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GPU TEE gives you dedicated H100, H200, or B200 GPUs running inside trusted execution environments for custom AI workloads. You get full control over your environment with Docker container support, and you can verify hardware authenticity using NVIDIA’s local verification tools. This option works when you need to train, fine-tune, or run inference on proprietary datasets with custom code. For standard LLM inference, the Confidential AI API or Model Template are simpler alternatives. Each instance includes NVIDIA Driver 570.133.20 and CUDA 12.8. You can scale from 1 to 8 GPUs per instance.

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Prerequisites

  • Phala Cloud account with sufficient credits
  • Basic understanding of Jupyter notebooks
  • Familiarity with command-line tools

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Step 1: Deploy GPU TEE instance

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Launch the deployment wizard

Sign in to cloud.phala.network, click GPU TEE in the navigation bar, then click Start Building to open the Launch GPU Instance wizard.
Check your credit balance in the upper right corner. GPU instances incur hourly charges, so confirm your balance before launching.

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Choose GPU hardware

Select your GPU type based on your compute needs.
GPU TEE hardware selection interface showing H100, H200, and B200 GPU options with specifications

GPU Device Selection

Available options include:
GPU typeRegionvCPU coresVRAMRAMStoragePrice*
H200US24141 GB256 GB200 GB$2.56/GPU/hour
H200India15141 GB384 GB200 GB$2.30/GPU/hour
B200US12180 GB192 GB200 GB$3.80/GPU/hour
*Pricing may vary. Check the dashboard for current rates. Click your preferred GPU card to highlight it in green.

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Configure GPU count

Choose the number of GPUs for your instance. You can scale from 1 to 8 GPUs per instance. The UI updates resource totals dynamically:
GPU countExample: B200Total vCPUTotal VRAMTotal RAMTotal storage
1 GPUSingle12 cores180 GB192 GB200 GB
8 GPUsMulti96 cores1 TB1 TB1 TB

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Configure deployment

Give your deployment a name or use the auto-generated name like gpu-tee-1p1qp. For the template, choose Jupyter Notebook (PyTorch) to get a GPU-accelerated JupyterLab environment with PyTorch and CUDA pre-installed. This template works well for running verification scripts and custom experiments. You can also choose vLLM for an inference server or Custom Configuration to provide your own Docker Compose file. For this tutorial, we’ll use Jupyter Notebook because it gives us terminal access to run verification commands.
You can always deploy different containers later. Your initial template choice isn’t permanent.

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Select pricing plan

Choose a commitment period:
PlanRateNotes
6-month commitment~$2.88/GPU/hourIncludes storage, saves ~18% vs on-demand
1-month commitment~$3.20/GPU/hourIncludes storage, short-term commitment
On-Demand~$3.50/GPU/hour + storagePay-as-you-go, no commitment
Review the Pricing Summary showing estimated costs per hour, day, and month.

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Launch instance

Before launching, review the Instance Summary to confirm your GPU model and count, VRAM, RAM, and storage allocations, plus your total estimated costs.
GPU TEE order summary showing selected hardware configuration, pricing breakdown, and submit order button

Order Review

Click Launch Instance when you’re ready to proceed.
Launching creates hourly charges. Confirm your configuration and budget before proceeding. Provisioning takes approximately 1 day.

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Step 2: Access your GPU TEE instance

After provisioning completes, your instance appears under the GPU TEE tab with connection details including the JupyterLab URL. Navigate to the GPU TEE tab in your dashboard and find your instance in the GPU Instances list. Click View Details to see the JupyterLab URL, then open that URL in your browser to access your instance.
Monitor provisioning status in the GPU Instances list. Instances progress from Preparing → Starting → Running.

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Step 3: Verify GPU TEE attestation

Open a terminal in JupyterLab (File → New → Terminal) to verify your instance runs on genuine TEE hardware.

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Check GPU and TEE status

First, confirm your GPU is detected and confidential compute mode is active. Run nvidia-smi to check GPU status: 
nvidia-smi
Look for your GPU model (H100/H200/B200), driver version 570.133.20, and CUDA version 12.8. Then check confidential compute status: 
nvidia-smi conf-compute -q
Expected output: 
# nvidia-smi conf-compute -q
==============NVSMI CONF-COMPUTE LOG==============

    CC State                   : ON
    Multi-GPU Mode             : None
    CPU CC Capabilities        : INTEL TDX
    GPU CC Capabilities        : CC Capable
    CC GPUs Ready State        : Ready
The key indicators are CC State: ON and CPU CC Capabilities: INTEL TDX, confirming your instance runs in TEE mode.

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Run attestation verification

Install NVIDIA’s attestation verification tools: 
pip install nv-local-gpu-verifier nv_attestation_sdk
Run the verifier to get cryptographic proof of hardware authenticity: 
python -m verifier.cc_admin
The verifier confirms your GPUs are genuine NVIDIA devices, checks confidential compute mode is enabled, verifies driver and firmware versions, and generates cryptographic evidence of your TEE status. Successful verification means your GPU hardware is authentic, confidential compute mode is active, and the driver version matches the expected TEE-enabled version.
If verification fails, do not use the instance for confidential workloads. Contact Phala Support with the error details.

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Step 4: Confirm GPU functionality

Verify GPU functionality with PyTorch. Open a new notebook in JupyterLab (File → New → Notebook) and run: 
import torch

print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU: {torch.cuda.get_device_name(0)}")
This confirms PyTorch can detect and access the GPU.

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Next steps

You’ve deployed and verified a GPU TEE instance! Now you can:

Programmatic verification

Learn how to fetch and verify attestations programmatically

Bind GPU and CPU attestations

Understand how GPU and CPU attestations create a complete trust chain

Expose services

Make your GPU TEE workloads accessible over HTTPS