vLLM: The Engine Behind Modern LLM Inference

vLLM is an open-source library built for fast, efficient serving of large language models. Released in 2023 by UC Berkeley researchers, it quickly became the go-to tool for anyone deploying LLMs in production.

The Problem It Solves

When you run an LLM, it stores something called a KV cache — the memory of all previous tokens — during generation. Before vLLM, this memory was allocated upfront at the maximum possible length, even if most of it was never used. The result: massive waste, poor batching, and low throughput.

The Core Idea: PagedAttention

vLLM introduced PagedAttention, which manages KV cache memory the way an operating system manages RAM — in small, flexible blocks instead of one big reserved chunk.

This means:

• Memory is only used when actually needed
• Multiple requests can share memory for identical prompt prefixes
• Far more requests can run concurrently on the same GPU

The result: up to 24x more throughput compared to naive HuggingFace inference.

Getting Started

pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct

vLLM runs an OpenAI-compatible server, so any existing OpenAI SDK client works with it out of the box — just change the base_url.

Key Features

• Continuous batching — handles requests of different lengths efficiently
• Speculative decoding — speeds up generation using a smaller draft model
• Multi-LoRA serving — serve many fine-tuned adapters on one base model
• Quantization support — run models in GPTQ, AWQ, or FP8 to save memory
• Multi-GPU support — spread large models across devices with tensor parallelism

When to Use It

Use vLLM when you’re serving an open-weight model to multiple users and throughput matters. If you just need local single-user inference, lighter tools like llama.cpp may suit you better.

Bottom Line

vLLM made deploying open-weight LLMs practical at scale. If you’re building on models like Llama, Mistral, or Qwen, it’s the most battle-tested serving stack available.