The Rise of vLLM: Building an Open Source LLM Inference Engine

vLLM has quickly become one of the most widely adopted open source LLM inference engines - reaching 66k+ GitHub stars and millions of downloads in just over two years. 🔗 For a deeper look at the evolution of vLLM and its roadmap, check out Simon’s “State of vLLM 2025” talk from Ray Summit: https://youtu.be/tN_-nktp1Hk?si=glgqD8EWlVOC69L4 In this conversation, we sit down with Simon Mo, co-lead of the vLLM project, to go deep on how vLLM is built, the architectural decisions behind its inference performance, how it integrates with Ray for distributed workloads, and where the project is headed next. Simon walks through the core problems vLLM was designed to solve, including efficient KV-cache management, high-throughput inference, and scaling across GPUs and nodes. We also discuss how vLLM fits into modern RLHF and post-training workflows, the role of open source governance, and what’s coming next across models, hardware, and the broader AI compute stack. ⏱️ Chapters & Timestamps 0:00 Overview of vLLM 01:01 Early Architectural Decisions 02:11 Why vLLM Adoption Is Accelerating 04:28 How vLLM and Ray Work Together 07:00 The State of vLLM Today 10:09 Simon Mo’s Open Source Journey 12:28 Advice for AI Builders & Contributors