Deep Dive into vLLM: How PagedAttention & Continuous Batching Revolutionized LLM Inference

Serving Large Language Models (LLMs) in production is notoriously difficult and expensive. While researchers focus heavily on making models smarter or training them faster, the operational bottleneck for deploying these models at scale almost always comes down to inference throughput and memory management. Enter vLLM, an open-source library that took the AI infrastructure world by storm. By tackling the root causes of GPU memory waste, vLLM achieves 2x to 4x higher throughput compared to naive HuggingFace Transformers implementations. Let's dive deep into the architectural breakthroughs that make vLLM the gold standard for high-throughput LLM serving: PagedAttention and Continuous Batching. The Bottleneck: The Dreaded KV Cache To understand why vLLM is necessary, we first have to understand the KV Cache. During autoregressive text generation, an LLM predicts the next token one at a time. To avoid recomputing the attention matrix for all previous tokens in the sequence during every sing