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

# Workload Classes

> Reserved vs elastic workloads

Chamber supports two workload classes that determine how workloads access GPU capacity: **Reserved** and **Elastic**.

## Overview

| Class        | Capacity Source                         | Preemptible | Best For                                |
| ------------ | --------------------------------------- | ----------- | --------------------------------------- |
| **Reserved** | Team's reservation                      | No          | Production training, SLA workloads      |
| **Elastic**  | Reserved capacity first, then idle pool | Yes         | Experiments, batch processing, dev work |

## Reserved Workloads

Reserved workloads use capacity from the team's reservation. They are guaranteed resources and cannot be preempted.

**Use reserved for:**

* Long-running training that can't be interrupted
* Workloads with delivery commitments or deadlines
* ML pipelines that must complete reliably

## Elastic Workloads

Elastic workloads use idle capacity. They maximize utilization but can be preempted when reserved workloads need resources.

**Use elastic for:**

* Experiments and exploratory work
* Development and debugging
* Batch workloads that can checkpoint and resume

<Tip>
  Design elastic workloads to checkpoint periodically so they can resume if preempted.
</Tip>

## Related Concepts

<CardGroup cols={2}>
  <Card title="Scheduling" icon="clock" href="/concepts/scheduling">
    How Chamber schedules workloads
  </Card>
</CardGroup>