[Blog] Orchestrating GPUs on DigitalOcean and AMD Developer Cloud

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docs/blog/posts/digitalocean-and-amd-dev-cloud.md

@@ -0,0 +1,151 @@
+---
+title: Orchestrating GPUs on DigitalOcean and AMD Developer Cloud
+date: 2025-09-04
+description: "TBA"  
+slug: digitalocean-and-amd-dev-cloud
+image: https://dstack.ai/static-assets/static-assets/images/digitalocean-and-amd-dev-cloud.png
+categories:
+  - Changelog
+---
+
+# Orchestrating GPUs on DigitalOcean and AMD Developer Cloud
+
+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 :material-arrow-top-right-thin:{ .external }](https://www.digitalocean.com/products/gradient/gpu-droplets){:target="_blank"} and 
+[AMD Developer Cloud :material-arrow-top-right-thin:{ .external }](https://www.amd.com/en/developer/resources/cloud-access/amd-developer-cloud.html){:target="_blank"}, enabling teams to provision cloud GPUs and run workloads more cost-efficiently.
+
+<img src="https://dstack.ai/static-assets/static-assets/images/digitalocean-and-amd-dev-cloud.png" width="630"/>
+
+<!-- more -->
+
+## About Digital Ocean
+
+DigitalOcean is one of the leading cloud platforms offering GPUs both as VMs and as bare-metal clusters equipped with NVIDIA and AMD GPUs.
+
+## About AMD Developer Cloud
+
+AMD Developer Cloud is a new cloud platform designed to make AMD GPUs easily accessible to developers, academics, open-source contributors, and AI innovators worldwide.
+
+## Why dstack
+
+Unlike Kubernetes, dstack provides a high-level, AI-engineer-friendly interface where GPUs work out of the box—no custom operators or low-level setup required. Unlike Slurm, it’s use-case agnostic, equally suited for training, inference, benchmarking, and dev environments.
+
+With the new DigitalOcean and AMD Developer Cloud backends, you can now provision NVIDIA or AMD GPU VMs and run workloads with a single CLI command.
+
+## Getting started
+
+Best part about `dstack` is that it's very easy to get started.
+
+1. Create a project in Digital Ocean or AMD Developer Cloud
+2. Get credits or approve a payment method
+3. Create an API key
+
+Then, configure the backend in `~/.dstack/server/config.yml`:
+
+<div editor-title="~/.dstack/server/config.yml">
+
+```yaml
+projects:
+- name: main
+  backends:
+    - type: amddevcloud
+      project_name: my-amd-project
+      creds:
+        type: api_key
+        api_key: ...
+```
+
+</div>
+
+For DigitalOcean, set `type` to `digitalocean`.
+
+Install and start the `dstack` server:
+
+<div class="termy">
+
+```shell
+$ pip install "dstack[server]"
+$ dstack server
+```
+
+</div>
+
+For more details, see [Installation](../../docs/installation/index.md).
+
+Use the `dstack` CLI to
+manage [dev environments](../../docs/concepts/dev-environments.md), [tasks](../../docs/concepts/tasks.md),
+and [services](../../docs/concepts/services.md).
+
+<img src="https://dstack.ai/static-assets/static-assets/images/amd-dev-cloud.png" width="750"/>
+
+The `digitalocean` and `amddevcloud` backends support NVIDIA and AMD GPU VMs, respectively, and allow you to run 
+[dev environments](../../docs/concepts/dev-environments.md) (interactive development), [tasks](../../docs/concepts/tasks.md) 
+(training, fine-tuning, or other batch jobs), and [services](../../docs/concepts/services.md) (inference).
+
+Here’s an example of a service configuration:
+
+<div editor-title="examples/modes/gpt-oss/120b.dstack.yml">
+
+```yaml
+type: service
+name: gpt-oss-120b
+
+model: openai/gpt-oss-120b
+
+env:
+  - HF_TOKEN
+  - MODEL=openai/gpt-oss-120b
+  # To enable AITER, set below to 1. Otherwise, set it to 0.
+  - VLLM_ROCM_USE_AITER=1
+  # To enable AITER Triton unified attention
+  - VLLM_USE_AITER_UNIFIED_ATTENTION=1
+  # below is required in order to enable AITER unified attention by disabling AITER MHA
+  - VLLM_ROCM_USE_AITER_MHA=0
+image: rocm/vllm-dev:open-mi300-08052025
+commands:
+  - |
+    vllm serve $MODEL \
+      --tensor-parallel $DSTACK_GPUS_NUM \
+      --no-enable-prefix-caching \
+      --disable-log-requests \
+      --compilation-config '{"full_cuda_graph": true}'
+port: 8000
+
+volumes:
+  # Cache downloaded models
+  - /root/.cache/huggingface:/root/.cache/huggingface
+
+resources:
+  gpu: MI300X:8
+  shm_size: 32GB
+```
+
+</div>
+
+As with any configuration, you can apply it via `dstack apply`. If needed, `dstack` will automatically provision new VMs and run the inference endpoint.
+
+<div class="termy">
+
+```shell
+$ dstack apply -f examples/models/gpt-oss/120b.dstack.yml
+
+ #  BACKEND             RESOURCES                                   PRICE   
+ 1  amddevcloud (alt1)  cpu=20 mem=240GB disk=720GB MI300X:192GB:8  $15.92
+
+ Submit the run? [y/n]:
+```
+
+</div>
+
+> If you prefer to use bare-metal clusters with `dstack`, you can create an [SSH fleet](../../docs/concepts/fleets.md#ssh-fleets).
+> This way, you’ll be able to run [distributed tasks](../../docs/concepts/tasks.md#distributed-tasks) efficiently across the cluster.
+
+!!! info "What's next?"
+    1. Check [Quickstart](../../docs/quickstart.md)
+    2. Learn more about [DigitalOcean :material-arrow-top-right-thin:{ .external }](https://www.digitalocean.com/products/gradient/gpu-droplets){:target="_blank"} and
+         [AMD Developer Cloud :material-arrow-top-right-thin:{ .external }](https://www.amd.com/en/developer/resources/cloud-access/amd-developer-cloud.html){:target="_blank"}
+    3. Explore [dev environments](../../docs/concepts/dev-environments.md), 
+        [tasks](../../docs/concepts/tasks.md), [services](../../docs/concepts/services.md), 
+        and [fleets](../../docs/concepts/fleets.md)
+    4. Join [Discord :material-arrow-top-right-thin:{ .external }](https://discord.gg/u8SmfwPpMd){:target="_blank"}

mkdocs.yml

@@ -294,11 +294,10 @@ nav:
         - Tenstorrent: examples/accelerators/tenstorrent/index.md
     - Models:
         - Wan2.2: examples/models/wan22/index.md
-  - Changelog: blog/changelog.md
-  - Case studies: blog/case-studies.md
-  - Benchmarks: blog/benchmarks.md
   - Blog: 
     - blog/index.md
+  - Case studies: blog/case-studies.md
+  - Benchmarks: blog/benchmarks.md
 #  - Discord: https://discord.gg/u8SmfwPpMd" target="_blank
 #  - Changelog: https://github.com/dstackai/dstack/releases" target="_blank
 #  - GitHub: https://github.com/dstackai/dstack" target="_blank