Production LLM Serving with vLLM & Continuous Batching

Introduction

While local execution is great for development, production environments require high throughput, low latency, and efficient resource utilization. This lesson covers production LLM serving using vLLM.

The Challenge of LLM Serving

LLMs generate tokens sequentially, which makes batching difficult. Traditional static batching requires waiting for all requests in a batch to finish before starting a new one, leading to massive inefficiencies.

Continuous Batching

Continuous batching (or iteration-level scheduling) solves this by dynamically adding new requests and removing finished ones at the token level. This dramatically improves throughput and GPU utilization.

Enter vLLM

vLLM is a high-throughput and memory-efficient LLM serving engine. It introduces PagedAttention, which treats the KV cache like virtual memory in an operating system. This reduces memory fragmentation and allows for significantly larger batch sizes.

Deploying vLLM

We will explore how to configure and deploy a vLLM server, manage its memory allocation, and integrate it with API gateways for production traffic.