Docker vs Podman for AI/ML Workloads in 2026: A Technical Comparison

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
docker model pull ai/llama3.2:1B-Q8_0

# Run inference
docker model run ai/llama3.2:1B-Q8_0 "Explain GPU memory fragmentation"

# List local models
docker model ls

# OpenAI-compatible API on localhost
curl http://localhost:12434/engines/llama3.2/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "ai/llama3.2:1B-Q8_0", "messages": [{"role": "user", "content": "Hello"}]}'

This is a unified workflow: containers for your application, models for your AI, same CLI, same lifecycle management. The OpenAI-compatible API means your code doesn't change between local development (Model Runner) and production (self-hosted vLLM or cloud APIs).

Podman has no equivalent feature. If you're building AI applications, you'd need to separately install and manage Ollama, llama.cpp, or another inference runtime alongside Podman. Two tools, two lifecycles, two sets of configuration.

Winner: Docker

2. GPU Passthrough UX

Both runtimes support the NVIDIA Container Toolkit. The capability is equivalent. The experience is not.

Docker

docker run --gpus all nvidia/cuda:12.4-base nvidia-smi

One flag. Works after installing nvidia-container-toolkit. Docker Desktop on Linux and WSL2 handles GPU passthrough configuration automatically.

Podman

podman run \
  --device nvidia.com/gpu=all \
  --security-opt=label=disable \
  nvidia/cuda:12.4-base nvidia-smi

Requires CDI (Container Device Interface) configuration. The --security-opt=label=disable is needed because SELinux blocks GPU device access by default in rootless mode. Rootless GPU support has additional edge cases — CDI device nodes need to be readable by the unprivileged user, which requires extra system configuration.

For multi-GPU setups where you want to expose specific GPUs:

Docker:

docker run --gpus '"device=0,2"' my-training-image

Podman:

podman run --device nvidia.com/gpu=0 --device nvidia.com/gpu=2 \
  --security-opt=label=disable my-training-image

Both work. Docker's syntax is more compact and better documented for GPU-specific use cases.

Winner: Docker (UX), Tie (raw capability)

3. Docker Scout — Integrated Supply Chain Security

Docker Scout is built into the Docker CLI:

# Scan for vulnerabilities
docker scout cves my-gpu-image:latest

# Policy evaluation against your organization's rules
docker scout policy my-gpu-image:latest

# Base image recommendations
docker scout recommendations my-gpu-image:latest

# Compare two image versions
docker scout compare my-gpu-image:v2 --to my-gpu-image:v1

Scout has first-party provenance data for Docker Official Images and Docker Hardened Images. It knows the full dependency chain from source → build → image. This matters for GPU images because the dependency trees are deep (OS → CUDA → cuDNN → Python → PyTorch → vLLM) and CVEs can hide anywhere in that chain.

Podman has no built-in scanning. You'd use Trivy, Grype, or Snyk as separate tools. These are excellent scanners, but they're not integrated into the container CLI, and they don't have first-party knowledge of Docker's image provenance.

For CI pipelines, Docker Scout provides a GitHub Action that can fail builds on critical CVEs:

- name: Docker Scout scan
  uses: docker/scout-action@v1
  with:
    command: cves
    image: my-gpu-image:${{ github.sha }}
    only-severities: critical,high
    exit-code: true  # Fail the build

Winner: Docker

4. Docker Extensions Ecosystem

Docker Desktop has an extensions marketplace with tools built by the community. I built a GPU Dashboard extension that shows real-time NVIDIA GPU metrics directly in Docker Desktop — utilization, memory, temperature, power draw per device. No terminal, no nvidia-smi.

Podman Desktop has a plugin system, and it's growing. But Docker's marketplace is larger and more actively promoted. If you're building developer tools for GPU/AI workflows, Docker Extensions reaches more users.

The extension development experience is also more mature on Docker — the SDK, documentation, and example extensions are further along.

Winner: Docker

5. Docker Sandboxes for AI Agents

Docker Sandboxes provide purpose-built isolation for agentic AI workloads — LLMs that execute code, call APIs, or modify files:

services:
  ai-agent:
    image: my-agent:latest
    sandbox:
      enabled: true
      network:
        egress:
          - "api.openai.com:443"
          - "huggingface.co:443"
      resources:
        memory: 4g
        gpus: 1

This is designed for the specific use case of running untrusted LLM-generated code safely. Filesystem isolation, network egress rules, resource limits, ephemeral execution — all in one configuration.

Podman's approach is rootless-by-default with user namespaces, which provides strong general-purpose isolation. But there's no agent-specific sandbox abstraction. You'd build the equivalent with a combination of rootless mode, --network=none plus manual iptables rules, cgroups limits, and tmpfs mounts. Doable, but not turnkey.

Winner: Docker (for AI agent use case)

6. Docker Compose for GPU Development

Both Docker Compose and Podman Compose handle GPU workloads, but Docker Compose has more mature GPU support:

# Docker Compose — native GPU support
services:
  inference:
    image: vllm/vllm-openai:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 2
              capabilities: [gpu]

  gpu-monitor:
    image: pmady/otel-gpu-receiver:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

Podman Compose supports GPU devices through CDI, but the syntax is different and less documented for multi-GPU configurations. Docker Compose also integrates with Docker Desktop's resource management, giving you a GUI to see which services are using which GPUs.

Winner: Docker

7. Where Podman Wins

This wouldn't be honest without acknowledging where Podman is genuinely better:

Rootless by Default

Podman runs containers as your unprivileged user by default. No daemon running as root. This is a meaningful security advantage for multi-tenant systems and environments where the Docker daemon's root access is a compliance concern.

Daemonless Architecture

No background daemon means smaller attack surface and no single point of failure. If a Podman container crashes, it doesn't affect other containers. If the Docker daemon crashes, everything stops.

Systemd Integration

podman generate systemd --new --name my-gpu-service

Generates a proper systemd service file. Elegant for non-Kubernetes deployments where you want GPU containers managed by the init system.

Pod Semantics

podman pod create --name ml-pod
podman run --pod ml-pod --gpus all inference-server
podman run --pod ml-pod metrics-sidecar

podman pod mirrors Kubernetes pod concepts directly. Containers in a pod share network and IPC namespaces. If you're prototyping Kubernetes pod configurations locally, Podman is more natural.

RHEL/CentOS Ecosystem

If you're in a Red Hat shop, Podman is the native container runtime. It's supported, patched, and integrated with the RHEL security stack (SELinux, FIPS). Docker on RHEL is possible but not the paved path.

Kubernetes YAML Support

podman play kube deployment.yaml

Run Kubernetes YAML directly with Podman. Useful for testing manifests without a cluster. Docker has docker compose but not native Kubernetes YAML support in the CLI.

8. The Production Runtime Question

Here's the thing most of this comparison misses: in production, neither Docker nor Podman is your container runtime.

Kubernetes uses containerd or CRI-O. Both Docker and Podman are development tools that produce OCI-compliant images. The image you build with docker build runs identically on containerd in your Kubernetes cluster.

So the real question is: which tool gives you the best development experience for GPU/AI containers?

For that question, Docker's answer in 2026 is comprehensive:

• Model Runner for local LLM inference
• Sandboxes for AI agent isolation
• Scout for supply chain security on deep GPU dependency trees
• Extensions for custom developer tools (GPU monitoring)
• Compose with mature GPU support
• Desktop with automatic GPU passthrough

Podman's answer is: strong fundamentals (rootless, daemonless, pod semantics) but no AI-specific features.

The Bottom Line

Criteria	Docker	Podman
LLM inference (Model Runner)	✅ Built-in	❌ Need separate tool
AI agent sandboxes	✅ Native	⚠️ Manual configuration
GPU passthrough UX	✅ One flag	⚠️ CDI + SELinux workarounds
Supply chain scanning	✅ Scout built-in	⚠️ External tools
Extensions ecosystem	✅ Marketplace	⚠️ Smaller plugin system
Rootless security	⚠️ Opt-in	✅ Default
Daemonless	❌ Requires daemon	✅ No daemon
Systemd integration	⚠️ Basic	✅ Native
Kubernetes YAML	❌ No	✅ podman play kube
RHEL support	⚠️ Community	✅ Native

For AI/ML development in 2026: Docker. The Model Runner, Sandboxes, Scout, and GPU UX advantages are not marginal — they're the entire AI developer workflow.

For security-first Linux servers: Podman. Rootless-by-default and daemonless architecture are real advantages.

For production Kubernetes: Doesn't matter. Both produce OCI images. containerd runs them.

Pick the tool that matches your workload. For GPU/AI containers, that's Docker in 2026.

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Pavan Madduri is a Senior Cloud Platform Engineer at W.W. Grainger, Inc., CNCF Golden Kubestronaut, and Oracle ACE Associate. He maintains keda-gpu-scaler and otel-gpu-receiver, and builds GPU infrastructure tools on Docker and Kubernetes.