A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.
dstack is a control plane that provisions and coordinates GPU computing for machine-learning development, training, inference, and agent workloads across cloud providers, Kubernetes, and on-premises clusters. It is used by teams running workloads on NVIDIA, AMD, Google TPU, or Tenstorrent hardware. The catalogue add-ons operate dstack through its CLI, API, or server workflow.
This repository also configures its own agents. See what dstack tells them →
Skill Claude CodeCodex
Create and manage dstack presets: a toolkit that streamlines model inference optimization with agents, and a portable preset format. Use together with the dstack skill, and only when the user explicitly asks to create a preset or manage existing presets, not for deploying or serving a model.
Skill Claude CodeCodex
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final…
Skill Claude CodeCodex
Skill "dstack" from dstackai/dstack, covering dstack, how it works, quick agent flow (detached runs), agent execution guidelines and output accuracy.