Introduction to vLLM and PagedAttention

vLLM is an open-source LLM inference and serving engine that leverages a novel memory allocation algorithm called PagedAttention to optimize memory usage and significantly boost throughput, achieving up to 24 times higher throughput than HuggingFace Transformers and 3.5 times higher than HuggingFace Text Generation Inference. PagedAttention draws inspiration from memory paging in operating systems to manage the KV-Cache more efficiently, reducing memory waste to under 4%, which allows for larger request batch sizes and reduces the need for GPUs, thus lowering inference costs. Widely adopted by thousands of companies, including LMSYS, vLLM supports various decoding strategies such as parallel sampling and beam search, enhancing flexibility and efficiency. It also incorporates several performance optimizations like quantization and automatic prefix caching, supporting a wide array of models and architectures compatible with both NVIDIA and AMD GPUs. With a thriving developer ecosystem, vLLM is easy to deploy, particularly on platforms like Runpod Serverless, offering custom API endpoints for LLM inference with minimal setup, making it highly attractive for startups scaling their applications.