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[Blog] Nebius joins dstack Sky GPU marketplace, first to support clusters (#3113)
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‎docs/blog/posts/nebius-in-dstack-sky.md‎

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---
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title: Nebius joins dstack Sky GPU marketplace, with production-ready GPU clusters
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date: 2025-09-18
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description: "TBA"
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slug: nebius-in-dstack-sky
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image: https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius.png
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categories:
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- Changelog
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---
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# Nebius joins dstack Sky GPU marketplace, with production-ready GPU clusters
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`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.
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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
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to offer on-demand and spot GPUs, including clusters.
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius.png" width="630"/>
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<!-- more -->
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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.
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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.
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Since early this year, the open-source `dstack` has supported Nebius, making it easy to manage clusters and orchestrate compute cost-effectively.
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## About dstack Sky
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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.
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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.
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-nebius-offers.png" width="750"/>
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While the open-source version of `dstack` has supported Nebius clusters from day one,
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Nebius is the first provider to bring on-demand and spot GPU clusters to `dstack` Sky.
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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.
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## Getting started
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After you [sign up :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"} with `dstack` Sky,
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you’ll be prompted to create a project and choose between the GPU marketplace or your own cloud account:
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-sky-project-wizard.png" width="750"/>
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Once the project is created, install the `dstack` CLI:
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=== "uv"
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<div class="termy">
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```shell
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$ uv tool install dstack -U
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```
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</div>
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=== "pip"
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<div class="termy">
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```shell
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$ pip install dstack -U
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```
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</div>
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Now you can define [dev environments](../../docs/concepts/dev-environments.md),
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[tasks](../../docs/concepts/tasks.md), [services](../../docs/concepts/services.md),
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and [fleets](../../docs/concepts/fleets.md), then apply them with `dstack apply`.
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`dstack` provisions cloud VMs, sets up environments, orchestrates runs, and handles everything required for development, training, or deployment.
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To create a Nebius cluster, for example for distributed training, define the following fleet configuration:
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<div editor-title="my-cluster.dstack.yml">
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```yaml
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type: fleet
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name: my-cluster
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placement: cluster
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nodes: 2
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backends: [nebius]
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resources:
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gpu: H100:8
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```
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</div>
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Then, create it via `dstack apply`:
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<div class="termy">
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```shell
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$ dstack apply -f my-cluster.dstack.yml
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```
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</div>
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Once the fleet is ready, you can run [distributed tasks](../../docs/concepts/tasks.md#distributed-tasks).
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`dstack` automatically configures drivers, networking, and fast GPU-to-GPU interconnect.
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To learn more, see the [clusters](../../docs/guides/clusters.md) guide.
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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.
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> If you prefer to go self-hosted, you can always switch to the open-source version of `dstack`, bringing the same functionality.
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Our goal is to give teams maximum flexibility while removing the complexity of managing infrastructure. More updates are coming soon.
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!!! info "How does `dstack` compare to Kubernetes?"
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`dstack` can run either on top of Kubernetes or directly on cloud VMs.
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In both cases, you don’t need to manage Kubernetes yourself — `dstack` handles container and GPU orchestration,
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providing a simple, multi-cloud interface for development, training, and inference.
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!!! info "What's next"
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1. Sign up with [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}
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2. Check [Quickstart](../../docs/quickstart.md)
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3. Learn more about [Nebius :material-arrow-top-right-thin:{ .external }](https://nebius.com/){:target="_blank"}
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4. Explore [dev environments](../../docs/concepts/dev-environments.md),
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[tasks](../../docs/concepts/tasks.md), [services](../../docs/concepts/services.md),
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and [fleets](../../docs/concepts/fleets.md)
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5. Reaad the the [clusters](../../docs/guides/clusters.md) guide

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