Infrastructure orchestration is becoming an agent skill. When agents run experiments, training, and eval autonomously, they now can provision compute, schedule workloads, and track state, all inside the loop. New post on what this means for platform teams, providers, and the orchestration layer dstack.ai/blog/agentic-orche…

dstack 0.20.24 🚀 * You can now use @zeddotdev with dev environments; handy for experiments when you need a GPU * Services support gRPC with prefill and decode workers (both vLLM and SGLang) when @lightseekorg SMG is used as the router * @jarvislabsai backend now supports on-demand RTX PRO 6000 github.com/dstackai/dstack/r…

dstack is now in the official @jarvislabsai docs 🙌 docs.jarvislabs.ai/dstack/ Orchestration is becoming the default way GPUs get used. Thank you, @jarvislabsai for the integration! If you're looking for on-demand GPUs, they offer H100, L4, H200, RTX PRO 6000.

Native @NVIDIA Dynamo support is now in dstack. Deploy high-throughput inference with PD disaggregation, without custom orchestration glue. Works with SGLang, vLLM, and TensorRT-LLM, across GPU clouds, Kubernetes, and on-prem fleets. Another step toward simpler AI-native orchestration for production inference: dstack.ai/blog/nvidia-dynamo…

new drop 🔥 native @NVIDIAAI Dynamo support is now in @dstackai. deploy high-throughput inference with PD disaggregation, without custom orchestration glue. works with @sgl_project, @vllm_project, and TensorRT-LLM, across GPU clouds, @kubernetesio, and on-prem fleets. another step toward simpler AI-native orchestration for production inference: dstack.ai/blog/nvidia-dynamo…

JarvisLabs published a tutorial on using dstack with their on-demand GPUs 🚀 Define training and inference workloads, run dstack apply, and dstack handles provisioning, scheduling, and execution. Works across L4, H100, H200, and CPU VMs. RTX PRO 6000 is coming soon too. The tutorial walks through the full lifecycle, from connecting your account to running workloads. Thanks to the @jarvislabsai team for putting it together. jarvislabs.ai/blog/dstack-ja…

The community asked us for an example of how to use @radixark Miles with dstack for RL training. Since Miles uses Ray and dstack can run Ray, using Miles with dstack is quite straightforward. Here’s a new example of running Miles on a multi-node cluster provisioned and managed by dstack: dstack.ai/docs/examples/trai…

dstack 0.20.22 is out 🚀 Tenstorrent Blackhole support has landed. Run dev, training, and inference workloads on Blackhole PCIe cards and systems. Thanks to the @tenstorrent team and @artem_aero for collaborating on this. github.com/dstackai/dstack/r…

1 for dstack. Fantastic tool if you have heterogeneous compute across different clusters or clouds

Most GPU workflows come with too much infra overhead. Spin up a VM, install dependencies, copy code over, expose ports, watch logs, and remember to shut everything down after. With JarvisLabs now available as a @dstackai backend, most of that goes away. You define the machine and the workload in a YAML file. Run dstack apply. That's it. dstack manages the infrastructure lifecycle. JarvisLabs provides the GPUs. We wrote a short tutorial covering the full setup, from connecting your account to running a nanochat training job on H100s Works for training runs, evals, benchmarks, inference services, and GPU dev environments.

Training models or serving inference on AMD GPUs? We’ve refreshed the AMD accelerator example in the dstack docs, covering on-prem fleets, cloud GPU provisioning, dev environments, training jobs, and production-grade inference. dstack.ai/docs/examples/acce…

Thanks @dstackai for this end-to-end example using Miles with dstack for RL training. Try it and let us know what you build. 👇

dstack 0.20.21 is out 🚀 ⚡ Kubernetes: multiple clusters via kubeconfig contexts, each exposed as a backend region ⚡ New @jarvislabsai backend: H100/H200 on demand now; RTX PRO 6000 to follow github.com/dstackai/dstack/r…