Introduction Running a 7B-8B class model on a single RTX 3090, you might settle for ~25-30 tokens/s, enough for personal use but far from optimal. For a production-grade API service, we aspire for maximal requests per second—this is our performance target. Through a series of optimizations—leveraging vLLM's specialized architecture, model quantization, and deep parameter tuning—we can transform a single 3090 into a high-throughput API node capable of handling over 50 concurrent sequences. This guide outlines the systematic approach I've used to move from a single-user setup to an efficient, concurrent API deployment. The Core Technology: Why vLLM Excels vLLM fundamentally changes LLM serving with two key innovations: PagedAttention: Transforms KV cache management by splitting it into fixed-size pages, akin to an OS virtual memory manager. This eliminates fragmentation and increases memory utilization, enabling far larger batch sizes on limited VRAM compared to traditional frameworks.…