# Supported Features

Different types of workloads in NVIDIA Run:ai, including both NVIDIA Run:ai native workloads and workloads enabled via the Resource Interface (RI) offer varying levels of feature support. When selecting a workload, it is important to consider which platform capabilities are required for your use case.

The availability of specific features and capabilities can evolve across different NVIDIA Run:ai versions. Refer to the table and documentation for the most current support details.

{% hint style="info" %}
**Note**

[Other Kubernetes workloads](#other-kubernetes-workloads) can also be submitted via `kubectl`, receiving only minimal scheduling and platform capabilities.
{% endhint %}

## Workload Submission <a href="#workload-submission-methods" id="workload-submission-methods"></a>

<table><thead><tr><th></th><th data-type="checkbox">Workspace</th><th data-type="checkbox">Standard Training</th><th data-type="checkbox">Distributed Training</th><th data-type="checkbox">Inference</th><th data-type="checkbox">Distributed Inference</th><th data-type="checkbox">Workloads via Resource Interface</th></tr></thead><tbody><tr><td>UI</td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td></tr><tr><td>API</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td>CLI</td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td></tr></tbody></table>

## Scheduling and Resource Management

<table><thead><tr><th>Functionality</th><th data-type="checkbox">Workspace</th><th data-type="checkbox">Standard Training</th><th data-type="checkbox">Distributed Training</th><th data-type="checkbox">Inference</th><th data-type="checkbox">Distributed Inference</th><th data-type="checkbox">Workloads via Resource Interface</th></tr></thead><tbody><tr><td><a href="/pages/SasS94gdsz5nOgpmSNCb#fairness-fair-resource-distribution">Fairness</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/SasS94gdsz5nOgpmSNCb#priority-and-preemption">Priority and preemption</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/SasS94gdsz5nOgpmSNCb#over-quota">Over quota</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/yl1KYTa0wnf69ZkxyJvk">Node pools</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/SasS94gdsz5nOgpmSNCb#placement-strategy-bin-pack-and-spread">Bin packing / Spread</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/oMwV8HUUiC3ax9YwVZj1">Multi-GPU fractions</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/P4XA8WHQNhkPrrGIwXND">Multi-GPU dynamic fractions</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/I9mPKQYPGUfrRRcj0QDa">Node level scheduler</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/fPcBvSr7cnyfeU8eIm4L">Multi-GPU memory swap</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td>Elastic scaling</td><td>false</td><td>false</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/SasS94gdsz5nOgpmSNCb#gang-scheduling">Gang scheduling</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/rLpWRBqXfGDb0JzPSgbf">Network topology-aware scheduling</a></td><td>false</td><td>false</td><td>true</td><td>false</td><td>true</td><td>false</td></tr><tr><td><a href="/pages/MoXCm8AVY69FzrS7sepA">GB200 NVL72 and Multi-Node NVLink domains (MNNVL)</a></td><td>false</td><td>false</td><td>true</td><td>false</td><td>true</td><td>false</td></tr><tr><td><a href="/pages/qPOp1wXyTvi8GtTU9rSb">Scheduling rules</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td></tr></tbody></table>

## Operational and Platform Features

<table><thead><tr><th>Functionality</th><th data-type="checkbox">Workspace</th><th data-type="checkbox">Standard Training</th><th data-type="checkbox">Distributed Training</th><th data-type="checkbox">Inference</th><th data-type="checkbox">Distributed Inference</th><th data-type="checkbox">Workloads via Resource Interface</th></tr></thead><tbody><tr><td><a href="/pages/tNjUWUgHIXA7e2D5v7wk">Monitoring</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td>Workload awareness</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/5XOzJMboWNncJF7bDnSE#role-based-access-control-rbac-in-run-ai">RBAC</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td></tr><tr><td><a href="/pages/0wic8PWUMBWrabyuEaaj">Workload actions (stop/run)</a></td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td><td>false</td></tr><tr><td><a href="/pages/NTLneCwoNgVEZnpxndMt">Rolling updates</a></td><td>false</td><td>false</td><td>false</td><td>true</td><td>false</td><td>false</td></tr><tr><td><a href="/pages/xPQkkwU4zlWRc6tLGKMO">Workload templates</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td></tr><tr><td><a href="/pages/f7pWAI3BhuYplb5UpegP">Workload assets</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td><td>false</td></tr><tr><td><a href="/pages/W7PhY5YXTNDHx9MBxriH">Workload Policies</a></td><td>true</td><td>true</td><td>true</td><td>true</td><td>true</td><td>false</td></tr></tbody></table>

{% hint style="info" %}
**Workload awareness**

Specific workload-aware visibility, so that different pods are identified and treated as a single workload (for example GPU utilization, workload view, dashboards).
{% endhint %}

## Other Kubernetes Workloads

Other Kubernetes workloads can also be submitted via `kubectl`, receiving full monitoring and minimal scheduling capabilities.

* Supported scheduling capabilities include:
  * [Fairness](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#fairness-fair-resource-distribution)
  * [Priority and preemption](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#priority-and-preemption)
  * [Over quota](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#over-quota)
  * [Node pools](/self-hosted/2.23/platform-management/aiinitiatives/resources/node-pools.md)
  * [Bin packing / Spread](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#placement-strategy-bin-pack-and-spread)
  * [Multi-GPU fractions](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/fractions.md)
  * [Multi-GPU dynamic fractions](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/dynamic-fractions.md)
  * [Node level scheduler](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/node-level-scheduler.md)
  * [Multi-GPU memory swap](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/memory-swap.md)
  * [Gang scheduling](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#gang-scheduling)
* All [monitoring](/self-hosted/2.23/workloads-in-nvidia-run-ai/workloads.md#show-hide-details) capabilities are supported including event history, metrics and logs.


---

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