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.
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.
Dev environments enable seamless provisioning of remote instances with the necessary GPU resources, automatic repository fetching, and streamlined access via SSH or a preferred desktop IDE.
Previously, support was limited to VS Code. However, as developers rely on a variety of desktop IDEs, we’ve expanded compatibility. With this update, dev environments now offer effortless access for users of Cursor .
DeepSeek-R1, with its innovative architecture combining Multi-head Latent Attention (MLA) and DeepSeekMoE, presents unique challenges for inference workloads. As a reasoning-focused model, it generates intermediate chain-of-thought outputs, placing significant demands on memory capacity and bandwidth.
In this benchmark, we evaluate the performance of three inference backends—SGLang, vLLM, and TensorRT-LLM—on two hardware configurations: 8x NVIDIA H200 and 8x AMD MI300X. Our goal is to compare throughput, latency, and overall efficiency to determine the optimal backend and hardware pairing for DeepSeek-R1's demanding requirements.
This benchmark was made possible through the generous support of our partners at Vultr and Lambda , who provided access to the necessary hardware.