Orchestration automates provisioning, running jobs, and tearing them down. While Kubernetes and Slurm are powerful in their domains, they lack the lightweight, GPU-native focus modern teams need to move faster.
dstack is built entirely around GPUs. Our latest update introduces native integration with DigitalOcean and
AMD Developer Cloud , enabling teams to provision cloud GPUs and run workloads more cost-efficiently.
dstack services are long-running workloads—most often inference endpoints and sometimes web apps—that run continuously on GPU or CPU instances. They can scale across replicas and support rolling deployments.
This release adds HTTP probes inspired by Kubernetes readiness probes. Probes periodically call an endpoint on each replica (for example, /health) to confirm it responds as expected. The result gives clear visibility into startup progress and, during rolling deployments, ensures traffic only shifts to a replacement replica after all configured probes have proven ready.
In large-scale training, a single bad GPU can derail progress. Sometimes the failure is obvious — jobs crash outright. Other times it’s subtle: correctable memory errors, intermittent instability, or thermal throttling that quietly drags down throughput. In big experiments, these issues can go unnoticed for hours or days, wasting compute and delaying results.
dstack already supports GPU telemetry monitoring through NVIDIA DCGM metrics, covering utilization, memory, and temperature. This release extends that capability with passive hardware health checks powered by DCGM background health checks. With these, dstack continuously evaluates fleet GPUs for hardware reliability and displays their status before scheduling workloads.
As the ecosystem around AMD GPUs matures, developers are looking for easier ways to experiment with ROCm, benchmark new architectures, and run cost-effective workloads—without manual infrastructure setup.
dstack is an open-source orchestrator designed for AI workloads, providing a lightweight, container-native alternative to Kubernetes and Slurm.
Today, we’re excited to announce native integration with Hot Aisle , an AMD-only GPU neocloud offering VMs and clusters at highly competitive on-demand pricing.
Thanks to feedback from the community, dstack continues to evolve. Here’s a look at what’s new.
Previously, updating running services could cause downtime. The latest release fixes this with rolling deployments. Replicas are now updated one by one, allowing uninterrupted traffic during redeployments.
$ dstack apply -f .dstack.yml
Active run my-service already exists. Detected changes that can be updated in-place:
- Repo state (branch, commit, or other)
- File archives
- Configuration properties:
- env
- files
Update the run? [y/n]: y
⠋ Launching my-service...
NAME BACKEND PRICE STATUS SUBMITTED
my-service deployment=1 running 11 mins ago
replica=0 deployment=0 aws (us-west-2) $0.0026 terminating 11 mins ago
replica=1 deployment=1 aws (us-west-2) $0.0026 running 1 min ago
The latest update to dstack introduces support for NVIDIA GH200 instances on Lambda
and enables ARM-powered hosts, including GH200 and GB200, with SSH fleets.
As demand for GPU compute continues to scale, open-source tools tailored for AI workloads are becoming critical to
developer velocity and efficiency.
dstack is an open-source orchestrator purpose-built for AI infrastructure—offering a lightweight, container-native
alternative to Kubernetes and Slurm.
Today, we’re announcing native integration with Nebius , offering a streamlined developer experience for teams using GPUs for AI workloads.
AI workloads generate vast amounts of metrics, making it essential to have efficient monitoring tools. While our recent update introduced the ability to export available metrics to Prometheus for maximum flexibility, there are times when users need to quickly access essential metrics without the need to switch to an external tool.
Previously, we introduced a CLI command that allows users to view essential GPU metrics for both NVIDIA
and AMD hardware. Now, with this latest update, we’re excited to announce the addition of a built-in dashboard within
the dstack control plane.
As AI models grow in complexity, efficient orchestration tools become increasingly important.
Fleets introduced by dstack last year streamline
task execution on both cloud and
on-prem clusters, whether it's pre-training, fine-tuning, or batch processing.
The strength of dstack lies in its flexibility. Users can leverage distributed framework like
torchrun, accelerate, or others. dstack handles node provisioning, job execution, and automatically propagates
system environment variables—such as DSTACK_NODE_RANK, DSTACK_MASTER_NODE_IP,
DSTACK_GPUS_PER_NODE and others—to containers.
One use case dstack hasn’t supported until now is MPI, as it requires a scheduled environment or
direct SSH connections between containers. Since mpirun is essential for running NCCL/RCCL tests—crucial for large-scale
cluster usage—we’ve added support for it.
Effective AI infrastructure management requires full visibility into compute performance and costs. AI researchers need detailed insights into container- and GPU-level performance, while managers rely on cost metrics to track resource usage across projects.
While dstack provides key metrics through its UI and dstack metrics CLI, teams often need more granular data and prefer
using their own monitoring tools. To support this, we’ve introduced a new endpoint that allows real-time exporting all collected
metrics—covering fleets and runs—directly to Prometheus.
