What Is Federator.ai GPU Booster?
Federator.ai GPU Booster runs on Kubernetes and uses patented multi-layer correlation with machine learning to manage GPU resources in multi-tenant AI and ML environments. Several teams can share the same GPU pool, including teams training large language models.
It predicts demand spikes and reallocates GPU capacity before jobs stall, instead of locking teams into fixed allocations. Teams run more training jobs on the same hardware and spend less doing it.
50%
90%
GPU Utilization
One-Step
Easy Installation
Core Technologies Powering Federator.ai GPU Booster
Predictive Analytics and Dynamic GPU Resource Allocation
Analyzes historical GPU usage patterns and recommends allocations before demand spikes cause contention, so multi-tenant training jobs get the compute they need.
Multi-Instance GPU (MIG) Utilization on Kubernetes
Recommends optimal NVIDIA MIG (Multi-Instance GPU) partitioning configurations to maximize GPU utilization, and then provides a script to apply them directly to the cluster.
Adaptability to Diverse AI/ML Workloads
Runs across multi-tenant clusters training everything from small models to resource-intensive large language models, adjusting GPU allocations as workload demand changes.
Multi-Layer Correlation
Correlates GPU and memory activity across application and infrastructure layers to see what the primary workload depends on. GPU capacity goes to the workload that matters, not spread thin across everything else. (U.S. Patent No. 11,579,933 B2)
Spatial-Temporal GPU Optimization
Combines dynamic bin-packing across nodes and GPU partitions with time-series demand forecasting, lifting cluster utilization from an industry average under 40% to near 90%. Budget goes to new projects instead of new hardware. (U.S. Patent No. 12,596,580 B2)
Benefits of Federator.ai GPU Booster
Minimize Latency
Efficient Resource Allocation
Partitions each GPU into isolated fractions with Multi-Instance GPU (MIG) technology, so multiple tenants share hardware without performance interference between workloads.
Maximize Total Throughput
Predicts demand and reallocates GPU capacity across concurrent training runs in real time, lifting cluster utilization from under 40% to near 90%.
ESG/ Green IT
Accelerate Time-to-Value
How Federator.ai GPU Booster Works to Optimize GPU Resource Usage for AI/ML Training
Video | Federator.ai GPU Booster Feature Demo
