Serving an LLM to many concurrent users efficiently is a genuinely different engineering problem than running one locally for personal use — vLLM is a widely adopted open-source inference server specifically built to solve it well.
The Core Problem: Memory-Efficient Batching
Naively running many concurrent LLM requests wastes an enormous amount of GPU memory, since each request’s “KV cache” (the model’s running memory of the conversation so far) needs to be allocated as a large, contiguous memory block sized for the worst-case possible sequence length, even for requests that turn out to be short — genuinely inefficient at any real scale.
pip install vllm # Serves an OpenAI-API-compatible endpoint, handling many concurrent requests efficiently python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.1-8B-Instruct
vLLM’s Key Innovation: PagedAttention
Inspired directly by virtual memory paging in operating systems, PagedAttention allocates the KV cache in smaller, non-contiguous memory blocks rather than one large fixed block per request — dramatically reducing genuine memory waste and letting a server handle significantly more concurrent requests on the exact same GPU hardware compared to naive, unoptimized serving approaches.
Why This Matters for Anyone Self-Hosting
The difference between naive serving and a genuinely optimized inference server like vLLM can be several times the concurrent request throughput on identical hardware — a critical, real difference for anyone self-hosting an LLM at any meaningful scale, rather than just for a single personal user.