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 delivers 24x higher throughput compared to traditional inference methods, reducing inference costs by 85% and enabling ACME to process 10,000 customer support queries per minute on standard GPU infrastructure. vLLM Playground provides a modern web interface for managing and interacting with vLLM servers, making enterprise-grade AI inference accessible without complex DevOps overhead.

In this module, you'll verify your environment, deploy your first vLLM server using Podman, and experience the chat interface firsthand. By the end, you'll understand how vLLM's PagedAttention algorithm and efficient memory management enable ACME to handle 5x more concurrent customer conversations on the same hardware budget.

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

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 on your lab environment.

You prepare to validate the environment to confirm ACME's infrastructure is ready for AI deployment.

Steps¶

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)

Check Podman is available:
```
podman version
```
You should see Podman version 4.0 or later.
Verify GPU availability:
```
nvidia-smi
```
You should see your NVIDIA GPU listed with driver and CUDA information.
Check the current status of vLLM Playground:
```
vllm-playground status
```
This shows whether any vLLM instance is currently running.

Check daemon status (if configured as a service):

sudo systemctl status vllm-playground

Expected output:

● vllm-playground.service - vLLM Playground Service
     Loaded: loaded (/etc/systemd/system/vllm-playground.service; enabled; preset: disabled)
     Active: active (running) since Sat 2026-01-10 03:00:18 UTC; 36min ago
 Invocation: 6185d5f065a34e3bbff96f54410c1943
   Main PID: 3975 (vllm-playground)
      Tasks: 4 (limit: 95955)
     Memory: 51.2M (peak: 68.5M)
        CPU: 11.483s
     CGroup: /system.slice/vllm-playground.service
             └─3975 /usr/bin/python3.12 /usr/local/bin/vllm-playground

The key indicators are:

Active: active (running) - Service is running
enabled - Service starts automatically on boot

✅ Verify¶

Confirm all checks passed:

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

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

Steps¶

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.

Note

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

Tip

In a pre-configured lab environment, the container image may already be pre-pulled. The command will complete quickly as the image is already available locally.
Access the Web UI:

Open your browser and navigate to: http://localhost:7860
Configure your first model:

In the vLLM Playground web interface:
- 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.
Select Run Mode:
- Subprocess: Runs vLLM directly as a Python subprocess (good for development, requires vLLM preinstalled via pip install vllm)
- Container: Runs vLLM in an isolated Podman container (recommended for production)
For this exercise, select Container mode - it provides better isolation and matches production deployments.
Select Compute Mode:
- CPU: For systems without GPU (slower inference)
- GPU: For NVIDIA GPU acceleration (recommended for performance)
Select GPU mode if your environment has GPU 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.
Review default settings:
- Port: API endpoint port (default: 8000)
- Tensor Parallel Size: Number of GPUs for model parallelism
- GPU Memory Utilization: Fraction of GPU memory to use (default: 0.9)
- Data Type: Model precision (auto, float16, bfloat16)
- Max Model Length: Maximum sequence length
Note

These parameters are powered by vLLM's extensive configuration options. As you adjust settings, the Command Preview dynamically generates the corresponding vLLM command.
Click Start Server
Wait for the model to load. You can monitor progress in the interface or check container logs:
```
sudo podman logs -f vllm-service
```
Tip

The sudo prefix is required because the vLLM container uses GPU resources, which requires elevated privileges.

✅ 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 via Podman:

sudo podman ps

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

Troubleshooting¶

Issue: Container fails to start with "out of memory"

Solution:

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

Issue: "Model not found" error

Solution:

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

Issue: Port 7860 already in use

Solution:

vllm-playground stop
vllm-playground --port 8080

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¶

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
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.

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.

Observe the Response Metrics panel on the right side of the chat interface. This built-in performance dashboard displays real-time metrics for each response:
- 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
- Avg Prompt Throughput: Average processing speed for prompts
These metrics highlight the high throughput that vLLM delivers.
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.
```
Tip

Click the Templates dropdown to access preset system prompt templates for common use cases like coding assistant, translator, or summarizer.

Tip

Notice the yellow indicator in the top-right corner of toolbar icons - this indicates the configuration has been changed from its default value.
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¶

Issue: Chat messages don't get responses

Solution:

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

Issue: Responses are very slow

Solution:

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

Issue: Model gives unexpected or poor responses

Solution:

Adjust temperature (try 0.7 for balanced responses)
Add a clear system prompt to guide behavior
Try rephrasing your question more specifically

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

Next steps:

Module 2 will explore Advanced Inferencing: Structured Outputs - learning how to constrain model responses to specific formats using JSON Schema, Regex, and Grammar for predictable, parseable outputs.

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