Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)

vLLM's core innovation is PagedAttention, which manages key-value cache memory in fixed-size blocks to reduce fragmentation and allow sharing. The system also employs continuous batching and speculative decoding to maximize GPU utilization. With these mechanisms, vLLM can achieve significantly higher throughput than traditional serving frameworks, making it a standard choice for production LLM deployments. The article breaks down the system's architecture, from request scheduling to tensor parallelism, and highlights trade-offs in latency versus throughput. Understanding these internals helps developers tune vLLM for their own workloads and anticipate future improvements in inference serving.