> ## Documentation Index
> Fetch the complete documentation index at: https://docs.baseten.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Availability modes

> Compare the serverless and dedicated availability modes, see how Loops chooses one for each run, and choose one yourself.

Loops trains and samples in two availability modes: serverless and dedicated. Start with serverless in the [Loops quickstart](https://docs.baseten.co/loops/quickstart.md); use dedicated when you need a model or configuration serverless doesn't support.

| | Serverless | Dedicated |
| - | - | - |
| What you manage | Your training code. There are no deployments to size, start, or shut down. | Your training code, plus your run's trainer and sampler deployments: replica counts, capacity type, and reuse. |
| Billing | Tokens you train and sample. | GPU time while the run's trainer and sampler run, including time between training operations. |
| Models | Models marked **Serverless** in the [model catalog](https://docs.baseten.co/loops/supported-models.md), at the sequence lengths listed there. | Every model in the [catalog](https://docs.baseten.co/loops/supported-models.md). |
| Ending a run | [Deactivate the run](https://docs.baseten.co/loops/deactivate-a-run.md) to end it and its paired sampler. An idle run incurs no token charges. | Deactivate the run to stop GPU billing, or it [ends after inactivity](https://docs.baseten.co/loops/deactivate-a-run.md#when-a-run-ends-on-its-own), 15 minutes by default. |

## When to use each mode

Start with the serverless mode if your model is available in it. Use the dedicated mode when you need a model or configuration serverless doesn't support, or when you need to control a run's throughput with replica counts.

The serverless mode fits experimentation:

* **Self-serve.** Start runs, and start more of them, without contacting Baseten.
* **Billed for usage.** You pay for the tokens you train and sample, so time spent reading results or revising a reward function between steps costs nothing.
* **Nothing to manage.** You create a run and train, with no deployments to size, start, or shut down.

The dedicated mode fits these cases:

* **Models and configurations serverless doesn't support.** Every model in the [catalog](https://docs.baseten.co/loops/supported-models.md) trains in the dedicated mode, including models, sequence lengths, and LoRA ranks that serverless doesn't support.
* **Scaling out a run, especially asynchronous RL.** Add [trainer replicas](https://docs.baseten.co/loops/availability-modes/dedicated.md#scale-the-trainer) for update throughput and sampler replicas for rollout throughput, and use [spot capacity](https://docs.baseten.co/loops/availability-modes/dedicated.md#spot-capacity) for interruptible runs that resume from checkpoints.
* **Planned, sustained runs.** In the dedicated mode, a run's deployments belong to it for its whole duration. To plan for a large run, [request more access](https://docs.baseten.co/loops/overview.md#request-more-access).

## How Loops chooses a mode

By default, a run uses the serverless mode when your workspace has serverless access to the model and serverless supports the run's LoRA rank and sequence length. Otherwise, the run uses the dedicated mode. A run that reuses another run's deployments stays in the dedicated mode. A run's paired sampler uses the same mode as the run.

## Choose a mode

Set `scope` to `serverless` when you create a run or a standalone sampler. If serverless can't accept the run, the request fails instead of falling back to dedicated GPU billing.

**scope** `string` (default: default)

Where the run or standalone sampler trains and samples.

* `default`: Loops chooses, as described above.
* `serverless`: the serverless mode. Creating the run fails, instead of falling back to the dedicated mode, if your workspace doesn't have serverless access to the model, serverless doesn't support the requested configuration, or serverless can't start the run.
* `deployment`: the dedicated mode, with trainer and paired sampler deployments for the run, billed for GPU time. Use it to scale out a model that's also available in the serverless mode.

> [tab] Python SDK
>
> **To use the serverless mode**:
>
> Pass `scope` when you create the training client:
>
> ```python theme={"system"}
> from baseten.loops import ServiceClient
>
> training_client = ServiceClient().create_lora_training_client(
>     base_model="zai-org/GLM-5.3-Flash",
>     scope="serverless",
> )
> ```
>
> `ServiceClient.create_sampling_client()` takes the same `scope` argument for a standalone sampler.

> [tab] Environment variable
>
> **To use the serverless mode**:
>
> Set `BASETEN_LOOPS_SCOPE` for the process. It applies to every run and standalone sampler the process creates without an explicit `scope` argument:
>
> ```bash theme={"system"}
> BASETEN_LOOPS_SCOPE=serverless uv run python train_loops.py
> ```

> [tab] REST API
>
> **To use the serverless mode**:
>
> Add `scope` to the body of [`POST /v1/loops/trainers`](https://docs.baseten.co/reference/loops-api/runs/create-a-trainer):
>
> ```bash theme={"system"}
> curl -s https://api.baseten.co/v1/loops/trainers \
>   -H "Authorization: Bearer $BASETEN_API_KEY" \
>   -H "Content-Type: application/json" \
>   -d '{
>     "session_id": "<session_id>",
>     "base_model": "zai-org/GLM-5.3-Flash",
>     "scope": "serverless"
>   }'
> ```

Replica counts and the capacity type set by `availability_model` configure deployments; they don't select the mode. Set `scope` to `deployment` to make sure they apply. For more information, see [Dedicated Loops](https://docs.baseten.co/loops/availability-modes/dedicated.md).

To check which models and sequence lengths your workspace can use, see [Supported models](https://docs.baseten.co/loops/supported-models.md).