> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phala.com/llms.txt
> Use this file to discover all available pages before exploring further.

> Deploy and manage worker nodes to provide computing power on Phala Network.

# Phala GPU Mining Deployment Guide (H100 / H200 / B200)

This document is a step-by-step guide for community members to deploy **Phala GPU Mining** on their own servers.

Currently supported GPU models:

* **NVIDIA H100**
* **NVIDIA H200**
* **NVIDIA B200**

Deployment is divided into **two major stages**:

1. **Enable TDX in BIOS, configure the system environment, and install Dstack (with GPU stack enabled)**
2. **Run the Phala GPU Mining service inside the server via Docker and connect to Phala Cloud**

## 1. Enable TDX & Install Dstack

### 1.1 Enable TDX in BIOS

Before installing or booting the OS, enter the server BIOS and enable:

* Intel TDX
* Intel VT-x / VT-d
* SR-IOV (if available)
* Any required CPU virtualization / security features (names may vary by vendor)

> Different hardware vendors may use different BIOS names. Please refer to your server’s manual.

### 1.2 Configure TDX System Environment

Follow Intel’s / Dstack’s official instructions to configure the TDX system environment:

* Dstack repo & basic setup:\
  **[https://github.com/Dstack-TEE/dstack](https://github.com/Dstack-TEE/dstack)**

If you encounter errors related to **Intel modules** (for example, TDX module installation failures), refer to Intel’s enabling guide:

* Intel TDX hardware setup guide:\
  **[https://cc-enabling.trustedservices.intel.com/intel-tdx-enabling-guide/04/hardware\_setup/](https://cc-enabling.trustedservices.intel.com/intel-tdx-enabling-guide/04/hardware_setup/)**

Make sure all required kernel modules and firmware settings for TDX are correctly configured before continuing.

### 1.3 Upgrade NVIDIA Driver to Latest Version

Phala GPU Mining relies on NVIDIA Confidential Computing–capable GPUs and drivers.\
Please upgrade your NVIDIA driver to the latest supported version and verify compatibility:

* NVIDIA Confidential Computing compatibility matrix:\
  **[https://www.nvidia.com/en-us/data-center/solutions/confidential-computing/secure-ai-compatibility-matrix/](https://www.nvidia.com/en-us/data-center/solutions/confidential-computing/secure-ai-compatibility-matrix/)**

After installation, verify that the GPU is correctly recognized:

```bash theme={"system"}
nvidia-smi
```

### 1.4 Install and Run Dstack

The base deployment of Dstack follows the official documentation:

* Dstack repo:\
  **[https://github.com/Dstack-TEE/dstack](https://github.com/Dstack-TEE/dstack)**

> By default, the Dstack GitHub documentation focuses on **Confidential CPU** only.\
> To run **Phala GPU Mining**, you must additionally enable and configure the **GPU Stack**.

#### 1.4.1 Update `vmm.toml` for GPU Stack

Locate your `vmm.toml` configuration file and update (or add) the following fields.

**Global paths & KMS configuration:**

```toml theme={"system"}
image_path = "<your own image path>"
run_path = "<your own vm path>"
kms_url = "<your own kms installation path>"

[cvm]
kms_urls = ["<your own kms installation path>"]
gateway_urls = ["<your own gateway url>"]
cid_start = 672000
cid_pool_size = 1000
max_disk_size = 2000
qemu_single_pass_add_pages = false
qemu_pic = false
qemu_pci_hole64_size = 1125899906842624
qemu_hotplug_off = true
```

**GPU configuration:**

```toml theme={"system"}
[cvm.gpu]
enabled = true
# To expose all available GPU devices to the dashboard and VMs:
include = []
# Alternatively, you can explicitly list GPU PCI IDs, e.g.:
# include = ["0000:17:00.0"]
```

**Supervisor configuration:**

```toml theme={"system"}
[supervisor]
detached = true
auto_start = false
```

**Notes:**

* `cvm.gpu.include`
  * `[]` = use **all** available GPUs
  * Or specify specific GPU device IDs as needed.
* `kms_url` / `kms_urls`
  * Replace with your actual KMS installation address or domain.
* `gateway_urls`
  * Replace with your actual Dstack gateway URL(s).
* `qemu_path`
  * In some environments, `qemu` is **not** located at `/opt/qemu-xxx`.\
    Check the actual QEMU location and ensure your Dstack configuration points to the correct path.
* `image_path` / `run_path`
  * Use paths where your VM images and runtime files will be stored.

#### 1.4.2 Start Dstack Services

Once configuration is complete, start the core Dstack components in the following order:

1. **Start KMS**

```bash theme={"system"}
./dstack-kms -c kms.toml
```

2. **Start Gateway**

```bash theme={"system"}
sudo ./dstack-gateway -c gateway.toml
```

3. **Start VMM**

```bash theme={"system"}
./dstack-vmm -c vmm.toml
```

#### 1.4.3 Verify Dstack Setup

Dstack is considered correctly deployed if:

* All three processes (**KMS**, **Gateway**, **VMM**) run without critical errors.
* You can access the Dstack web UI via its URL (dashboard loads successfully).
* GPU devices are visible/usable through Dstack after enabling `[cvm.gpu]`.

If these are met, your **Confidential CPU + GPU Stack environment is ready**.

## 2. Run the Phala GPU Mining Service

After Dstack and the GPU stack are running correctly, you can start deploying the Phala GPU Mining service.

The mining backend is a Docker-based service that:

* Connects to your local Dstack instance
* Registers the node with **Phala Cloud**
* Exposes your GPU resources for Phala GPU mining

> It is recommended to run the backend as **root** (or with equivalent privileges).

### 2.1 Use the Official Mining Backend Docker Setup

Phala provides an official Docker Compose configuration:

* Docker Compose file:\
  **[https://github.com/Phala-Network/dstack-mining-backend/blob/main/docker-compose.yaml](https://github.com/Phala-Network/dstack-mining-backend/blob/main/docker-compose.yaml)**

* Full backend repo:\
  **[https://github.com/Phala-Network/dstack-mining-backend](https://github.com/Phala-Network/dstack-mining-backend)**

### 2.2 Clone the Repository and Configure

```bash theme={"system"}
git clone https://github.com/Phala-Network/dstack-mining-backend.git
cd dstack-mining-backend
```

Edit `docker-compose.yaml` and update necessary fields:

* Dstack gateway and KMS endpoints
* Phala Cloud API credentials
* Optional: volume mounts, logging, etc.

### 2.3 Start the Mining Backend

```bash theme={"system"}
docker compose up -d
```

This will:

* Launch the backend container
* Connect it to your local Dstack instance
* Register your GPU node with Phala Cloud

### 2.4 Confirm Mining Is Active

Check Docker containers:

```bash theme={"system"}
docker ps
```

View logs:

```bash theme={"system"}
docker compose logs -f
```

Your setup is successful when:

* Backend connects to Dstack gateway & KMS
* GPUs (H100 / H200 / B200) are detected
* Your node registers on Phala Cloud
* Mining jobs begin to run

## 3. Troubleshooting

Common issues:

* **Intel TDX module errors**\
  → Re-check BIOS and Intel’s TDX guide.

* **GPU not detected**\
  → Ensure `cvm.gpu.enabled = true` and correct NVIDIA drivers.

* **QEMU path issue**\
  → Verify QEMU location on your OS.

* **Backend cannot reach Dstack**\
  → Confirm gateway/KMS URLs and firewall settings.

* **Docker device permission issues**\
  → Use root or ensure access to `/dev/nvidia*`.