ngrok explains vLLM high-throughput inference optimizations

The author explains how vLLM optimizes large language model inference by tackling compute and memory bottlenecks through continuous batching, PagedAttention, prefix caching, and CUDA graphs. These techniques maximize GPU utilization, reduce memory waste, and allow high-throughput serving for open-weight models at scale. Open article →