dstack vs TORQUE Resource Manager
dstack
dstack.aiTORQUE Resource Manager
github.comSide by side
| Figure | dstack | TORQUE Resource Manager |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · GPU Cluster Management Software | 8.9 (#1) | 7.5 (#12) |
| Integrations | Listed backend types include AWS, Azure, GCP, Kubernetes, Slurm, Runpod, Nebius, Lambda, and other cloud providers. | The maker's documentation describes integration between TORQUE and the Moab Workload Manager. |
| Purpose | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. | TORQUE is an open-source resource manager and queue manager based on the original PBS resource manager. |
| Support | The dstack Sky page directs users with questions or needing help to Discord or to contact the company directly. | The project README directs questions and contributions to the [email protected] mailing list. |
Plans
In their own words
dstack
dstack is an open-source orchestration layer for AI workloads across GPU clouds, Kubernetes, virtual machines, and bare-metal clusters. YAML configurations define fleets, development environments, tasks, services, presets, and volumes, while dstack handles provisioning and job scheduling, including auto-scaling, port forwarding, and ingress. Supported accelerators include NVIDIA, AMD, TPU, and Tenstorrent. Documented backends include AWS, Azure, GCP, Kubernetes, GPU cloud providers, remote SSH hosts, and experimental Slurm. Tasks can use frameworks such as accelerate, torchrun, Ray, and Spark. Users manage resources through the CLI or HTTP API, and the server can run on a laptop or another environment that can access the clusters in use. Services can publish inference endpoints through gateways with HTTPS, custom domains, auto-scaling, and rate limits. The self-hosted open-source stack is free. Server data and project secrets are stored in plaintext by default unless administrators configure AES-256-GCM encryption. TPU support is limited to single-host instances with up to eight cores. GPU Marketplace usage is prepaid, with prices that vary by GPU and provider and appear in the console before provisioning.
TORQUE Resource Manager
TORQUE Resource Manager is an open-source resource and queue manager for compute clusters, based on the original PBS resource manager. The project is free, with Linux, macOS, API and self-hosted options. TORQUE 4.0 release notes state that it can support clusters with more than 15,000 compute nodes; that release also introduced a multithreaded pbs_server intended to improve command response and job throughput. An optional MOM hierarchy can reduce update traffic from compute nodes to pbs_server and lets administrators manage network communication. In version 4.0, component communication moved to TCP/IP, removing UDP and RPP. The trqauthd daemon authorizes client connections to pbs_server and must run as root on client hosts. Linux builds can optionally use control groups and cpusets, and documentation describes integration with Moab Workload Manager. The project is built from source using configure and make; installation instructions also cover RPM and self-extracting package creation. Its license terms are in the repository's PBS_License.txt. A compatibility caveat for version 4.0 is that its components cannot communicate with earlier TORQUE versions, so rolling upgrades will not work.