Containerization for AI: Docker and Kubernetes for GPU Wo…

May 14, 2026 · Enterprise AI Deployment
Reviewed by NTS AI Infrastructure Engineer · Technical accuracy verified for enterprise & federal deployment
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Quick Summary

  • Docker: NVIDIA Container Toolkit enables GPU access in containers
  • Kubernetes: Orchestrates multi-node GPU training jobs efficiently
  • Slurm: Traditional HPC scheduler, preferred for large training jobs
  • MIG: Multi-Instance GPU partitions A100/H100 for multi-tenant use
  • Best Practice: Kubernetes for inference serving, Slurm for training

Containerization for GPU Workloads

Containerization has become the standard deployment model for AI workloads, providing environment reproducibility, dependency isolation, and resource management. Docker and Kubernetes GPU server form the foundation of modern AI infrastructure, while Slurm remains dominant in HPC-oriented AI training environments. Understanding when to use each technology is essential for efficient AI operations.

Docker for GPU-Accelerated Containers

NVIDIA Docker 2.0 (now the NVIDIA Container Toolkit) provides seamless GPU access within containers through the nvidia-container-runtime. This runtime automatically mounts GPU drivers, CUDA libraries, and NCCL communication libraries from the host into containers, enabling GPU-accelerated applications without manual driver installation inside containers. The container toolkit supports GPU enumeration, MIG partitioning, and compute exclusive modes.

Kubernetes for Inference Serving

Kubernetes excels at orchestrating stateless AI inference workloads. With KFServing/KServe, models can be deployed with automatic scaling, rolling updates, and canary deployments. NVIDIA GPU Operator extends Kubernetes with GPU monitoring, driver management, and MIG partitioning. For production inference serving, Kubernetes provides the orchestration framework for high-availability model serving with load balancing and auto-scaling.

Slurm for Training Workloads

Slurm (Simple Linux Utility for Resource Management) remains the dominant workload manager for AI training in HPC environments. Slurm's gang scheduling guarantees GPU allocations without contention, critical for long-running training jobs. Slurm integrates with Enroot or Pyxis for containerized training workloads, combining Slurm scheduling with Docker-compatible containers.

Best Practice: Unified Container Strategy

Enterprise AI teams should standardize on container images that work across development, training, and inference environments. NVIDIA GPU Cloud (NGC) provides optimized containers for major AI frameworks. Organizations should build custom containers based on NGC images with additional libraries, security patches, and custom code, maintaining a container registry for version control.

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Frequently Asked Questions

Should I use Kubernetes or Slurm for AI training?

Kubernetes is preferred for inference serving and short training jobs (24 hours) requiring guaranteed resource allocations. Many organizations use both: Slurm for training, Kubernetes for inference.

How do I share GPUs between containers?

NVIDIA MIG partitions A100/H100 into up to 7 isolated GPU instances. For non-MIG GPUs, NVIDIA time-slicing shares GPUs between containers with quality-of-service controls. For exclusive GPU access, use Kubernetes node selectors or Slurm GPU allocation.