This article analyzes why vLLM's PagedAttention can cause GPU memory fragmentation and out‑of‑memory errors in production, presents four typical OOM scenarios, and provides concrete diagnostics, configuration tweaks, code examples, and monitoring strategies to eliminate the problem.
The article explains how Prompt Caching leverages vLLM's PagedAttention and block‑level hashing to reuse KV cache across identical prefixes, dramatically cutting LLM inference latency and cost, and provides concrete engineering tips for maximizing cache hit rates.
The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.
This guide details how to replace native Transformers inference with the high‑performance vLLM engine, leveraging PagedAttention, continuous batching, tensor parallelism, and OpenAI‑compatible APIs to achieve 3‑4× higher throughput, lower latency, and scalable multi‑GPU deployments for production‑grade large language models.
The article evaluates Augment Code, an AI‑powered coding assistant with 200K token context, persistent memory, multimodal input, and top SWE‑bench scores, walks through its installation, explores its use on vllm and PagedAttention, demonstrates adding a new model and auto‑generating a WeChat mini‑program, and compares its capabilities and speed to Cursor.
This article explains how vLLM’s PagedAttention, inspired by operating‑system virtual‑memory paging, dynamically allocates KV‑cache memory to dramatically reduce GPU memory fragmentation, improve throughput, and handle scheduling, preemption, and distributed inference for large language models.
