Container Orchestration

Container orchestration automates the deployment, management, scaling, and networking of containerized applications across a cluster of machines. In the context of GPU infrastructure, orchestration systems schedule containerized AI workloads onto GPU-equipped nodes, manage GPU resource allocation, handle container lifecycle events, and ensure workloads run reliably. Kubernetes has emerged as the dominant container orchestration platform, with specialized extensions for GPU workloads.

For GPU workloads, container orchestration adds unique challenges beyond what standard CPU orchestration handles. GPU resources must be tracked and allocated at the device level using the NVIDIA device plugin or similar mechanisms. Container images for AI workloads are often large (10+ GB) due to CUDA libraries and model weights, making image pull times a significant factor in scheduling latency. GPU workloads also have distinct failure modes, including GPU hardware errors and out-of-memory crashes, that the orchestration system must detect and handle.

Kubernetes manages GPU workloads through resource requests and limits in pod specifications. A pod can request a specific number of GPUs, and the Kubernetes scheduler ensures it is placed on a node with available GPU resources. More advanced scheduling features include GPU topology awareness (placing pods on GPUs connected by NVLink for multi-GPU workloads), bin-packing multiple workloads onto shared GPUs using time-slicing or MIG, and priority-based preemption for high-priority inference traffic.

Serverless GPU platforms like Cumulus build on top of container orchestration with additional layers of abstraction. They handle container image optimization, model weight staging, GPU selection, autoscaling policies, and cold start optimization automatically. The user deploys a model, and the platform manages all container orchestration details. For teams running their own GPU clusters, Cumulus OS provides a Kubernetes-native orchestration layer with GPU-aware scheduling and intelligent bin-packing.

Effective container orchestration is foundational to GPU infrastructure at scale. Without it, teams must manually manage which workloads run on which GPUs, handle failures through manual intervention, and coordinate deployments across machines. Orchestration transforms GPU clusters from collections of individual machines into a unified compute platform that can be managed declaratively through configuration rather than imperatively through manual operations.