dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks.
Accelerators
dstack supports NVIDIA, AMD, Google TPU, and Tenstorrent accelerators out of the box.
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How does it work?
Launch the server
Before using dstack through CLI or API, set up a dstack server. If you already have a running dstack server, you only need to install the CLI.
To orchestrate compute across GPU clouds or Kubernetes clusters, you need to configure backends.
When using dstack with on-prem servers, backend configuration isn’t required. Simply create SSH fleets once the server is up.
The server can be installed on Linux, macOS, and Windows (via WSL 2). It requires Git and
OpenSSH.
$ uv tool install "dstack[all]" -U
$ dstack server
Applying ~/.dstack/server/config.yml...
The admin token is "bbae0f28-d3dd-4820-bf61-8f4bb40815da"
The server is running at http://127.0.0.1:3000/
For more details on server configuration options, see the
Server deployment guide.
Install the CLI
Once the server is up, you can access it via the dstack CLI.
The CLI can be installed on Linux, macOS, and Windows. It requires Git and OpenSSH.
$ uv tool install dstack -U
To point the CLI to the dstack server, configure it
with the server address, user token, and project name:
$ dstack project add \
--name main \
--url http://127.0.0.1:3000 \
--token bbae0f28-d3dd-4820-bf61-8f4bb40815da
Configuration is updated at ~/.dstack/config.yml
Install agent skills
Install dstack skills to help AI agents use the CLI and edit configuration files.
$ npx skills add dstackai/dstack
AI agents like Claude, Codex, and Cursor can now create and manage fleets and submit workloads on your behalf.
Define configurations
dstack supports the following configurations:
- Fleets — Provision and manage clusters across clouds, Kubernetes, and on-prem
- Dev environments — Launch dev environments to be accessed by agents or from your IDE
- Tasks — Run training, batch or other jobs across a single node or clusters
- Services — Deploy model inference as secure and scalable endpoints
- Presets — Agent-driven inference optimization (experimental)
- Volumes — Managing instance and network volumes for persisting data
Configuration can be defined as YAML files within your repo.
Apply configurations
Apply the configuration via the dstack apply CLI command, a programmatic API, or through AI agent skills.
dstack automatically manages provisioning, job queuing, auto-scaling, networking, volumes, run failures,
out-of-capacity errors, port-forwarding, and more — across clouds and on-prem clusters.
Useful links
For additional information, see the following links:
Contributing
You're very welcome to contribute to dstack.
Learn more about how to contribute to the project at CONTRIBUTING.md.
License
Mozilla Public License 2.0
