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[Docs] Minor edits on the landing page, in README, and Quickstart (#3129)
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README.md

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`dstack` is an open-source container orchestrator that simplifies workload orchestration and drives GPU utilization for ML teams. It works with any GPU cloud, on-prem cluster, or accelerated hardware.
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`dstack` provides a unified control plane for running development, training, and inference on GPUs — across cloud VMs, Kubernetes, or on-prem clusters. It helps your team avoid vendor lock-in and reduce GPU costs.
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#### Accelerators
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`dstack` supports `NVIDIA`, `AMD`, `Google TPU`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.
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## Latest news ✨
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- [2025/09] [dstack 0.19.27: Offers UI, Digital Ocean and AMD Developer Cloud](https://github.com/dstackai/dstack/releases/tag/0.19.27)
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- [2025/08] [dstack 0.19.26: Repos – explicit repo configuration via YAML](https://github.com/dstackai/dstack/releases/tag/0.19.26)
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- [2025/08] [dstack 0.19.25: `dstack offer` CLI command](https://github.com/dstackai/dstack/releases/tag/0.19.25)
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- [2025/08] [dstack 0.19.22: Service probes, GPU health-checks, Tenstorrent Galaxy, Secrets UI](https://github.com/dstackai/dstack/releases/tag/0.19.22)
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- [2025/07] [dstack 0.19.21: Scheduled tasks](https://github.com/dstackai/dstack/releases/tag/0.19.21)
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- [2025/07] [dstack 0.19.17: Secrets, Files, Rolling deployment](https://github.com/dstackai/dstack/releases/tag/0.19.17)
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- [2025/06] [dstack 0.19.16: Docker in Docker, CloudRift](https://github.com/dstackai/dstack/releases/tag/0.19.16)
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- [2025/06] [dstack 0.19.13: InfiniBand support in default images](https://github.com/dstackai/dstack/releases/tag/0.19.13)
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- [2025/06] [dstack 0.19.12: Simplified use of MPI](https://github.com/dstackai/dstack/releases/tag/0.19.12)
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- [2025/05] [dstack 0.19.10: Priorities](https://github.com/dstackai/dstack/releases/tag/0.19.10)
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- [2025/05] [dstack 0.19.8: Nebius clusters, GH200 on Lambda](https://github.com/dstackai/dstack/releases/tag/0.19.8)
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- [2025/04] [dstack 0.19.6: Tenstorrent, Plugins](https://github.com/dstackai/dstack/releases/tag/0.19.6)
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## How does it work?
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v10-dark.svg"/>
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v10.svg" width="750" />
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<source media="(prefers-color-scheme: dark)" srcset="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v11-dark.svg"/>
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v11.svg" width="750" />
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</picture>
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### Installation

docs/assets/stylesheets/landing.css

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.tx-landing__hero_text h1 {
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margin-bottom: .75rem;
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margin-bottom: .6rem;
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font-weight: 700;
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font-size: 2.5em;
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/*letter-spacing: -3px;*/
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.md-header__buttons .md-button,
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.md-typeset .md-button {
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margin-top: 0.6rem;
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font-size: 19px;
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font-weight: 300 !important;

docs/docs/quickstart.md

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</div>
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## Run your first workload
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## Submit your first run
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`dstack` supports three types of workloads.
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`dstack` supports three types of run configurations.
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=== "Dev environment"
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docs/overrides/header-2.html

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<div class="md-header__buttons">
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<a href="https://github.com/dstackai/dstack" target="_blank" class="md-button md-button--primary github">GitHub</a>
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<a href="https://sky.dstack.ai" target="_blank" class="md-button md-button--primary sky external">Sign up</a>
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<a href="https://sky.dstack.ai" target="_blank" class="md-button md-button--primary sky">dstack Sky</a>
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</div>
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</nav>
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{% if "navigation.tabs.sticky" in features %}

docs/overrides/home.html

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<h1>The orchestration layer for modern ML teams</h1>
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<p>
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<span class="highlight">dstack</span> gives your team a single control plane to run development, training, and inference
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jobs on GPU—whether on hyperscalers, neoclouds, or your on-prem hardware. Avoid vendor lock-in and minimize GPU spend.
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<span class="highlight">dstack</span> provides a unified control plane for running development, training, and inference
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on GPUs — across cloud VMs, Kubernetes, or on-prem clusters. It helps your team avoid vendor lock-in and reduce GPU
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costs.
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</p>
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</div>
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</a>
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</div>
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</div>
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<div class="tx-landing__hero_button_container_text" style="text-align: center; font-size: 85%; margin-top: 25px;">
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The entire setup takes minutes.
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<div class="tx-landing__hero_button_container_text" style="text-align: center; font-size: 85%; margin-top: 1.25rem">
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Open-source self-hosted or fully-managed. Setup takes a few minutes.
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</div>
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</div>
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</div>
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v10.svg" />
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<img src="https://dstack.ai/static-assets/static-assets/images/dstack-architecture-diagram-v11.svg" />
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<h2>Run on any cloud. Or all of them.</h2>
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<h2>Native integration with GPU clouds</h2>
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<p>
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<span class="highlight">dstack</span> natively provisions GPU instances and clusters across your preferred cloud
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providers—maximizing efficiency and minimizing overhead.
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<span class="highlight">dstack</span> natively integrates with top GPU clouds—automating cluster provisioning and
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workload orchestration to maximize efficiency and minimize overhead.
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Connect GPU clouds directly to <span class="highlight">dstack</span> natively, or run <span class="highlight">dstack</span> on top of Kubernetes if needed.
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It can provision and manage VM clusters through native integrations or via <span class="highlight">Kuberenetes</span>.
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<a href="/docs/concepts/backends" target="_blank"
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<a href="/docs/concepts/backends" target="_blank" class="md-button md-button-secondary small">
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<span>Backends</span>
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<span class="icon"><svg viewBox="0 0 13 10" xmlns="http://www.w3.org/2000/svg"><path d="M12.823 4.164L8.954.182a.592.592 0 0 0-.854 0 .635.635 0 0 0 0 .88l2.836 2.92H.604A.614.614 0 0 0 0 4.604c0 .344.27.622.604.622h10.332L8.1 8.146a.635.635 0 0 0 0 .88.594.594 0 0 0 .854 0l3.869-3.982a.635.635 0 0 0 0-.88z" fill-rule="nonzero" fill="currentColor" class="fill-main"></path></svg></span>
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</a>
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<a href="/docs/concepts/backends#kubernetes" target="_blank" class="md-button md-button-secondary small">
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<span>Kubernetes</span>
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<path
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<h2>Bring your own compute</h2>
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<h2>Easy to use with on-prem clusters</h2>
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and manage everything alongside your cloud resources.
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For provisioned <span class="highlight">Kuberenetes</span> clusters, connect them to <span class="highlight">dstack</span> using the <span
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If you run vanilla bare-metal servers or VMs without <span class="highlight">Kuberenetes</span>, use <span class="highlight">SSH fleets</span>
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<span>Kubernetes</span>
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Either way, connecting existing on-prem clusters to <span class="highlight">dstack</span> takes just minutes.
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Before training or deployment, ML engineers explore and debug their code.
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<span>Clusters</span>
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Quickstart
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Already have an account? <a href="https://sky.dstack.ai" target="_blank"
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Get $5 free credit. Already have an account? <a href="https://sky.dstack.ai" target="_blank"
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