---
title: "Save and reuse a GPU image"
description: "Bake the driver, toolkit, and your framework into a saved GPU image so new nodes boot ready to work."
content_type: "how-to"
product: "gpu-node"
platform: "MyAccount"
url: "/myaccount/gpu-node/how-to/save-and-reuse-a-gpu-image/"
topic: ["run-and-serve"]
---

# Save and reuse a GPU image

Getting a fresh GPU node ready by hand, with the framework packages and container images your
job needs, commonly takes 15 to 25 minutes. A node launched from a saved image that already
holds them is ready in about 3 to 6 minutes, so saving an image once pays off from the second
node on. **Save Image** works the same way it does on any node (see
[Save a node as an image](/myaccount/node/how-to/save-a-node-as-an-image/)). On a GPU node, what matters is what to bake in, and how a few GPU settings behave in a saved image.

Snapshots depend on the [node family](/myaccount/node/concepts/node-families/): **GDC3** nodes
support snapshots, and on **GDC** nodes Save Image is the way to capture a re-launchable
template of the root disk.

## What to bake into a GPU image

| Component                                | Why bake it                                                      |
| ---------------------------------------- | ---------------------------------------------------------------- |
| NVIDIA driver                            | Skips the kernel-module install and reboot on first boot.        |
| CUDA toolkit, if you compile on the host | Skips a multi-GB toolkit download.                               |
| Docker and the NVIDIA Container Toolkit  | Skips the `nvidia-ctk runtime configure` step on every new node. |
| Pulled container images                  | Skips a multi-GB image pull for every replica.                   |
| Conda or pip environments                | Skips the PyTorch, JAX, vLLM, or TGI install path.               |
| cuDNN and NCCL                           | Most frameworks need them; check they came with the toolkit.     |
| Firewall and `sysctl` settings           | Every node starts with the same tuning.                          |

Keep large model weights and datasets on a volume and mount them, so the image stays small. Inject
secrets, API keys, and anything per-node at runtime rather than baking them in.

## Save the GPU node as an image

1. Set up everything in [What to bake into a GPU image](#what-to-bake-into-a-gpu-image). Then
   check it end to end: run `nvidia-smi`, run a container with `--gpus all`, and run a single
   inference or training step.
2. Turn on persistence mode:

   ```bash frame="none"
   sudo nvidia-smi -pm 1
   ```

   On Ubuntu 24.04 with the 580.x driver, it stays on after a reboot by itself. On driver 570.x
   and earlier, also run `sudo systemctl enable nvidia-persistenced`.

   See [Troubleshooting GPU Node](/myaccount/gpu-node/troubleshooting/gpu-node/#slow-first-request-persistence-mode)
   if that command, or enabling the persistence service, returns an error.

3. **GDC3** nodes can run Save Image while **Running**; **GDC** nodes need to be **Powered off**
   first, the same rule as most other node families.
4. Run **Save Image**. GPU images capture the entire root disk, driver, toolkit, and container
   caches included, and can reach hundreds of GB; they are billed for their full size while
   retained, so delete a saved image you no longer need from the **Manage Saved Images** page
   (**Nodes**, then **Saved Images**). Saving an image of 50 to 300 GB takes about 10 to 20
   minutes.
5. Wait for the image to read **Ready** on the **Manage Saved Images** page. To launch a new
   node from it, open the image's **Actions** menu and click **Create Node**. The create-node
   flow opens with your image as the base; pick a plan in the same region with a root disk at
   least as big as the image.

A node launched from the image boots with the driver, toolkit, and pulled container images
already in place. Run `nvidia-smi` on it once to confirm the card, as in
[Connect to a Linux GPU node](/myaccount/gpu-node/how-to/connect-to-a-linux-gpu-node/). If the
card is missing or the driver will not load, see
[Troubleshooting GPU Node](/myaccount/gpu-node/troubleshooting/gpu-node/#card-not-detected).

Bake one image per card-family-and-framework combination, and re-bake it when you upgrade the
framework or driver, rather than trying to make one image cover every workload.

## Plan upgrade and reinstall

**Plan Upgrade** supports moving within the same card family, for example a 1x plan to a 2x or 4x
plan on the same card, or a bigger vCPU/RAM SKU on that card. To move to a different card model, an
L40S to an A100 for example, save an image and launch a new node on the target card. Plan changes go
up only, on every plan including committed ones, so size up in steps you are sure of.

**Reinstall** wipes the root disk, including the driver and toolkit. If you do not have a saved
image, plan for the full driver-and-framework install path again on first boot after a
reinstall.