What is GPU Resource Orchestration in ML Clusters?

GPU Resource Orchestration: The automated allocation, scheduling, and management of GPU resources across ML workloads. Unlike CPU orchestration, GPU scheduling must handle hardware heterogeneity (different GPU models), topology constraints (NVLink connections), and the binary nature of GPU allocation (models often need whole GPUs, not fractions).

Why GPU Orchestration is Different

CPUs are fungible: any core can run any task, and tasks share cores seamlessly via time-slicing. GPUs are not: a model compiled for one GPU architecture may not run on another, memory is not easily shared between processes, and GPU context switching is expensive (milliseconds vs microseconds). Additionally, GPUs cost 10-100x more than CPUs per hour, making utilization efficiency critical. A cluster with 50% GPU utilization is wasting thousands of dollars per day.

Key Orchestration Challenges

Heterogeneity: Clusters often mix GPU generations (A100, H100, older V100s). Training workloads need specific architectures; inference can often run on older hardware. Topology: Multi-GPU training performance depends on interconnect bandwidth. GPUs connected via NVLink (600 GB/s) communicate 10x faster than PCIe (32 GB/s). Scheduling must consider physical topology, not just available GPU count. Fragmentation: If jobs request 4 GPUs but available GPUs are scattered across nodes (2 here, 2 there), the job cannot run despite sufficient total capacity.

Orchestration Components

GPU discovery (detecting available GPUs and their properties), scheduling (matching workloads to appropriate GPUs), isolation (preventing workloads from interfering with each other), and monitoring (tracking utilization, memory, temperature). Standard container orchestrators like Kubernetes handle discovery and basic scheduling; ML workloads often require custom schedulers for topology-awareness and gang scheduling.

Cost Reality: GPU clusters are expensive. An 8-GPU node costs around 30,000 per month. At 50% utilization, you are wasting 15,000 per node monthly. Orchestration directly impacts the bottom line.