This is a practical map for teams renting GPUs — whether you’re a single project team fine-tuning models or a production-scale team managing thousand-GPU workloads. We’ll break down where providers fit, what actually drives performance, how pricing really works, and how to design a control plane that makes multi-cloud not just possible, but a competitive advantage.
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.
This is the first in our series of benchmarks exploring the performance of AMD GPUs in virtualized versus bare-metal environments. As cloud infrastructure increasingly relies on virtualization, a key question arises: can VMs match bare-metal performance for GPU-intensive tasks? For this initial investigation, we focus specifically on a single-GPU setup, comparing a containerized workload on a VM against a bare-metal server, both equipped with the powerful AMD MI300X GPU.
Our new benchmark explores two important areas for optimizing AI workloads on AMD GPUs: First, do containers introduce a performance penalty for network-intensive tasks compared to a bare-metal setup? Second, how does partitioning a powerful GPU like the MI300X affect its real-world performance for different types of AI workloads?
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
At NVIDIA GTC 2025, Electronic Arts shared how they’re scaling AI development and managing infrastructure across teams. They highlighted using tools like dstack to provision GPUs quickly, flexibly, and cost-efficiently. This case study summarizes key insights from their talk.
EA has over 100+ AI projects running, and the number keeps growing. There are many teams with AI needs—game dev, ML engineers, AI researchers, and platform teams—supported by a central tech team. Some need full MLOps support; others have in-house expertise but need flexible tooling and infrastructure.
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.
