vLLM Workshop¶
🚀 Powered by vLLM Playground — A modern web interface for vLLM
Welcome to the vLLM Workshop! This hands-on learning experience will guide you through deploying and using vLLM — the industry-leading high-performance LLM inference engine.
What You'll Learn¶
In this workshop, you will:
- ✅ Deploy vLLM servers via container management using Podman
- ✅ Use a modern chat UI with streaming responses to interact with LLMs
- ✅ Implement structured outputs using JSON Schema, Regex, and Grammar
- ✅ Configure and use tool calling with various Open Source LLMs (Qwen, Llama, Mistral, etc.)
- ✅ Set up MCP (Model Context Protocol) servers for agentic capabilities
- ✅ Run performance benchmarks with GuideLLM
Who This Is For¶
This workshop is designed for Developers, AI Engineers, Platform Engineers, and Architects who want to set up a complete AI inference environment with tool calling and MCP integration.
Experience level: All levels — Beginner, Intermediate, and Advanced
Prerequisites¶
Before starting this workshop, you should have:
- Basic knowledge of Linux command line
- Basic understanding of vLLM, a high-throughput and memory-efficient inference engine
- Familiarity with containers (Podman or Docker)
- Basic understanding of AI inferencing concepts
Environment Setup¶
Hardware Requirements¶
| Component | Minimum | Recommended |
|---|---|---|
| GPU | NVIDIA GPU with 8GB VRAM | NVIDIA GPU with 16GB+ VRAM |
| RAM | 16GB | 32GB+ |
| Storage | 50GB free | 100GB+ free |
CPU Mode Available
No GPU? You can still complete most exercises using CPU mode, though inference will be slower.
Software Requirements¶
- Python 3.10 or later
- Podman 4.0+ or Docker
- NVIDIA GPU drivers and CUDA (for GPU mode)
- Modern web browser (Chrome, Firefox, Safari, Edge)
Install vLLM Playground¶
# Install from PyPI
pip install vllm-playground
# Pre-download container image (~10GB for GPU)
vllm-playground pull
# Start the playground
vllm-playground
Open http://localhost:7860 and you're ready to begin!
Let's Get Started!¶
Click on Workshop Overview in the navigation to begin your learning journey with vLLM!
⭐ Like this workshop? Star vLLM Playground on GitHub!