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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

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1. Enable TDX & Install Dstack

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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.

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1.2 Configure TDX System Environment

Follow Intel’s / Dstack’s official instructions to configure the TDX system environment: If you encounter errors related to Intel modules (for example, TDX module installation failures), refer to Intel’s enabling guide: Make sure all required kernel modules and firmware settings for TDX are correctly configured before continuing.

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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: After installation, verify that the GPU is correctly recognized: 
nvidia-smi

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1.4 Install and Run Dstack

The base deployment of Dstack follows the official documentation:
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.

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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: 
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: 
[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: 
[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.

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1.4.2 Start Dstack Services

Once configuration is complete, start the core Dstack components in the following order:
  1. Start KMS
./dstack-kms -c kms.toml
  1. Start Gateway
sudo ./dstack-gateway -c gateway.toml
  1. Start VMM
./dstack-vmm -c vmm.toml

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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.

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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).

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2.1 Use the Official Mining Backend Docker Setup

Phala provides an official Docker Compose configuration:

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2.2 Clone the Repository and Configure

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.

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2.3 Start the Mining Backend

docker compose up -d
This will:
  • Launch the backend container
  • Connect it to your local Dstack instance
  • Register your GPU node with Phala Cloud

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2.4 Confirm Mining Is Active

Check Docker containers: 
docker ps
View logs: 
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

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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*.