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Dedicated Models let you host large language models on dedicated GPU hardware inside trusted execution environments (TEEs). You get the same pre-deployed models as the Confidential AI API, but with dedicated resources and hourly pricing instead of per-request billing. Choose this option when you need predictable performance or cost efficiency for high-volume inference workloads. For custom workloads beyond pre-deployed models, see GPU TEE. This option uses the same OpenAI-compatible API as API Access, including all advanced features like tool calling and vision. The only difference is dedicated GPU resources with hourly billing instead of shared infrastructure.

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Prerequisites

  • Phala Cloud account with sufficient credits
  • Basic familiarity with LLM deployment

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Step 1: Deploy your model

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Access the deployment page

Sign in to your Phala Cloud account and select Confidential AI Models from the top navigation bar.
Confidential AI Models

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Choose a model

Locate the model you want to deploy (e.g., qwen-2.5-7b-instruct) and click Deploy to open the deployment wizard. Review the model description to understand its capabilities. For example, Qwen 2.5 models are instruction-tuned language models optimized for chat and task completion.
Don’t see your preferred model? We can add custom models—reach out through Support.

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Configure deployment

Fill out the deployment form with these details: Service Name: Give your deployment a memorable name (e.g., qwen-2-5-7b-instruct). Use the auto-generated name or create your own. Node & Image: Select your preferred deployment location and image version. Available options depend on current capacity (e.g., gpu1 us-west-1 with dstack-nvidia-0.5.0). Resource Plan: Review the hardware allocation for your chosen model. For example, qwen-2.5-7b-instruct typically includes:
  • H200 GPU with 141 GB VRAM
  • 24 vCPUs
  • 256 GB RAM
  • 40 GB disk
  • $3.20 per hour pricing
Scheduled Destroy: Set an auto-destruction date to avoid ongoing charges, or leave empty for manual control.

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Launch the deployment

Review your configuration and click Deploy to launch. Provisioning takes a few minutes.
Deploy LLM Model
Monitor the status in the Active Workloads section on the Confidential AI Models page. Your model will progress through these states:
  • Preparing: Allocating resources
  • Starting: Launching the model
  • Running: Ready to use
Billing starts when the model enters Running status. Remember to stop or schedule destruction when you no longer need the instance.

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Step 2: Access Open WebUI

Once your model reaches Running status, you can access it through Open WebUI, a browser-based chat interface.

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Open the interface

When the workload shows RUNNING status, a Connect UI button appears under the model card. The button initially reads “Initializing…” while the interface loads. Click it when active to open a new tab.
Model Details Page

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Create admin account

On your first visit, create an admin account with your name, email, and password. Click Create Admin Account to continue.
Critical: Choose a strong password and store it securely. Because your model runs in a confidential environment, even we (the cloud provider) cannot access or reset your password. If you forget it, you’ll lose access to all chat history permanently.
After account creation, click Okay, Let’s Go! to access the chat interface.

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Step 3: Interact with your model

Open WebUI provides a standard chat interface. Type your questions in the prompt box, manage conversation history in the left sidebar, and use the tools menu (+) for advanced features like file uploads. The interface is intuitive and works like other chat applications you may be familiar with.
Open WebUI Chat Interface

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Step 4: Access via API

Beyond the WebUI, you can access your dedicated model programmatically through a REST API. Navigate to the Overview page for your running instance. In the Inference API section, click Quickstart to see example commands and integration code. To make API calls, you’ll need an API key from Open WebUI. Open the WebUI, navigate to Settings → Account → API keys, and create a new key. Use this key as a Bearer token in your requests.
Protect your API key like a password. Anyone with the key can access your model.
Here’s a quick example for chat completions: 
curl -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-your-api-key" \
  -d '{
    "model": "qwen/qwen-2.5-7b-instruct",
    "messages": [
      {"role": "user", "content": "Hello! Can you tell me a fun fact about space?"}
    ]
  }' \
  https://<base_url>/v1/chat/completions
For TEE verification, your instance exposes attestation endpoints: 
# Get attestation report
curl -H "Authorization: Bearer sk-your-api-key" \
  "https://<base_url>/v1/attestation/report?model=MODEL_NAME"

# Get signature for a chat completion
curl -H "Authorization: Bearer sk-your-api-key" \
  "https://<base_url>/v1/signature/{chat_completion_id}?model=MODEL_NAME&signing_algo=ecdsa"
See Verify Attestation and Verify Signature for how to use these endpoints to confirm your requests were processed inside a TEE.

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Managing your instance

Return to the Confidential AI Models page anytime to monitor your model: Overview button: Opens a dashboard with resource metrics, logs, and attestation data. Connect UI button: Always launches the Open WebUI for your model.
Billing continues while the instance is running. Stop or schedule destruction when you no longer need the model to avoid ongoing charges.

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Troubleshooting

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Connect UI button stuck on “Initializing…”

Wait 2-3 minutes after the model reaches Running status. The interface needs time to fully start. Refresh the Confidential AI Models page if the button doesn’t activate.

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Cannot access Open WebUI

Verify your model status is Running in the Active Workloads section. Check that you’re using the correct URL (should end in dstack-prod5.phala.network).

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Forgot admin password

We can’t reset your admin password because the model is running in a confidential environment that even the cloud provider doesn’t have access to. You will lose all the chat history if you forget your password.

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Next steps

You’ve deployed a dedicated model and accessed it through Open WebUI! Now you can:

Verify attestation

Confirm your model runs in genuine TEE hardware

GPU TEE

Deploy custom models with full infrastructure control