Introduction
dstack is an open-source orchestration platform for AI development that lets teams run training, fine-tuning, inference, and agentic workloads on any cloud, Kubernetes, or bare-metal infrastructure. It abstracts away compute provisioning so GPU resources from multiple providers and accelerators (NVIDIA, AMD, TPU, Tenstorrent) can be used through a single declarative interface.
Key Features
- Vendor-agnostic support for clouds, Kubernetes, and on-prem bare metal
- Unified provisioning across NVIDIA, AMD, TPU, and Tenstorrent accelerators
- Declarative run and task configuration for training, fine-tuning, and serving
- Built-in fleet and policy management for cost control and capacity sharing
- Drop-in acceleration for popular frameworks via containerized environments
Use Cases
- Provisioning elastic GPU capacity across multiple cloud providers
- Running distributed training and fine-tuning jobs without infra glue code
- Deploying and scaling inference services on Kubernetes or bare metal
- Centralizing compute policy and cost governance for ML teams
Technical Highlights
- Declarative YAML-based run definitions replace manual provisioning scripts
- Designed as an open alternative to vendor-specific GPU platforms
- Integrates with Kubernetes and Slurm-style scheduling workflows
- Python-native CLI and API for programmatic workload orchestration