This is an honest comparison from someone who runs GPU containers in production daily. Both Docker and Podman are excellent container runtimes. But for AI/ML infrastructure in 2026, Docker has pulled ahead in ways that matter if you're building inference services, training pipelines, or agentic AI workflows.

I maintain keda-gpu-scaler (GPU autoscaling for KEDA), otel-gpu-receiver (GPU observability for OpenTelemetry), and contributed GPU NUMA topology scheduling to Volcano. All of this runs in Docker containers. Here's why.

1. Docker Model Runner — No Podman Equivalent

Docker Model Runner lets you pull, run, and manage LLMs alongside your containers using the same CLI and registry infrastructure:

# Pull a model like you pull an image