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127 changes: 127 additions & 0 deletions docs/blog/posts/nebius-in-dstack-sky.md
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---
title: Nebius joins dstack Sky GPU marketplace, with production-ready GPU clusters
date: 2025-09-18
description: "TBA"
slug: nebius-in-dstack-sky
image: https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius.png
categories:
- Changelog
---

# Nebius joins dstack Sky GPU marketplace, with production-ready GPU clusters

`dstack` is an [open-source :material-arrow-top-right-thin:{ .external }](https://github.com/dstackai/dstack){:target="_blank"} control plane for orchestrating GPU workloads. It can provision cloud VMs, run on top of Kubernetes, or manage on-prem clusters. If you don’t want to self-host, you can use [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}, the managed version of `dstack` that also provides access to cloud GPUs via its markfetplace.

With our latest release, we’re excited to announce that [Nebius :material-arrow-top-right-thin:{ .external }](https://nebius.com/){:target="_blank"}, a purpose-built AI cloud for large scale training and inference, has joined the `dstack` Sky marketplace
to offer on-demand and spot GPUs, including clusters.

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius.png" width="630"/>
<!-- more -->

Last week we published the [state of cloud GPU](state-of-cloud-gpu-2025.md), a study of the GPU market. As noted there, Nebius is one of the few purpose-built AI clouds delivering performant and resilient GPUs at scale — available on-demand, as spot instances, and as full clusters.

Nebius designs and operates its own GPU servers in energy-efficient data centers, giving full control over quality, performance tuning, and delivery timelines. Every cluster undergoes a three-stage validation — hardware burn-in, reference architecture checks, and long-haul stress tests — ensuring production-ready infrastructure with consistent performance for large-scale AI training.

Since early this year, the open-source `dstack` has supported Nebius, making it easy to manage clusters and orchestrate compute cost-effectively.

## About dstack Sky

With this week's release, Nebius officially joins [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}. Nebius can now be used not only with your own account, but also directly via the GPU marketplace.

The marketplace lets you access Nebius GPUs without having a Nebius account. You can pay through `dstack Sky`, and switch to your own Nebius account anytime with just a few clicks.

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius-offers.png" width="750"/>

While the open-source version of `dstack` has supported Nebius clusters from day one,
Nebius is the first provider to bring on-demand and spot GPU clusters to `dstack` Sky.

With Nebius, `dstack` Sky users can orchestrate NVIDIA GPUs provisioned in hours, with optimized InfiniBand networking to minimize bottlenecks, non-virtualized GPUs for predictable throughput, and industry-leading MTBF/MTTR proven on multi-thousand-GPU clusters.

## Getting started

After you [sign up :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"} with `dstack` Sky,
you’ll be prompted to create a project and choose between the GPU marketplace or your own cloud account:

<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-project-wizard.png" width="750"/>

Once the project is created, install the `dstack` CLI:

=== "uv"

<div class="termy">

```shell
$ uv tool install dstack -U
```

</div>

=== "pip"

<div class="termy">

```shell
$ pip install dstack -U
```

</div>

Now you can define [dev environments](../../docs/concepts/dev-environments.md),
[tasks](../../docs/concepts/tasks.md), [services](../../docs/concepts/services.md),
and [fleets](../../docs/concepts/fleets.md), then apply them with `dstack apply`.

`dstack` provisions cloud VMs, sets up environments, orchestrates runs, and handles everything required for development, training, or deployment.

To create a Nebius cluster, for example for distributed training, define the following fleet configuration:

<div editor-title="my-cluster.dstack.yml">

```yaml
type: fleet
name: my-cluster

placement: cluster
nodes: 2

backends: [nebius]

resources:
gpu: H100:8
```

</div>

Then, create it via `dstack apply`:

<div class="termy">

```shell
$ dstack apply -f my-cluster.dstack.yml
```

</div>

Once the fleet is ready, you can run [distributed tasks](../../docs/concepts/tasks.md#distributed-tasks).
`dstack` automatically configures drivers, networking, and fast GPU-to-GPU interconnect.

To learn more, see the [clusters](../../docs/guides/clusters.md) guide.

With Nebius joining `dstack` Sky, users can now run on-demand and spot GPUs and clusters directly through the marketplace—gaining access to the same production grade infrastrucure Nebius customers use for frontier-scale training, without needing a separate Nebius account.

> If you prefer to go self-hosted, you can always switch to the open-source version of `dstack`, bringing the same functionality.

Our goal is to give teams maximum flexibility while removing the complexity of managing infrastructure. More updates are coming soon.

!!! info "How does `dstack` compare to Kubernetes?"
`dstack` can run either on top of Kubernetes or directly on cloud VMs.
In both cases, you don’t need to manage Kubernetes yourself — `dstack` handles container and GPU orchestration,
providing a simple, multi-cloud interface for development, training, and inference.

!!! info "What's next"
1. Sign up with [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}
2. Check [Quickstart](../../docs/quickstart.md)
3. Learn more about [Nebius :material-arrow-top-right-thin:{ .external }](https://nebius.com/){:target="_blank"}
4. Explore [dev environments](../../docs/concepts/dev-environments.md),
[tasks](../../docs/concepts/tasks.md), [services](../../docs/concepts/services.md),
and [fleets](../../docs/concepts/fleets.md)
5. Reaad the the [clusters](../../docs/guides/clusters.md) guide
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