Supporting Hot Aisle AMD AI Developer Cloud¶

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.

About Hot Aisle¶

Hot Aisle is a next-generation GPU cloud built around AMD’s flagship AI accelerators.

Highlights:

AMD’s flagship AI-optimized accelrators
On-demand pricing: $1.99/hour for 1-GPU VMs
No commitment – start and stop when you want
First AMD-only GPU backend in dstack

While it has already been possible to use HotAisle’s 8-GPU MI300X bare-metal clusters via SSH fleets, this integration now enables automated provisioning of VMs—made possible by HotAisle’s newly added API for MI300X instances.

Why dstack¶

dstack is a new open-source container orchestrator built specifically for GPU workloads.
It fills the gaps left by Kubernetes and Slurm when it comes to GPU provisioning and orchestration:

Unlike Kubernetes, dstack offers a high-level, AI-engineer-friendly interface, and GPUs work out of the box, with no need to wrangle custom operators, device plugins, or other low-level setup.
Unlike Slurm, it’s use-case agnostic — equally suited for training, inference, benchmarking, or even setting up long-running dev environments.
It works across clouds and on-prem without vendor lock-in.

With the new Hot Aisle backend, you can automatically provision MI300X VMs for any workload — from experiments to production — with a single dstack CLI command.

Getting started¶

Before configuring dstack to use Hot Aisle’s VMs, complete these steps:

Create a project via ssh admin.hotaisle.app
Get credits or approve a payment method
Create an API key

Then, configure the backend in ~/.dstack/server/config.yml:

projects:
- name: main
  backends:
    - type: hotaisle
      team_handle: hotaisle-team-handle
      creds:
        type: api_key
        api_key: 9c27a4bb7a8e472fae12ab34.3f2e3c1db75b9a0187fd2196c6b3e56d2b912e1c439ba08d89e7b6fcd4ef1d3f

Install and start the dstack server:

$ pip install "dstack[server]"
$ dstack server

For more details, see Installation.

Use the dstack CLI to manage dev environments, tasks, and services.

$ dstack apply -f .dstack.yml

 #  BACKEND                   RESOURCES                                     INSTANCE TYPE                     PRICE   
 1  hotaisle (us-michigan-1)  cpu=13 mem=224GB disk=12288GB MI300X:192GB:1  1x MI300X 13x Xeon Platinum 8470  $1.99
 2  hotaisle (us-michigan-1)  cpu=8 mem=224GB disk=12288GB MI300X:192GB:1   1x MI300X 8x Xeon Platinum 8470   $1.99

 Submit the run? [y/n]:

Currently, dstack supports 1xGPU Hot Aisle VMs. Support for 8xGPU VMs will be added once Hot Aisle supports it.

If you prefer to use Hot Aisle’s bare-metal 8-GPU clusters with dstack, you can create an SSH fleet. This way, you’ll be able to run distributed tasks efficiently across the cluster.

What's next?

Check Quickstart
Learn more about Hot Aisle
Explore dev environments, tasks, services, and fleets
Join Discord