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Cloud GPU • Oct 16, 2026

How to Stop a cloud GPU for AI image generation and Avoid Charges

Learn how to stop a cloud GPU for AI image generation on RunPod, what still bills after stopping, and how to terminate safely to avoid surprise charges.

Stopping a cloud GPU for AI image generation means releasing the GPU so compute billing ends, while keeping your /workspace data on the volume disk. On RunPod, the Stop action does exactly that — but it is not a full shutdown. According to the RunPod Pod management documentation, stopping a Pod releases the GPU and preserves data in /workspace, container disk data is cleared, and you are still charged for volume disk storage while the Pod is stopped. If you do not need to keep the environment, terminate the Pod instead.

Stop vs. terminate: what each action does

The two actions are not interchangeable, and choosing the wrong one is the most common source of unexpected charges.

  • Stop — releases the GPU, preserves /workspace on the volume disk, clears container disk data. Volume disk storage continues to bill.
  • Terminate — permanently deletes all data not stored on a network volume. Export important data first.
  • Network volume — if you attached one, your /workspace data is preserved whether you stop or terminate the Pod.

That last point matters for image generation workflows: model weights are large, and re-downloading them after every session wastes time. A network volume lets you terminate the GPU without losing the checkpoint, so you pay only for storage between jobs.

How to stop a RunPod Pod

The official Pod management page documents three equivalent routes. Pick whichever fits your workflow.

Web UI

  1. Open the Pods page.
  2. Expand your Pod.
  3. Click the Stop button (square icon) and confirm.

CLI

Install and configure the RunPod CLI first, then stop by Pod ID:

runpodctl config --apiKey RUNPOD_API_KEY
runpodctl pod stop $RUNPOD_POD_ID

You can also schedule a stop, which is useful when you know a batch job will finish soon:

sleep 2h ; runpodctl pod stop $RUNPOD_POD_ID &

REST API

curl --request POST \
  --url "https://rest.runpod.io/v1/pods/$RUNPOD_POD_ID/stop" \
  --header 'Authorization: Bearer RUNPOD_API_KEY'

To permanently remove the Pod and its non-network-volume data, use runpodctl pod delete $RUNPOD_POD_ID or the corresponding DELETE request shown in the same documentation.

What still bills after you stop

Stopping ends GPU compute billing, but the documentation is explicit that volume disk storage continues to bill while the Pod is stopped. Container disk data is cleared on stop, so anything you wrote outside /workspace is gone. Before you walk away, confirm two things: that your outputs and checkpoints live in /workspace or a network volume, and that the storage you are keeping is storage you actually need.

If you do not need to retain the environment, terminate the Pod rather than leaving it stopped.

This is the same billing-state check recommended in the cloud deployment planning guide: confirm what continues billing while an instance is stopped, and treat the checkout screen as the source of truth for current rates. That guide does not claim a fixed hourly price for any provider, and neither should you assume one — inventory and rates change.

Restarting later: one caveat

Starting a stopped Pod resumes it, but the documentation notes that you may be allocated zero GPUs if capacity has changed. In practice, a stopped Pod is not a reservation. If your image generation pipeline depends on a specific GPU type, plan for the possibility that it is unavailable when you return. If you only need occasional inference and do not want to manage capacity at all, the no-GPU workflow is worth reading before you rent anything.

A practical shutdown checklist

  1. Save outputs and checkpoints to /workspace or a network volume.
  2. Remove secrets from notebooks and environment variables.
  3. Decide: stop (keep volume disk, keep paying storage) or terminate (delete non-network-volume data).
  4. Run the stop or delete command, or use the Web UI.
  5. Verify the Pod state on the Pods page.

If you are still choosing a machine, start from the model's official requirements rather than a provider or a price — the quickstart links the exact repositories and model cards. Note that hosted models such as Nano Banana 2 are Google cloud APIs and do not require a rented GPU at all; open-weight models like HunyuanImage 3.0 have multi-GPU-class requirements listed in Tencent's official table. The distinction matters because only the open-weight route involves a Pod you have to stop.

RunPod vs Vast.ai on this specific question

For cost, setup, and reliability comparisons, the site's cloud deployment guide covers what to compare on a marketplace: total GPUs and VRAM, interconnect and topology, software image compatibility, storage, billing state, and current price. On the narrow question of stopping charges, the RunPod documentation is unambiguous about the stop/terminate distinction and the storage billing that continues after a stop. Equivalent behavior on other marketplaces is not established by the supplied sources, so verify each provider's own documentation before assuming a stopped instance is free.

The short version: stop to pause compute and keep your workspace, terminate to end everything except network-volume data, and never assume a stopped instance costs nothing.

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