Module 1: Getting Started with vLLM Playground

ACME Corporation is kicking off their AI customer support initiative, and you've been tasked with evaluating vLLM as the foundation for their inference infrastructure. vLLM is the industry-leading high-throughput and memory-efficient inference engine, and vLLM Playground provides a modern web interface for managing and interacting with vLLM servers.

In this module, you'll verify your environment, deploy your first vLLM server using Podman, and experience the chat interface firsthand.

Learning Objectives

By the end of this module, you'll be able to:

• ✅ Verify vLLM Playground installation and environment readiness
• ✅ Pull and deploy a vLLM server using Podman containers
• ✅ Navigate the vLLM Playground web interface
• ✅ Interact with an LLM through the chat UI with streaming responses

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Exercise 1: Verify Your Environment

ACME needs to ensure the AI infrastructure is properly configured before proceeding. Let's start by verifying that vLLM Playground and all dependencies are correctly installed.

Prerequisites

• vLLM Playground installed (pip install vllm-playground)
• Podman or Docker installed
• GPU with CUDA support (recommended) or CPU

Steps

1. Verify vLLM Playground is installed:

   vllm-playground --help
   

   Expected output:

   usage: vllm-playground [-h] [--port PORT] [--host HOST] {pull,stop,status} ...
   
   vLLM Playground - A modern web interface for vLLM
   
   positional arguments:
     {pull,stop,status}
       pull              Pre-download container images
       stop              Stop running vLLM Playground instance
       status            Check status of vLLM Playground
   
   optional arguments:
     -h, --help          show this help message and exit
     --port PORT         Port to run on (default: 7860)
     --host HOST         Host to bind to (default: 0.0.0.0)
   

2. Check Podman is available:

   podman version
   

   You should see Podman version 4.0 or later. (Or use docker version if using Docker)

3. Verify GPU availability (if using GPU mode):

   nvidia-smi
   

   You should see your NVIDIA GPU listed with driver and CUDA information.

4. Check the current status of vLLM Playground:

   vllm-playground status
   

   This shows whether any vLLM instance is currently running.

✅ Verify

Confirm all checks passed:

• vllm-playground --help displays usage information
• podman version shows version 4.0+
• nvidia-smi shows GPU information (if using GPU)
• Environment is ready for vLLM deployment

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Exercise 2: Deploy Your First vLLM Server

With the environment validated, ACME is ready to deploy their first LLM. You'll pull the necessary container images and start a vLLM server with a capable model for customer support scenarios.

Prerequisites

• Exercise 1 completed successfully
• GPU detected and available (or CPU mode)

Steps

1. Pre-download the GPU container image (this may take a few minutes):

   vllm-playground pull
   

   This downloads the vLLM GPU container image (~10GB). You'll see progress indicators as the layers download.

   CPU Mode

   If you're in a CPU-only environment, use vllm-playground pull --cpu instead.

2. Start vLLM Playground:

   vllm-playground
   

3. Access the Web UI by opening your browser to:

   http://localhost:7860
   

   

4. In the vLLM Playground web interface, configure your first model:

   • Navigate to Server Configuration section
   • In the Model section, you have several options:
     • Dropdown list: Select from pre-configured models like TinyLlama 1.1B Chat (Fast, No token) — ideal for quick testing
     • Custom model name: Enter any HuggingFace model ID in the text field
     • Browse Community Recipes: Explore community-contributed model configurations
     • HuggingFace Token: Required only for gated models (Llama 3.1, Llama 3.2, etc.)

   For this exercise, select TinyLlama 1.1B Chat from the dropdown — it's fast and doesn't require authentication.

   

5. Configure the Run Mode:

   • Subprocess: Runs vLLM directly as a Python subprocess (requires vLLM preinstalled)
   • Container: Runs vLLM in an isolated Podman container (recommended)

   Select Container mode for better isolation.

   

6. Configure the Compute Mode:

   • CPU: For systems without GPU (slower inference)
   • GPU: For NVIDIA GPU acceleration (recommended)

   Select GPU mode if available.

   GPU Mode Requires Root Privilege

   Running containers with GPU access requires root privilege. The vLLM container will be started with sudo when GPU mode is selected.

   

7. Review the Command Preview at the bottom — it shows the exact vLLM command that will run.

   

8. Click "Start Server"

9. Wait for the model to load. You can monitor progress in the interface or check container logs:

   sudo podman logs -f vllm-service
   

   Sudo for GPU Access

   You may need sudo podman logs -f vllm-service as the container uses GPU resources.

✅ Verify

Look for these indicators that the server is ready:

• The Server Logs panel shows Application startup complete.
• A green "Server is ready to chat!" toast notification appears
• The Send button in the chat interface turns green

You can also verify the container is running:

sudo podman ps

• Container vllm-service is running
• Status shows "healthy" or "Up"

Troubleshooting

Container fails to start with 'out of memory'

Solution:

1. Check GPU memory: nvidia-smi
2. Try a smaller model or adjust GPU memory utilization in settings
3. Use --gpu-memory-utilization 0.8 for 80% GPU memory usage

'Model not found' error

Solution:

1. Verify model ID is correct (check HuggingFace)
2. For gated models (Llama, Gemma), ensure you have access and set HF_TOKEN
3. Try a public model like TinyLlama/TinyLlama-1.1B-Chat-v1.0

Port 7860 already in use

Solution:

vllm-playground stop
vllm-playground --port 8080

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Exercise 3: Interact with Your LLM

Now that the vLLM server is running, you'll explore the chat interface and have your first conversation with the model. This simulates how ACME's customer support system would interact with users.

Steps

1. In the vLLM Playground web UI, navigate to the Chat section.

   You'll see a ChatGPT-style interface with:

   • Message input area at the bottom
   • Conversation history in the main panel
   • Model settings in the sidebar

   

2. Send your first message to test the model:

   Hello! Can you introduce yourself and explain what you can help me with?
   

   Observe the streaming response — text appears word by word as the model generates it.

3. Test a customer support scenario for ACME:

   I'm a customer support agent. A customer is asking about their order status. 
   How should I professionally respond if their order is delayed by 2 days?
   

   Notice how the model provides helpful guidance for the support scenario.

4. Observe the Response Metrics panel on the right side of the chat interface:

   Metric	Description
   Prompt Tokens	Number of tokens in your input
   Completion Tokens	Number of tokens generated by the model
   Total Tokens	Combined input and output tokens
   Time Taken	Total response generation time
   Tokens/sec	Generation throughput rate

   These metrics highlight the high throughput that vLLM delivers.

   

5. Explore the chat settings:

   • Temperature: Controls randomness (lower = more deterministic)
   • Max Tokens: Limits response length
   • System Prompt: Sets the model's persona/behavior

   Try setting a system prompt:

   You are a helpful customer support assistant for ACME Corporation. 
   Be professional, empathetic, and solution-oriented.
   

   Templates

   Click the Templates dropdown to access preset system prompt templates for common use cases.

   

6. Send another message with the system prompt active:

   A customer is frustrated because their product arrived damaged. What should I say?
   

   Notice how the response now aligns with the customer support persona.

✅ Verify

Confirm you can interact with the model:

• Messages send successfully
• Responses stream in real-time (word by word)
• System prompt affects model behavior
• Different temperature settings change response style

Troubleshooting

Chat messages don't get responses

Solution:

1. Verify server is running: Check the status indicator in the UI
2. Check container logs: podman logs vllm-service
3. Stop the Server and Start again

Responses are very slow

Solution:

1. Check GPU utilization: nvidia-smi
2. Verify model loaded to GPU (not CPU)
3. Consider a smaller model for faster responses

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Learning Outcomes

By completing this module, you should now understand:

• ✅ How vLLM Playground provides a web interface for managing vLLM servers
• ✅ The container-based architecture that enables easy deployment via Podman
• ✅ How streaming responses work for real-time user interaction
• ✅ The role of system prompts and temperature in controlling model behavior
• ✅ Basic troubleshooting for vLLM server issues

Module Summary

You've successfully completed the Getting Started module for vLLM Playground.

What you accomplished:

• Verified vLLM Playground installation and GPU availability
• Pulled container images and deployed a vLLM server
• Explored the modern chat interface with streaming responses
• Tested customer support scenarios relevant to ACME's use case

Key takeaways:

• vLLM Playground simplifies LLM deployment through container management
• The chat interface provides a familiar experience similar to ChatGPT
• System prompts are essential for tailoring model behavior to specific use cases
• Streaming responses enable real-time interaction for better user experience

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Next: Module 2: Structured Outputs — Learn how to constrain model responses to specific formats using JSON Schema, Regex, and Grammar.

References

• vLLM Playground GitHub Repository — Installation and configuration
• vLLM Project — High-performance inference engine
• TinyLlama Model — Lightweight model for testing