> ## Documentation Index > Fetch the complete documentation index at: https://runpod-b18f5ded-promptless-websocket-streaming-tutorial.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Network volumes > Persistent, portable storage for your AI workloads. Network volumes offer persistent storage that exists independently of your compute resources. Your data is retained even when your Pods are terminated or your Serverless workers are scaled to zero. You can use them to share data and maintain datasets across multiple machines and [Runpod products](/overview). Network volumes are backed by high-performance NVMe SSDs connected via high-speed networks. Transfer speeds typically range from 200-400 MB/s, with peak speeds up to 10 GB/s depending on location and network conditions. ## When to use network volumes Consider using network volumes when you need: * **Persistent data that outlives compute resources**: Your data remains accessible even after Pods are terminated or Serverless workers stop. * **Shareable storage**: Share data across multiple Pods or Serverless endpoints by attaching the same network volume. * **Portable storage**: Move your working environment and data between different compute resources. * **Efficient data management**: Store frequently used models or large datasets to avoid re-downloading them for each new Pod or worker, saving time, bandwidth, and reducing cold start times. ## Pricing Network volumes are billed hourly at a rate of \$0.07 per GB per month for the first 1TB, and \$0.05 per GB per month for additional storage beyond that. If your account lacks sufficient funds to cover storage costs, your network volume may be terminated. Once terminated, the disk space is immediately freed for other users, and Runpod cannot recover lost data. Ensure your account remains funded to prevent data loss. ## Create a network volume Network volume size can be increased later, but cannot be decreased. To create a new network volume: 1. Navigate to the [Storage page](https://www.console.runpod.io/user/storage) in the Runpod console. 2. Click **New Network Volume**. 3. Select a datacenter for your volume. Datacenter location does not affect pricing, but determines which GPU types and endpoints your network volume can be used with. 4. Provide a descriptive name for your volume (e.g., "project-alpha-data" or "shared-models"). 5. Specify the desired size for the volume in gigabytes (GB). 6. Click **Create Network Volume**. You can edit and delete your network volumes using the [Storage page](https://www.console.runpod.io/user/storage). To create a network volume using the REST API, send a POST request to the `/networkvolumes` endpoint: ```bash theme={null} curl --request POST \ --url https://rest.runpod.io/v1/networkvolumes \ --header 'Authorization: Bearer RUNPOD_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "name": "my-network-volume", "size": 100, "dataCenterId": "US-KS-2" }' ``` For complete API documentation and parameter details, see the [network volumes API reference](/api-reference/network-volumes/POST/networkvolumes). ## Network volumes for Serverless When attached to a Serverless endpoint, a network volume is mounted at `/runpod-volume` within the worker environment. ### Benefits for Serverless Using network volumes with Serverless provides several advantages: * **Reduced cold starts**: Store large models or datasets on a network volume so workers can access them quickly without downloading on each cold start. * **Cost efficiency**: Network volume storage costs less than frequently re-downloading large files. * **Simplified data management**: Centralize your datasets and models for easier updates and management across multiple workers and endpoints. ### Attach to an endpoint To enable workers on an endpoint to use network volumes: 1. Navigate to the [Serverless section](https://www.console.runpod.io/serverless/user/endpoints) of the Runpod console. 2. Select an existing endpoint and click **Manage**, then select **Edit Endpoint**. 3. In the endpoint configuration menu, scroll down and expand the **Advanced** section. 4. Click **Network Volumes** and select one or more network volumes you want to attach to the endpoint. 5. Configure any other fields as needed, then select **Save Endpoint**. Data from the attached network volume(s) will be accessible to workers from the `/runpod-volume` directory. Use this path to read and write shared data in your [handler function](/serverless/workers/handler-functions). When you attach multiple network volumes to an endpoint, you can only select one network volume per datacenter. Writing to the same network volume from multiple endpoints or workers simultaneously may result in conflicts or data corruption. Ensure your application logic handles concurrent access appropriately for write operations. ### Attach multiple volumes If you attach a single network volume to your Serverless endpoint, worker deployments will be constrained to the datacenter where the volume is located. This may impact GPU availability and failover options. To improve GPU availability and reduce downtime during datacenter maintenance, you can attach multiple network volumes to your endpoint. Workers will be distributed across the datacenters where the volumes are located, with each worker receiving exactly one network volume based on its assigned datacenter. Data **does not sync** automatically between multiple network volumes even if they are attached to the same endpoint. You'll need to manually copy data (using the [S3-compatible API](/storage/s3-api) or [`runpodctl`](#using-runpodctl)) if you need the same data to be available to all workers on the endpoint (regardless of which volume they're attached to). ## Network volumes for Pods When attached to a Pod, a network volume replaces the Pod's default volume disk and is typically mounted at `/workspace`. Network volumes are only available for Pods in the Secure Cloud. For more information, see [Pod types](/pods/overview#pod-types). ### Attach to a Pod Network volumes must be attached during Pod deployment. They cannot be attached to a previously-deployed Pod, nor can they be detached later without deleting the Pod. To deploy a Pod with a network volume attached: 1. Navigate to the [Pods section](https://www.console.runpod.io/pods) of the Runpod console. 2. Select **Deploy**. 3. Select **Network Volume** and choose the network volume you want to attach from the dropdown list. 4. Select a GPU type. The system will automatically show which Pods are available to use with the selected network volume. 5. Select a **Pod Template**. 6. If you wish to change where the volume mounts, select **Edit Template** and adjust the **Volume Mount Path**. 7. Configure any other fields as needed, then select **Deploy On-Demand**. Data from the network volume will be accessible to the Pod from the volume mount path (default: `/workspace`). Use this directory to upload, download, and manipulate data that you want to share with other Pods. ### Share data between Pods You can attach a network volume to multiple Pods, allowing them to share data seamlessly. Multiple Pods can read files from the same volume concurrently, but you should avoid writing to the same file simultaneously to prevent conflicts or data corruption. ## Network volumes for Instant Clusters Network volumes for Instant Clusters work the same way as they do for Pods. They must be attached during cluster creation, and by default are mounted at `/workspace` within each node in the cluster. ### Attach to an Instant Cluster To enable workers on an Instant Cluster to use a network volume: 1. Navigate to the [Instant Clusters section](https://www.console.runpod.io/cluster) of the Runpod console. 2. Click **Create Cluster**. 3. Click **Network Volume** and select the network volume you want to attach to the cluster. 4. Configure any other fields as needed, then click **Deploy Cluster**. ## S3-compatible API Runpod provides an [S3-compatible API](/storage/s3-api) that allows you to access and manage files on your network volumes directly, without needing to launch a Pod or run a Serverless worker for file management. This is particularly useful for: * **Uploading large datasets or models** before launching compute resources. * **Managing files remotely** without maintaining an active connection. * **Automating data workflows** using standard S3 tools and libraries. * **Reducing costs** by avoiding the need to keep compute resources running for file management. * **Pre-populating volumes** to reduce worker initialization time and improve cold start performance. The S3-compatible API supports standard S3 operations including file uploads, downloads, listing, and deletion. You can use it with popular tools like the AWS CLI and Boto3 (Python). The S3-compatible API is currently available for network volumes in the following datacenters: `EUR-IS-1`, `EU-RO-1`, `EU-CZ-1`, `US-KS-2`, `US-CA-2`. ## Migrate files You can migrate files between network volumes (including between data centers) using the following methods: ### Using runpodctl The simplest way to migrate files between network volumes is to use `runpodctl send` and `receive` on two running Pods. Before you begin, you'll need: * A source network volume containing the data you want to migrate. * A destination network volume (which can be empty or contain existing data). Deploy two Pods using the default Runpod PyTorch template. Each Pod should have one [network volume attached](#attach-to-a-pod). 1. Deploy the first Pod in the source data center and attach the source network volume. 2. Deploy the second Pod in the target data center and attach the target network volume. 3. Start the [web terminal](/pods/connect-to-a-pod#web-terminal) in both Pods. Using your source Pod's web terminal, navigate to the network volume directory (usually `/workspace`): ```bash theme={null} cd workspace ``` Use `runpodctl send` to start the transfer. To transfer the entire volume: ```bash theme={null} runpodctl send * ``` You can also specify specific files or directories instead of `*`. After running the send command, copy the `receive` command from the output. It will look something like this: ```bash theme={null} runpodctl receive 8338-galileo-collect-fidel ``` Using your destination Pod's web terminal, navigate to the network volume directory (usually `/workspace`): ```bash theme={null} cd workspace ``` Paste and run the `receive` command you copied earlier: ```bash theme={null} runpodctl receive 8338-galileo-collect-fidel ``` The transfer will begin and show progress as it copies files from the source to the destination volume. For a visual walkthrough using JupyterLab, check out this video tutorial: