docs: Add comprehensive GPU support documentation

openhands/usage/advanced/configuration-options.mdx

@@ -413,6 +413,59 @@ All sandbox configuration options can be set as environment variables by prefixi
   - Default: `""`
   - Description: BrowserGym environment to use for evaluation
 
+### GPU Support
+- `enable_gpu`
+  - Type: `bool`
+  - Default: `false`
+  - Description: Enable GPU support in the runtime container
+  - Note: Requires NVIDIA Container Toolkit (nvidia-docker2) installed on the host
+
+- `cuda_visible_devices`
+  - Type: `str`
+  - Default: `""`
+  - Description: Specify which GPU devices to make available to the container
+  - Examples:
+    - `""` (empty) - Mounts all available GPUs
+    - `"0"` - Mounts only GPU 0
+    - `"0,1"` - Mounts GPUs 0 and 1
+    - `"2,3,4"` - Mounts GPUs 2, 3, and 4
+
+**Example GPU Configuration:**
+```toml
+[sandbox]
+# Enable GPU support with all GPUs
+enable_gpu = true
+
+# Or enable GPU support with specific GPU IDs
+enable_gpu = true
+cuda_visible_devices = "0,1"
+
+# Use a CUDA-enabled base image for GPU workloads
+base_container_image = "nvidia/cuda:12.2.0-devel-ubuntu22.04"
+```
+
+**Prerequisites for GPU Support:**
+1. NVIDIA GPU with drivers installed on the host
+2. NVIDIA Container Toolkit (nvidia-docker2) installed:
+   ```bash
+   # For Ubuntu/Debian
+   distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
+   curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
+   curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
+       sudo tee /etc/apt/sources.list.d/nvidia-docker.list
+   sudo apt-get update && sudo apt-get install -y nvidia-docker2
+   sudo systemctl restart docker
+   ```
+
+**Verifying GPU Access:**
+
+After enabling GPU support, you can verify GPU access in OpenHands by asking the agent to run:
+```bash
+nvidia-smi
+```
+
+This should display your GPU information if GPU support is properly configured.
+
 ## Security Configuration
 
 The security configuration options are defined in the `[security]` section of the `config.toml` file.

openhands/usage/environment-variables.mdx

@@ -112,8 +112,8 @@ These variables correspond to the `[sandbox]` section in `config.toml`:
 | `SANDBOX_PAUSE_CLOSED_RUNTIMES` | boolean | `false` | Pause instead of stopping closed runtimes |
 | `SANDBOX_CLOSE_DELAY` | integer | `300` | Delay before closing idle runtimes (seconds) |
 | `SANDBOX_RM_ALL_CONTAINERS` | boolean | `false` | Remove all containers when stopping |
-| `SANDBOX_ENABLE_GPU` | boolean | `false` | Enable GPU support |
-| `SANDBOX_CUDA_VISIBLE_DEVICES` | string | `""` | Specify GPU devices by ID |
+| `SANDBOX_ENABLE_GPU` | boolean | `false` | Enable GPU support (requires NVIDIA Container Toolkit) |
+| `SANDBOX_CUDA_VISIBLE_DEVICES` | string | `""` | Specify GPU devices by ID (e.g., `"0"`, `"0,1"`, `"2,3,4"`). Empty string mounts all GPUs |
 | `SANDBOX_VSCODE_PORT` | integer | auto | Specific port for VSCode server |
 
 ### Sandbox Environment Variables

sdk/guides/agent-server/docker-sandbox.mdx

@@ -605,6 +605,49 @@ http://localhost:8012/vnc.html?autoconnect=1&resize=remote
 
 ---
 
+## 4) GPU Support in Docker Sandbox
+
+<Note>
+GPU support requires NVIDIA Container Toolkit (nvidia-docker2) to be installed on the host system.
+</Note>
+
+The Docker sandbox supports GPU acceleration for compute-intensive tasks like machine learning, data processing, and GPU-accelerated applications. Enable GPU support by setting the `enable_gpu` parameter when creating a `DockerWorkspace` or `DockerDevWorkspace`.
+
+### Basic GPU Configuration
+
+```python
+from openhands.workspace import DockerWorkspace
+
+with DockerWorkspace(
+    server_image="ghcr.io/openhands/agent-server:latest-python",
+    host_port=8010,
+    platform="linux/amd64",
+    enable_gpu=True,  # Enable GPU support
+) as workspace:
+    # GPU is now available in the workspace
+    result = workspace.execute_command("nvidia-smi")
+    print(result.stdout)
+```
+
+### GPU with Custom Base Image
+
+When using GPU-accelerated workloads, you may want to use a CUDA-enabled base image:
+
+```python
+from openhands.workspace import DockerDevWorkspace
+
+with DockerDevWorkspace(
+    base_image="nvidia/cuda:12.2.0-devel-ubuntu22.04",
+    host_port=8010,
+    platform="linux/amd64",
+    enable_gpu=True,
+    target="source",
+) as workspace:
+    # Workspace has CUDA toolkit and GPU access
+    result = workspace.execute_command("nvcc --version && nvidia-smi")
+    print(result.stdout)
+```
+
 ## Next Steps
 
 - **[Local Agent Server](/sdk/guides/agent-server/local-server)**