Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.
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dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.
About dstack
dstack is an open-source orchestration layer for AI compute across GPU clouds, Kubernetes, VMs, and bare-metal or on-prem clusters. It uses a server accessed through a CLI, API, or AI agent skills to run dev environments, training and batch tasks, inference services, fleets, and volumes defined as YAML files.
It handles provisioning, job queuing, auto-scaling, networking, volumes, port-forwarding, run failures, and out-of-capacity errors. Use requires setting up a server with Git and OpenSSH, plus backend configuration for GPU clouds or Kubernetes; on-prem SSH fleets do not require backend configuration. Presets for inference optimization are experimental.
Key features
- Provision and manage fleets across clouds, Kubernetes, and on-prem
- Run training and batch tasks on single nodes or clusters
- Deploy model inference as scalable endpoints
- Launch dev environments for IDE or AI agent access
- Manage instance and network volumes for persisting data
- Define configurations as YAML files applied via CLI or API
Best for: Developers and teams running training and inference across mixed GPU clouds, Kubernetes, and on-prem hardware.
Good for
- Open source under Mozilla Public License 2.0
- Works across GPU clouds, Kubernetes, and on-prem clusters
- Manages provisioning, queuing, auto-scaling, and failure handling
Watch out for
- Requires server setup plus Git and OpenSSH for server and CLI
- Requires backend configuration for GPU clouds and Kubernetes
- Presets inference optimization is experimental
dstack at a glance
- Website
- github.com/dstackai/dstack
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Linux, macOS, Windows
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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dstack FAQ
What is needed to use dstack?
Set up a dstack server, install the CLI, and configure backends for GPU clouds or Kubernetes. On-prem use creates SSH fleets without backend configuration.
Which operating systems and requirements apply?
The server runs on Linux, macOS, and Windows via WSL 2. The CLI runs on Linux, macOS, and Windows. Both require Git and OpenSSH.
Which accelerators are supported?
NVIDIA, AMD, Google TPU, and Tenstorrent accelerators out of the box.
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