> ## Documentation Index
> Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Volumes (Beta)
> Keep full fine-tuning outputs across runs with named volumes
Volumes are in **closed beta** for full fine-tuning. Reach out to us to get access.
A volume is named storage for [full fine-tuning](/hosted-training/full-finetuning) outputs. It is a CephFS-backed persistent volume claim (PVC) in your team's or personal namespace on a cluster. It outlives individual runs, so outputs stay on the volume after a run ends.
Volumes default to **1 TiB** (`1Ti`) and can grow to **10 TiB** (`10Ti`). They cannot shrink.
## Before you start
[Install and authenticate the Prime CLI](/cli-reference/introduction). These commands require Prime CLI 0.9.0 or later.
Volumes use the team configured in your CLI. Select a team before creating a volume:
```bash theme={null}
prime teams list
prime config set-team-id
```
With no team configured, volumes belong to your personal account. To switch back to personal volumes:
```bash theme={null}
prime config remove-team-id
```
Use the same team or personal account when creating, listing, resizing, deleting, and training with a volume. See [CLI configuration](/cli-reference/config-cli#team-settings).
## Create a volume
Choose a name using lowercase letters, digits, and hyphens:
```bash theme={null}
prime volumes create training-outputs
```
This creates a `1Ti` volume. To choose a different starting size, pass `--size`:
```bash theme={null}
prime volumes create experiment-outputs --size 500Gi
```
To choose a cluster, add `--cluster `; otherwise, the CLI uses your first available cluster.
## List volumes
```bash theme={null}
prime volumes list
```
The table shows each volume's name, size, cluster, status, and creation time. For JSON output:
```bash theme={null}
prime volumes list --output json
```
Both `create` and `list` accept `--output json` (or `-o json`). JSON includes the volume's `clusterId`.
## SSH into a volume
Open a session to inspect outputs or stage datasets. Configure your SSH key with `prime config set-ssh-key-path ` first.
```bash theme={null}
prime volumes ssh training-outputs --read-only
```
The CLI opens a session, waits for the connection, pins the session's SSH host key, and connects. The volume is mounted at `/volume`. Bare `prime volumes ssh ` opens a read-write session by default; `--read-only` (or `--read`) mounts it read-only to inspect or download files without changing them. Pass `--read-write` explicitly to upload or edit files:
```bash theme={null}
prime volumes ssh training-outputs --read-write
```
Sessions work from your laptop over the public internet — no VPN or special network access is needed.
Copy files with `prime volumes get [local]` or `prime volumes put [remote]`:
```bash theme={null}
prime volumes get training-outputs /runs// ./outputs
prime volumes put training-outputs ./my-data /datasets/my-data
```
These commands open or reuse a session and use rsync when available, or scp otherwise. Downloads (`get`) work with either session mode; uploads (`put`) require read-write access, since writes fail on a read-only mount. `get` requests a read-only session but can reuse your read-write session; `put` opens or reuses a read-write session. The default local destination for `get` is `.`; the default remote destination for `put` is `/`. Remote paths are relative to the volume root: `/datasets/my-data` in `put` is `/volume/datasets/my-data` inside SSH.
Stop a session using the ID printed when it opens:
```bash theme={null}
prime volumes stop training-outputs
```
## Use a volume for training
Pass a volume name when launching a full fine-tuning run:
```bash theme={null}
prime train config.toml --volume training-outputs
```
Use a [full fine-tuning config](/hosted-training/full-finetuning#config) with a `[deployment]` block. Volumes are not supported for LoRA runs.
If the volume is missing from the selected team or personal account, the CLI creates it (default `1Ti`), waits until it is `RUNNING`, then dispatches the run. Set `--volume-size` to choose its initial size; existing volumes are left unchanged. The run writes its outputs under `runs//` on the volume, giving each run its own directory.
A config with a `[data]` block and no `[trainer]` or `[orchestrator]` block auto-dispatches as [Hosted SFT](/hosted-training/hosted-sft), mounting the volume read-only at `/volume` for `data.name` to read the staged dataset.
The volume pins the run to the cluster where it was created. That cluster must support the run's GPU requirements and have the base model cached; the run cannot move to another cluster just because it has free GPUs.
You can also set the volume at the top level of your TOML config, before any tables:
```toml theme={null}
volume = "training-outputs"
volume_size = "1Ti" # Used only when creating a missing volume
```
The `--volume` and `--volume-size` flags take precedence over their config values.
## Grow a volume
Pass a new size larger than the current size, up to `10Ti`:
```bash theme={null}
prime volumes resize training-outputs --size 2Ti
```
The volume grows in place. Running pods see the new size; you do not need to stop the run. Shrinking is not supported.
## Delete a volume
Deleting a volume deletes all data on it, including outputs from every run that used it.
```bash theme={null}
prime volumes delete training-outputs
```
The CLI asks for confirmation. Pass `--yes` (or `-y`) to skip the prompt.
Team volumes require a team admin to delete them; personal volumes can be deleted by their owner.
Deletion is refused while the volume is still provisioning or runs are still mounting it. Wait for those runs to finish, or [stop them](/hosted-training/full-finetuning#run-lifecycle) and wait for their pods to terminate, then retry. `--yes` skips only the confirmation; it does not bypass this guard.