vLLM: Complete Guide 2026
PythonAI Inference Engine35k+ stars
Overview
A high-throughput, memory-efficient inference and serving engine for large language models. vLLM uses PagedAttention to optimize memory usage and enable continuous batching for production-grade LLM serving.
Key Features
✓PagedAttention for efficient memory management
✓Continuous batching for high throughput
✓OpenAI-compatible API server
✓Tensor parallelism for multi-GPU serving
✓Support for LoRA adapters
✓Quantization support (AWQ, GPTQ, FP8)
Use Cases
- → Production LLM API serving
- → High-throughput inference for multiple users
- → Cost-optimized GPU inference deployment
- → Self-hosted OpenAI-compatible endpoints
Pros & Cons
Pros
- +State-of-the-art inference throughput
- +Significantly lower memory usage than naive implementations
- +Production-ready with OpenAI-compatible API
- +Wide model support and active development
Cons
- -Requires GPU hardware for optimal performance
- -More complex setup than llama.cpp for simple use cases
- -Focused on server deployment rather than local use
Frequently Asked Questions
What is vLLM?▾
A high-throughput, memory-efficient inference and serving engine for large language models. vLLM uses PagedAttention to optimize memory usage and enable continuous batching for production-grade LLM serving.
What language is vLLM built in?▾
vLLM is primarily built in Python.
Is vLLM good for production?▾
vLLM has 35k+ GitHub stars. State-of-the-art inference throughput for production llm api serving.