How to Run vLLM on Runpod Serverless (Beginner-Friendly Guide)

vLLM is an open-source inference and serving engine that significantly enhances throughput for large language models (LLMs) by optimizing memory usage with a novel algorithm called PagedAttention. This technology minimizes memory waste, requiring fewer GPUs and achieving up to 24 times higher throughput than HuggingFace Transformers and 3.5 times higher than HuggingFace Text Generation Inference. PagedAttention, inspired by memory paging in operating systems, dynamically allocates memory for the Key-Value (KV) Cache, reducing internal and external fragmentation and allowing for larger batch sizes during model inference. This efficient memory management enables vLLM to process more requests simultaneously, reducing inference costs for companies, as illustrated by LMSYS, which halved its GPU usage while increasing requests served. vLLM supports various models, including classic transformer LLMs, mixture-of-expert LLMs, and multi-modal LLMs, and has gained significant popularity, with over 20,000 GitHub stars and support from major companies and universities. The engine is user-friendly, compatible with OpenAI, and can be deployed rapidly on platforms like Runpod Serverless, making it an attractive option for developers and companies looking to optimize their LLM applications.