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.
This guide explains how to unleash vLLM’s full potential by optimizing batch size, leveraging 4‑bit quantization, tuning concurrency parameters, planning capacity with token‑per‑second metrics, and implementing robust monitoring to balance latency, cost, and scalability in production deployments.
This guide walks you through deploying the DeepSeek‑R1‑Distill model on Volcengine CPU ECS instances, covering use‑case scenarios, recommended server types, Docker setup, environment configuration, and verification steps to achieve cost‑effective, high‑compatibility AI inference.
The author shares a candid half‑year reflection on entering computer‑science research, outlining practical steps for discovering research ideas, navigating papers, focusing on LLM serving systems, and emphasizing collaboration to help newcomers succeed in academia.
The article introduces NanoFlow, a novel service framework that leverages intra‑device parallelism, operation‑based pipelining, and async scheduling to significantly improve large language model serving throughput, achieving up to 1.91× higher performance while integrating with Alibaba Cloud PAI.
Alibaba Cloud's PAI team unveiled Llumnix, a dynamic scheduling framework for large language model serving that dramatically reduces tail latency, speeds high‑priority requests, and cuts costs, earning acceptance at OSDI 2024 and now open‑sourced on GitHub.
