Infra
Maximize Model Performance
Accelerate model load times and maintain top performance for self-hosted AI models with dynamic demand
Cut GPU Costs
Maximize GPU utilization to eliminate idle capacity, cutting waste by up to 70%
Free Your Engineers
Automate resource management across GPUs, nodes, and clusters so DevOps and AIOps teams can focus on building, not tuning