Inside vLLM: How PagedAttention and Continuous Batching Scale LLM Inference

Aleksa Gordić has published a comprehensive technical analysis of vLLM, an open-source inference engine designed for high-throughput large language model (LLM) deployment. The deep-dive explores how the system optimizes the critical intersection of memory management and compute to reduce latency in production environments.

According to the analysis, vLLM achieves its performance gains through several core architectural innovations. Central to its design is PagedAttention, a mechanism that manages KV (Key-Value) cache memory by reducing fragmentation. By treating GPU memory similarly to virtual memory in traditional operating systems, vLLM allows for non-contiguous memory allocation. Additionally, the system employs continuous batching, which increases throughput by processing incoming requests immediately rather than waiting for a full batch to accumulate before execution. Gordić's analysis also highlights advanced features such as prefix caching and chunked prefill.

The Memory Bottleneck

As LLMs scale in size and deployment volume, the primary bottleneck for inference is typically the KV cache. This cache consumes significant GPU memory, often leading to inefficiencies where memory is reserved but underutilized. vLLM, which was originally developed in the Sky Computing Lab at UC Berkeley, addresses this by decoupling the physical storage of the KV cache from the logical sequence of tokens. This approach prevents the waste associated with static memory allocation and allows the engine to handle larger batches of requests simultaneously.

Economic Impact of Throughput

Optimizing inference throughput is a critical factor in the economic viability of AI services. Because GPU resources are expensive and limited, the ability to serve more users per chip directly reduces the operational cost and latency of deploying models in production. By maximizing the efficiency of memory allocation and request scheduling, vLLM enables providers to scale their infrastructure more sustainably while maintaining the performance levels required for real-time applications.

Future Technical Directions

This analysis marks the first in a series of five parts, beginning with the LLM engine and progressing toward more advanced system features. Future discussions are expected to further detail the implementation of the scheduling layers and the specific mechanics of the engine's memory management. As the industry moves toward larger context windows and more complex prompt structures, the role of non-contiguous memory management and efficient prefilling will remain a primary focus for high-performance inference systems.