[Docs] Improve Kubernetes documentation

Updated `README`, `Overview`, `Installation`

Author: peterschmidt85

README.md

@@ -14,9 +14,11 @@
 
 </div>
 
-`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.
+`dstack` is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters. 
 
-#### Accelerators
+It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks.
+
+#### Hardware
 
 `dstack` supports `NVIDIA`, `AMD`, `Google TPU`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.
 
@@ -44,15 +46,15 @@
 
 #### Set up the server
 
-##### (Optional) Configure backends
+##### Configure backends
+
+To orchestrate compute across cloud providers or existing Kubernetes clusters, you need to configure backends.
 
-To use `dstack` with cloud providers, configure backends
-via the `~/.dstack/server/config.yml` file.
+Backends can be set up in `~/.dstack/server/config.yml` or through the [project settings page](../concepts/projects.md#backends) in the UI.
 
-For more details on how to configure backends, check [Backends](https://dstack.ai/docs/concepts/backends).
+For more details, see [Backends](../concepts/backends.md).
 
-> For using `dstack` with on-prem servers, create [SSH fleets](https://dstack.ai/docs/concepts/fleets#ssh) 
-> once the server is up.
+> When using `dstack` with on-prem servers, backend configuration isn’t required. Simply create [SSH fleets](../concepts/fleets.md#ssh) once the server is up.
 
 ##### Start the server
 

docs/docs/concepts/backends.md

@@ -1080,9 +1080,9 @@ However, often [SSH fleets](../concepts/fleets.md#ssh) are a simpler and lighter
 ### SSH fleets
 
 SSH fleets require no backend configuration. 
-All you need to do is [provide hostnames and SSH credentials](../concepts/fleets.md#ss), and `dstack` sets up a fleet that can orchestrate container-based runs on your servers.
+All you need to do is [provide hostnames and SSH credentials](../concepts/fleets.md#ssh), and `dstack` sets up a fleet that can orchestrate container-based runs on your servers.
 
-> SSH fleets support the same features as [VM-based](#vm-based) backends.
+SSH fleets support the same features as [VM-based](#vm-based) backends.
 
 !!! info "What's next"
     1. See the [`~/.dstack/server/config.yml`](../reference/server/config.yml.md) reference

docs/docs/index.md

@@ -1,9 +1,10 @@
 # What is dstack?
 
-`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.
+`dstack` is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters. 
 
-#### Accelerators
+It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks.
+
+#### Hardware
 
 `dstack` supports `NVIDIA`, `AMD`, `TPU`, `Intel Gaudi`, and `Tenstorrent` accelerators out of the box.
 

docs/docs/installation/index.md

@@ -1,20 +1,21 @@
 # Installation
 
-> If you don't want to host the `dstack` server (or want to access GPU marketplace),
-> skip installation and proceed to [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}.
+!!! info "dstack Sky"
+    If you don't want to host the `dstack` server (or want to access GPU marketplace),
+    skip installation and proceed to [dstack Sky :material-arrow-top-right-thin:{ .external }](https://sky.dstack.ai){:target="_blank"}.
 
 ## Set up the server
 
-### (Optional) Configure backends
+### Configure backends
 
-Backends allow `dstack` to manage compute across various providers.
-They can be configured via `~/.dstack/server/config.yml` (or through the [project settings page](../concepts/projects.md#backends) in the UI).
+To orchestrate compute across cloud providers or existing Kubernetes clusters, you need to configure backends.
 
-For more details on how to configure backends, check [Backends](../concepts/backends.md).
+Backends can be set up in `~/.dstack/server/config.yml` or through the [project settings page](../concepts/projects.md#backends) in the UI.
+
+For more details, see [Backends](../concepts/backends.md).
 
 ??? info "SSH fleets"
-    For using `dstack` with on-prem servers, create [SSH fleets](../concepts/fleets.md#ssh) 
-    once the server is up.
+    When using `dstack` with on-prem servers, backend configuration isn’t required. Simply create [SSH fleets](../concepts/fleets.md#ssh) once the server is up.
 
 ### Start the server
 

docs/overrides/home.html

@@ -53,9 +53,9 @@
                 <h1>The orchestration layer for modern ML teams</h1>
 
                 <p>
-                    <span class="highlight">dstack</span> 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.
+                    <span class="highlight">dstack</span> provides ML teams with a unified control plane for GPU provisioning and orchestration
+                    across cloud, Kubernetes, and on-prem. It streamlines development, training, and inference — reducing costs 3–7x and
+                    preventing lock-in.
                 </p>
             </div>
 
@@ -83,14 +83,15 @@ <h1>The orchestration layer for modern ML teams</h1>
         <div class="tx-landing__major_feature">
             <div class="section">
                 <div class="block margin right">
-                    <h2>One control plane for all your GPUs</h2>
+                    <h2>An open platform for GPU orchestration</h2>
                     <p>
-                        Instead of wrestling with complex Helm charts and Kubernetes operators, <span class="highlight">dstack</span> provides a simple, declarative way to
-                        manage clusters, containerized dev environments, training, and inference.
+                        Managing AI infrastructure requires efficient GPU orchestration, whether workloads run
+                        on a single GPU cloud, across multiple GPU providers, or on-prem clusters.
                     </p>
 
-                    <p>This container-native interface makes your team more productive and your GPU usage more efficient—leading to lower
-                        costs and faster iteration.
+                    <p>
+                        <span class="highlight">dstack</span> provides an open stack for GPU orchestration that streamlines development, training,
+                        and inference, and can be used with any hardware, open-source tools, and frameworks.
                     </p>
 
                     <!-- TODO: Add `Why dstack?` -->
@@ -219,7 +220,7 @@ <h2>Easy to use with on-prem clusters</h2>
                                 </svg></span>
                         </a>
 
-                        <a href="/docs/concepts/fleets#ssh" target="_blank" class="md-button md-button-secondary small">
+                        <a href="/docs/concepts/backends#ssh-fleets" target="_blank" class="md-button md-button-secondary small">
                             <span>SSH fleets</span>
                             <span class="icon"><svg viewBox="0 0 13 10" xmlns="http://www.w3.org/2000/svg">
                                     <path