GPU Orchestration
GPU orchestration is the automated management of GPU resources across a computing cluster, including workload scheduling, resource allocation, scaling, and health monitoring. A GPU orchestration system decides which workloads run on which GPUs, manages model loading and unloading, and ensures efficient utilization of expensive GPU hardware.
GPU orchestration becomes critical as organizations scale beyond a handful of GPUs. Manual management of GPU workloads is impractical at scale — orchestration systems automate placement decisions, handle failures, and optimize resource utilization across the cluster.
Key capabilities of GPU orchestration include intelligent scheduling (placing workloads on the most appropriate GPU), auto-scaling (adjusting capacity based on demand), model weight management (caching and preloading model weights), and multi-tenant isolation (ensuring fair resource sharing).
TurbOS is Hoonify's GPU orchestration engine, originally built for high-performance computing (HPC) workloads at national laboratories. It handles GPU scheduling, model weight caching, cold start optimization, and auto-scaling for AI inference deployments.
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