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

vLLM is a high-throughput LLM inference system that uses advanced techniques like paged attention, continuous batching, and chunked prefill to optimize performance. This detailed technical breakdown explains vLLM's core components—from the LLM engine and scheduling mechanisms to multi-GPU scaling and distributed serving—providing a comprehensive guide for understanding how modern LLM inference systems achieve high throughput and low latency. The analysis covers both fundamental concepts and advanced features, building from single-GPU offline inference to multi-node online serving architectures.